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Weak decisions create communication overload and execution drift downstream." } }, { "id": "lpm:layer:communication-architecture", "type": "layer", "slug": "communication-architecture", "title": "Communication Architecture", "canonicalUrl": "https://largepeoplemodel.com/framework/communication-architecture", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:use-case:product-operating-model" }, { "type": "applies_to", "targetId": "lpm:use-case:ma-integration" } ], "content": { "number": "03", "shortName": "Communication", "definition": "Designs how information, intent, decisions, and commitments move across teams without creating noise or confusion.", "primaryQuestion": "How does communication create shared understanding?", "whyItMatters": "More communication does not create more clarity unless the architecture is intentional.", "businessProblem": "Communication overload creates misalignment, rework, and hidden coordination cost.", "symptoms": [ "Too many meetings with unclear decisions.", "Important context is buried in chat.", "Teams communicate activity instead of commitment.", "Leaders cannot tell whether messages changed behavior." ], "diagnosticQuestions": [ "What needs to be communicated, to whom, and why?", "Which channels carry decisions versus discussion?", "Where does communication fail to create shared understanding?", "Which meetings exist because decision rights are unclear?" ], "artifacts": [ "Communication map", "Meeting architecture", "Decision communication protocol", "Channel purpose guide" ], "aiReadinessQuestion": "Can AI tools access clear, trusted communication patterns instead of fragmented noise?", "aiRiskWhenWeak": "AI summarizes noise and makes confusion appear organized.", "connection": "Communication moves decisions, context, risks, and commitments through the enterprise." } }, { "id": "lpm:layer:information-ecology", "type": "layer", "slug": "information-ecology", "title": "Information Ecology", "canonicalUrl": "https://largepeoplemodel.com/framework/information-ecology", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:layer:communication-architecture" }, { "type": "applies_to", "targetId": "lpm:use-case:ai-adoption-readiness" }, { "type": "applies_to", "targetId": "lpm:use-case:ma-integration" }, { "type": "applies_to", "targetId": "lpm:use-case:platform-governance" } ], "content": { "number": "04", "shortName": "Information", "definition": "Defines how trusted information is created, maintained, accessed, refreshed, and used across the enterprise.", "primaryQuestion": "What information can leaders trust?", "whyItMatters": "AI is only as useful as the information environment it learns from and acts within.", "businessProblem": "Leaders cannot make confident decisions when information is duplicated, stale, conflicting, or hard to trust.", "symptoms": [ "Multiple sources of truth exist for the same topic.", "Reports conflict across teams.", "Decisions rely on stale or undocumented information.", "AI tools retrieve plausible but unreliable context." ], "diagnosticQuestions": [ "What information do leaders trust?", "Where is the source of truth?", "Who owns freshness and quality?", "Can decisions be traced back to evidence?" ], "artifacts": [ "Source-of-truth map", "Information ownership register", "Data lineage map", "Knowledge freshness review" ], "aiReadinessQuestion": "Can AI retrieve trusted, current, and governed information?", "aiRiskWhenWeak": "AI accelerates the spread of outdated or conflicting information.", "connection": "Information turns communication into reusable operating memory, evidence, and decision context." } }, { "id": "lpm:layer:platform-structure", "type": "layer", "slug": "platform-structure", "title": "Platform Structure", "canonicalUrl": "https://largepeoplemodel.com/framework/platform-structure", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:layer:information-ecology" }, { "type": "applies_to", "targetId": "lpm:use-case:product-operating-model" }, { "type": "applies_to", "targetId": "lpm:use-case:ma-integration" }, { "type": "applies_to", "targetId": "lpm:use-case:platform-governance" } ], "content": { "number": "05", "shortName": "Platforms", "definition": "Maps how tools, systems, workflows, and integrations shape how work actually moves through the enterprise.", "primaryQuestion": "Where does work actually move?", "whyItMatters": "Tool sprawl creates coordination debt when platforms are not intentionally structured.", "businessProblem": "Work fragments across systems, creating manual handoffs, duplicate effort, and poor visibility.", "symptoms": [ "Teams use different tools for the same workflow.", "Work moves through manual handoffs.", "Platform ownership is unclear.", "Leaders cannot see the real operating flow." ], "diagnosticQuestions": [ "Which platforms carry critical work?", "Where does work leave one system and enter another?", "Who owns the platform workflow?", "Which tools duplicate or conflict with each other?" ], "artifacts": [ "Platform map", "Workflow inventory", "Integration map", "Tool rationalization worksheet" ], "aiReadinessQuestion": "Can AI operate across platforms with clear ownership, integration, and controls?", "aiRiskWhenWeak": "Governed agents act across fragmented systems without reliable operating boundaries.", "connection": "Platforms operationalize work, decisions, information, and governance through tools and workflows." } }, { "id": "lpm:layer:governance-architecture", "type": "layer", "slug": "governance-architecture", "title": "Governance Architecture", "canonicalUrl": "https://largepeoplemodel.com/framework/governance-architecture", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:layer:platform-structure" }, { "type": "applies_to", "targetId": "lpm:use-case:ai-adoption-readiness" }, { "type": "applies_to", "targetId": "lpm:use-case:platform-governance" }, { "type": "applies_to", "targetId": "lpm:use-case:agent-workforce-governance" } ], "content": { "number": "06", "shortName": "Governance", "definition": "Defines the controls, policies, review loops, and decision boundaries that keep execution safe without slowing it unnecessarily.", "primaryQuestion": "How is risk controlled without freezing execution?", "whyItMatters": "Governance should create trust and speed, not theater and delay.", "businessProblem": "Governance is either too slow to support execution or too weak to manage risk.", "symptoms": [ "Approvals are unclear or excessive.", "Policy exceptions are hard to track.", "Governance happens after work is already in motion.", "AI use cases scale before controls are defined." ], "diagnosticQuestions": [ "What must be governed?", "Who approves exceptions?", "Which controls are preventive versus reactive?", "Where does governance slow execution without reducing risk?" ], "artifacts": [ "Governance decision tree", "Control map", "Risk acceptance register", "AI governance checklist" ], "aiReadinessQuestion": "Are AI decisions, exceptions, and controls visible enough to govern?", "aiRiskWhenWeak": "AI scales faster than oversight, auditability, and risk ownership.", "connection": "Governance sets the operating boundaries for safe, scalable, and trusted execution." } }, { "id": "lpm:layer:ai-amplification", "type": "layer", "slug": "ai-amplification", "title": "AI Amplification", "canonicalUrl": "https://largepeoplemodel.com/framework/ai-amplification", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:layer:governance-architecture" }, { "type": "applies_to", "targetId": "lpm:use-case:ai-adoption-readiness" }, { "type": "applies_to", "targetId": "lpm:use-case:platform-governance" }, { "type": "applies_to", "targetId": "lpm:use-case:agent-workforce-governance" } ], "content": { "number": "07", "shortName": "AI Amplification", "definition": "Determines whether AI improves the operating model or amplifies the dysfunction already inside it.", "primaryQuestion": "Will AI amplify clarity or chaos?", "whyItMatters": "AI does not remove the need for ownership, decisions, communication, information, platforms, or governance. It magnifies their quality.", "businessProblem": "AI pilots create activity but fail to become trusted enterprise capability.", "symptoms": [ "Many AI pilots, few scaled outcomes.", "AI use cases lack owners, controls, and adoption paths.", "Agents automate fragmented workflows.", "Leaders cannot tell whether AI improved the operating model or just accelerated work." ], "diagnosticQuestions": [ "What is AI amplifying?", "Who owns the AI-supported outcome?", "What decisions can AI support versus make?", "How are AI actions monitored and governed?" ], "artifacts": [ "AI readiness assessment", "Agent accountability checklist", "Human-in-the-loop model", "AI use case governance register" ], "aiReadinessQuestion": "Will AI amplify clarity or chaos?", "aiRiskWhenWeak": "AI makes the hidden operating model more powerful before it is understood.", "connection": "AI touches every layer and amplifies the quality, clarity, or dysfunction of the system underneath it." } }, { "id": "lpm:law:01", "type": "law", "slug": "01", "title": "Structure Governs Behavior", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-01", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:platform-structure" } ], "content": { "number": "01", "claim": "People scale to the structure they are given. Performance problems are usually architecture problems.", "dependency": "Foundation law. Every other law depends on whether the operating structure is explicit.", "application": { "number": "01", "violation": "A transformation team adds a new workflow tool, but approvals still follow the old reporting hierarchy and work slows down.", "useWhen": "a recurring behavior persists despite new tools, policies, or leadership messages", "layers": [ "Ownership", "Platforms" ], "problem": { "label": "Fragmented truth", "slug": "fragmented-truth" } } } }, { "id": "lpm:law:02", "type": "law", "slug": "02", "title": "Decisions Are the Atomic Unit", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-02", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:law:01" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:communication-architecture" } ], "content": { "number": "02", "claim": "Organizations move through decisions. Every delay is a decision-architecture failure.", "dependency": "Depends on Law 1 because decision behavior follows the structure that defines it.", "application": { "number": "02", "violation": "A product launch is revisited in three meetings because contributors were named but one decider was not.", "useWhen": "decisions stall, reverse, or escalate because authority is unclear", "layers": [ "Decisions", "Communication" ], "problem": { "label": "Slow decisions", "slug": "slow-decisions" } } } }, { "id": "lpm:law:03", "type": "law", "slug": "03", "title": "Incentives Override Intent", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-03", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:law:01" }, { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" } ], "content": { "number": "03", "claim": "Declared values never beat lived incentives. Behavior follows what is rewarded.", "dependency": "Depends on Law 1 because incentives are part of the operating structure.", "application": { "number": "03", "violation": "Leaders ask teams to share customer data while bonuses still reward isolated departmental performance.", "useWhen": "stated priorities and rewarded behavior point in different directions", "layers": [ "Ownership", "Governance" ], "problem": { "label": "Unclear ownership", "slug": "unclear-ownership" } } } }, { "id": "lpm:law:04", "type": "law", "slug": "04", "title": "Information Quality Determines Intelligence", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-04", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:law:01" }, { "type": "depends_on", "targetId": "lpm:law:02" }, { "type": "applies_to", "targetId": "lpm:layer:information-ecology" }, { "type": "applies_to", "targetId": "lpm:layer:platform-structure" } ], "content": { "number": "04", "claim": "Signal clarity determines decision quality, human or AI. AI on ungoverned information amplifies disorder.", "dependency": "Depends on Laws 1 and 2 because information quality follows ownership and decision discipline.", "application": { "number": "04", "violation": "An AI assistant gives different answers because policy copies across three systems have different owners and dates.", "useWhen": "people or AI cannot identify which information is current and authoritative", "layers": [ "Information", "Platforms" ], "problem": { "label": "Fragmented truth", "slug": "fragmented-truth" } } } }, { "id": "lpm:law:05", "type": "law", "slug": "05", "title": "Governance Must Reduce Entropy", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-05", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:law:01" }, { "type": "depends_on", "targetId": "lpm:law:02" }, { "type": "depends_on", "targetId": "lpm:law:03" }, { "type": "depends_on", "targetId": "lpm:law:04" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" } ], "content": { "number": "05", "claim": "Real governance creates operational clarity. Compliance theater creates the appearance of control.", "dependency": "Depends on Laws 1 through 4 because governance must govern real behavior, decisions, incentives, and information.", "application": { "number": "05", "violation": "A review board approves AI use annually, but no control fires when confidence drops or exceptions spike.", "useWhen": "governance exists in documents but not inside the path of work", "layers": [ "Governance", "Decisions" ], "problem": { "label": "AI governance gaps", "slug": "ai-governance-gaps" } } } }, { "id": "lpm:law:06", "type": "law", "slug": "06", "title": "Architecture Precedes AI", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-06", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:law:01" }, { "type": "depends_on", "targetId": "lpm:law:02" }, { "type": "depends_on", "targetId": "lpm:law:03" }, { "type": "depends_on", "targetId": "lpm:law:04" }, { "type": "depends_on", "targetId": "lpm:law:05" }, { "type": "applies_to", "targetId": "lpm:layer:ai-amplification" } ], "content": { "number": "06", "claim": "AI amplifies the operating system beneath it. Structure before velocity.", "dependency": "Depends on Laws 1 through 5 because AI requires structure, decisions, incentives, information, and governance.", "application": { "number": "06", "violation": "An agent accelerates refund recommendations while unclear ownership and conflicting policy sources create faster mistakes.", "useWhen": "AI activity is increasing faster than operating-model readiness", "layers": [ "AI", "All supporting layers" ], "problem": { "label": "AI governance gaps", "slug": "ai-governance-gaps" } } } }, { "id": "lpm:law:07", "type": "law", "slug": "07", "title": "Leaders Model the System", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-07", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "supports", "targetId": "lpm:law:01" }, { "type": "supports", "targetId": "lpm:law:02" }, { "type": "supports", "targetId": "lpm:law:03" }, { "type": "supports", "targetId": "lpm:law:04" }, { "type": "supports", "targetId": "lpm:law:05" }, { "type": "supports", "targetId": "lpm:law:06" }, { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" } ], "content": { "number": "07", "claim": "The operating model mirrors leadership behavior. No exception.", "dependency": "Sustains Laws 1 through 6 because leaders make the architecture credible in practice.", "application": { "number": "07", "violation": "Executives bypass the published decision path for urgent work, teaching everyone that the architecture is optional.", "useWhen": "leadership exceptions are weakening an otherwise sound operating design", "layers": [ "Ownership", "Governance" ], "problem": { "label": "Unclear ownership", "slug": "unclear-ownership" } } } }, { "id": "lpm:law:08", "type": "law", "slug": "08", "title": "Autonomy Is Earned", "canonicalUrl": "https://largepeoplemodel.com/framework/laws#law-08", "version": "0.5.0", "status": "canonical", "source": "@lpm/canon@0.5.0", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "depends_on", "targetId": "lpm:law:05" }, { "type": "depends_on", "targetId": "lpm:law:06" }, { "type": "applies_to", "targetId": "lpm:layer:ai-amplification" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" } ], "content": { "number": "08", "claim": "No agent or decision type advances in autonomy by default. 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Confirm whether each has one accountable active owner and no disputed ownership.", "diagnosticQuestions": [ "Where is ownership clarity score already visible in the operating model?", "Which owner can improve this ownership signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "maturity-scoring", "manual-artifact" ], "measurement": { "formula": { "label": "Owner clarity ratio", "formula": "(items with exactly one active, undisputed owner / total scoped items) * 100", "numerator": "Scoped outcomes, workflows, systems, decisions, or AI use cases with one active owner", "denominator": "All scoped items reviewed", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Scoped item inventory", "Named owner", "Owner active status", "Dispute flag" ], "dataSources": [ "Ownership Map", "Decision Rights Matrix", "Workflow inventory", "Platform owner list" ], "collectionCadence": "Monthly 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"Counting committee names as owners", "Ignoring disputed ownership", "Mixing owners with contributors" ], "recommendedActions": [ "Resolve owner gaps", "Publish decision-owner changes", "Review ownership again next month" ], "relatedUseCases": [ "transformation-operating-model", "ai-adoption-readiness", "agent-workforce-governance" ] }, "traceabilityNote": "Supported by current model", "tags": [ "ownership", "accountability", "owner coverage" ] } }, { "id": "lpm:metric:decision-owner-coverage", "type": "metric", "slug": "decision-owner-coverage", "title": "Decision owner coverage", "canonicalUrl": "https://largepeoplemodel.com/metrics/decision-owner-coverage", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "identity-and-incentives", "supportStatus": "platform-backed-supported", "definition": "The share of decision types that have a single named owner accountable for the outcome.", "whatItReveals": "Whether decisions can be traced to accountable people instead of committees.", "whyItMatters": "This metric helps leaders see whether the ownership layer is healthy enough to support execution and AI readiness.", "manualAssessment": "List consequential decision types and identify the named decision owner, approver, and escalation path for each.", "diagnosticQuestions": [ "Where is decision owner coverage already visible in the operating model?", "Which owner can improve this ownership signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "maturity-scoring", "manual-artifact" ], "measurement": { "formula": { "label": "Decision owner coverage", "formula": "(decision types with one named 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decision types." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting approvers as owners", "Leaving decision types too broad", "Not documenting escalation" ], "recommendedActions": [ "Update the decision rights matrix", "Assign owner authority", "Review repeated escalations" ], "relatedUseCases": [ "product-operating-model", "transformation-operating-model", "platform-governance" ] }, "traceabilityNote": "Supported by current model", "tags": [ "decision owner", "decision rights", "accountability" ] } }, { "id": "lpm:metric:outcome-owner-coverage", "type": "metric", "slug": "outcome-owner-coverage", "title": "Outcome owner coverage", "canonicalUrl": "https://largepeoplemodel.com/metrics/outcome-owner-coverage", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "identity-and-incentives", "supportStatus": "platform-backed-supported", "definition": "The share of named organizational outcomes that have a single named owner.", "whatItReveals": "Whether strategy and transformation work can be traced to accountable owners.", "whyItMatters": "This metric helps leaders see whether the ownership layer is healthy enough to support execution and AI readiness.", "manualAssessment": "List priority outcomes and confirm whether each has a named owner with authority, accountability, and review cadence.", "diagnosticQuestions": [ "Where is outcome owner coverage already visible in the operating model?", "Which owner can improve this ownership signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "maturity-scoring", "manual-artifact" ], "measurement": { "formula": { "label": "Outcome owner coverage", "formula": "(priority outcomes with one named accountable owner / total priority outcomes) * 100", "numerator": "Priority outcomes with one named accountable owner", "denominator": "All priority outcomes reviewed", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Priority outcome list", "Accountable owner", "Authority confirmation", "Review cadence" ], "dataSources": [ "Strategy plan", "OKR register", "Transformation roadmap", "Ownership Map" ], "collectionCadence": "Monthly during strategic execution reviews.", "workedExample": { "scenario": "Executive team reviews enterprise outcomes for the next two quarters.", "inputs": [ { "label": "Priority outcomes", "value": "18" }, { "label": "Outcomes with named owners", "value": "14" } ], "calculation": "14 / 18 * 100", "result": "77.8%", "interpretation": "Several outcomes are still aspirations rather than owned commitments.", "recommendedAction": "Assign owners and review cadence for the 4 uncovered outcomes." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting a sponsor as the accountable owner", "Listing team names instead of people" ], "recommendedActions": [ "Name accountable owners", "Confirm owner authority", "Add outcomes to operating review" ], "relatedUseCases": [ "transformation-operating-model", "ma-integration" ] }, "traceabilityNote": "Supported by current model", "tags": [ "outcomes", "ownership", "transformation" ] } }, { "id": "lpm:metric:accountability-gap-count", "type": "metric", "slug": "accountability-gap-count", "title": "Accountability gap count", "canonicalUrl": "https://largepeoplemodel.com/metrics/accountability-gap-count", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:identity-and-incentives" } ], "content": { "layer": "identity-and-incentives", "supportStatus": "platform-backed-live", "definition": "The number of entities that can affect an outcome but resolve to no named owner, or sit on a broken ownership chain.", "whatItReveals": "Where accountability disappears across workflows, platforms, decisions, or risks.", "whyItMatters": "This metric helps leaders see whether the ownership layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Map a workflow or operating problem and count the points where no owner can approve, correct, or carry the result.", "diagnosticQuestions": [ "Where is accountability gap count already visible in the operating model?", "Which owner can improve this ownership signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Accountability gaps", "formula": "count(unowned entities + broken ownership-chain points)", "unit": "count", "direction": "lower-is-better" }, "requiredInputs": [ "Workflow map", "Owner map", "Decision map", "Platform map", "AI responsibility map" ], "dataSources": [ "Ownership Map", "Workflow inventory", "Platform inventory", "Agent accountability checklist" ], "collectionCadence": "Every operating review until gaps are declining.", "workedExample": { "scenario": "Workflow review for a customer-impacting AI-supported process.", "inputs": [ { "label": "Unowned handoffs", "value": "7" }, { "label": "Unowned data sources", "value": "3" }, { "label": "AI decision points without supervisor", "value": "2" } ], "calculation": "7 + 3 + 2", "result": "12 gaps", "interpretation": "The workflow has multiple points where accountability can disappear.", "recommendedAction": "Prioritize gaps closest to customer, regulatory, or AI decision risk." }, "interpretationBands": [ { "label": "Healthy", "range": "Low and declining", "meaning": "Gaps are being removed on cadence." }, { "label": "Watch", "range": "Flat", "meaning": "The operating model is not improving." }, { "label": "Risk", "range": "Rising", "meaning": "Debt is accumulating faster than teams are resolving it." }, { "label": "Critical", "range": "Concentrated in critical work", "meaning": "Escalate for owner action." } ], "commonPitfalls": [ "Treating low counts as safe without checking severity", "Ignoring broken chains across teams" ], "recommendedActions": [ "Assign owners", "Close broken chains", "Track gap count trend" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance", "ma-integration" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "ownership gaps", "risk signals", "lineage" ] } }, { "id": "lpm:metric:incentive-conflict-score", "type": "metric", "slug": "incentive-conflict-score", "title": "Incentive conflict score", "canonicalUrl": "https://largepeoplemodel.com/metrics/incentive-conflict-score", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:identity-and-incentives" } ], "content": { "layer": "identity-and-incentives", "supportStatus": "future-signal-required", "definition": "The degree to which an owner's incentives pull against the outcome they own.", "whatItReveals": "Where local performance signals may undermine enterprise outcomes.", "whyItMatters": "This metric helps leaders see whether the ownership layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Compare owner goals, rewards, performance metrics, and expected enterprise outcomes for signs of conflict.", "diagnosticQuestions": [ "Where is incentive conflict score already visible in the operating model?", "Which owner can improve this ownership signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "manual-artifact" ], "measurement": { "formula": { "label": "Incentive conflict score", "formula": "(conflict indicators present / total incentive indicators assessed) * 100", "numerator": "Observed conflicts across goals, rewards, local KPIs, enterprise outcomes, and risk behavior", "denominator": "Total incentive checks performed", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Owner goals", "Reward model", "Local KPIs", "Enterprise outcomes", "Observed behaviors" ], "dataSources": [ "Performance plans", "OKRs", "Incentive plans", "Leadership interviews" ], "collectionCadence": "Semiannually and before major operating-model redesign.", "workedExample": { "scenario": "Ten platform and product owners assessed across five incentive checks each.", "inputs": [ { "label": "Conflict indicators", "value": "17" }, { "label": "Total checks", "value": "50" } ], "calculation": "17 / 50 * 100", "result": "34%", "interpretation": "Owners may rationally optimize local metrics against enterprise outcomes.", "recommendedAction": "Redesign incentives for owners with high-conflict signals." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Measuring stated intent instead of actual incentives", "Ignoring local optimization" ], "recommendedActions": [ "Align incentives to enterprise outcomes", "Review conflicting KPIs", "Change owner scorecards" ], "relatedUseCases": [ "transformation-operating-model", "product-operating-model" ] }, "traceabilityNote": "Requires future connector or instrumentation", "tags": [ "incentives", "misalignment", "future signal" ] } }, { "id": "lpm:metric:decision-latency", "type": "metric", "slug": "decision-latency", "title": "Decision latency", "canonicalUrl": "https://largepeoplemodel.com/metrics/decision-latency", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "decision-architecture", "supportStatus": "platform-backed-supported", "definition": "The time it takes a decision to move from raised to decided and executed.", "whatItReveals": "Where execution is slowed by delayed, unresolved, or poorly governed choices.", "whyItMatters": "This metric helps leaders see whether the decisions layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Select a decision sample and record when each decision was raised, decided, communicated, and executed.", "diagnosticQuestions": [ "Where is decision latency already visible in the operating model?", "Which owner can improve this decisions signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "maturity-scoring", "lapemo-signal" ], "measurement": { "formula": { "label": "Raised-to-decided cycle time", "formula": "average(decided timestamp - raised timestamp)", "numerator": "Total elapsed days across decisions", "denominator": "Number of decisions sampled", "unit": "days", "direction": "lower-is-better" }, "requiredInputs": [ "Decision raised date", "Decision date", "Decision type", "Decision owner" ], "dataSources": [ "Decision log", "Meeting notes", "Governance workflow", "Product intake board" ], "collectionCadence": "Monthly by decision type.", "workedExample": { "scenario": "Portfolio team samples 20 consequential decisions from the last month.", "inputs": [ { "label": "Total elapsed days", "value": "210" }, { "label": "Decisions sampled", "value": "20" } ], "calculation": "210 / 20", "result": "10.5 days average latency", "interpretation": "Decision flow is slow enough to affect execution cadence.", "recommendedAction": "Segment latency by decision type and inspect owner coverage for the slowest group." }, "interpretationBands": [ { "label": "Healthy", "range": "Within decision-type SLA", "meaning": "Decisions move at the expected cadence." }, { "label": "Watch", "range": "1-25% over SLA", "meaning": "Inspect repeat delays." }, { "label": "Risk", "range": "26-75% over SLA", "meaning": "Decision debt is slowing work." }, { "label": "Critical", "range": ">75% over SLA", "meaning": "Escalate decision architecture redesign." } ], "commonPitfalls": [ "Mixing strategic and operational decisions", "Ignoring decided-to-executed lag" ], "recommendedActions": [ "Clarify decision rights", "Create decision SLAs", "Review aging decisions weekly" ], "relatedUseCases": [ "product-operating-model", "transformation-operating-model", "ma-integration" ] }, "traceabilityNote": "Supported by current model", "tags": [ "decision debt", "latency", "execution" ] } }, { "id": "lpm:metric:decision-reversal-rate", "type": "metric", "slug": "decision-reversal-rate", "title": "Decision reversal rate", "canonicalUrl": "https://largepeoplemodel.com/metrics/decision-reversal-rate", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "decision-architecture", "supportStatus": "platform-backed-supported", "definition": "The share of decisions later overturned or replaced.", "whatItReveals": "Whether decisions are stable commitments or recurring sources of rework.", "whyItMatters": "This metric helps leaders see whether the decisions layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review decisions over a period and count how many were superseded, reopened, or replaced by later decisions.", "diagnosticQuestions": [ "Where is decision reversal rate already visible in the operating model?", "Which owner can improve this decisions signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Decision reversal rate", "formula": "(decisions reversed, reopened, or superseded / total decisions in period) * 100", "numerator": "Reversed, reopened, or superseded decisions", "denominator": "Total decisions in period", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Decision log", "Decision status", "Superseded link", "Reopen date" ], "dataSources": [ "Decision log", "Audit trail", "Operating review notes" ], "collectionCadence": "Monthly for active transformation and product governance.", "workedExample": { "scenario": "Review of decisions made in the last 60 days.", "inputs": [ { "label": "Reversed or reopened decisions", "value": "8" }, { "label": "Total decisions", "value": "50" } ], "calculation": "8 / 50 * 100", "result": "16%", "interpretation": "Decisions are not consistently becoming stable commitments.", "recommendedAction": "Inspect evidence quality, rights, and stakeholder inclusion for reversed decisions." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Counting planned iteration as reversal", "Ignoring informal reversals" ], "recommendedActions": [ "Improve decision evidence", "Document rationale", "Separate experiments from commitments" ], "relatedUseCases": [ "product-operating-model", "transformation-operating-model" ] }, "traceabilityNote": "Supported by current model", "tags": [ "reversal", "rework", "decision lineage" ] } }, { "id": "lpm:metric:decision-aging", "type": "metric", "slug": "decision-aging", "title": "Decision aging", "canonicalUrl": "https://largepeoplemodel.com/metrics/decision-aging", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "decision-architecture", "supportStatus": "platform-backed-supported", "definition": "How long open decisions have been waiting unresolved.", "whatItReveals": "Where unresolved choices are accumulating into decision debt.", "whyItMatters": "This metric helps leaders see whether the decisions layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review open decisions and calculate age from creation or escalation date against the expected decision cadence.", "diagnosticQuestions": [ "Where is decision aging already visible in the operating model?", "Which owner can improve this decisions signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Open decision age", "formula": "current date - raised date for each open decision; report median, p90, and over-SLA count", "unit": "days", "direction": "lower-is-better" }, "requiredInputs": [ "Open decision list", "Raised date", "Expected cadence", "Decision owner" ], "dataSources": [ "Decision log", "Escalation register", "Governance workflow" ], "collectionCadence": "Weekly for active portfolios.", "workedExample": { "scenario": "Portfolio review of unresolved decisions.", "inputs": [ { "label": "Open decisions", "value": "34" }, { "label": "Median age", "value": "18 days" }, { "label": "Older than 30 days", "value": "9" } ], "calculation": "Count decisions where age > 30 days", "result": "9 aged decisions", "interpretation": "Unresolved choices are accumulating into decision debt.", "recommendedAction": "Escalate aged decisions to named owners with closure dates." }, "interpretationBands": [ { "label": "Healthy", "range": "Low and declining", "meaning": "Gaps are being removed on cadence." }, { "label": "Watch", "range": "Flat", "meaning": "The operating model is not improving." }, { "label": "Risk", "range": "Rising", "meaning": "Debt is accumulating faster than teams are resolving it." }, { "label": "Critical", "range": "Concentrated in critical work", "meaning": "Escalate for owner action." } ], "commonPitfalls": [ "Only reporting average age", "Not separating blocked decisions from neglected decisions" ], "recommendedActions": [ "Publish aged decision list", "Assign closure owner", "Remove stale decisions" ], "relatedUseCases": [ "transformation-operating-model", "product-operating-model" ] }, "traceabilityNote": "Supported by current model", "tags": [ "aging", "open decisions", "bottlenecks" ] } }, { "id": "lpm:metric:decision-dependency-count", "type": "metric", "slug": "decision-dependency-count", "title": "Decision dependency count", "canonicalUrl": "https://largepeoplemodel.com/metrics/decision-dependency-count", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "decision-architecture", "supportStatus": "platform-backed-supported", "definition": "How many other decisions or entities a decision depends on or feeds.", "whatItReveals": "Where a decision sits inside a broader chain of commitments and constraints.", "whyItMatters": "This metric helps leaders see whether the decisions layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Map upstream decisions, downstream decisions, affected systems, owners, policies, and workflows for a critical decision.", "diagnosticQuestions": [ "Where is decision dependency count already visible in the operating model?", "Which owner can improve this decisions signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "maturity-scoring" ], "measurement": { "formula": { "label": "Decision dependency count", "formula": "count(upstream decisions + downstream decisions + affected systems + policies + owners)", "unit": "count", "direction": "context-dependent" }, "requiredInputs": [ "Decision record", "Upstream/downstream links", "Affected systems", "Policies", "Owners" ], "dataSources": [ "Decision log", "Architecture map", "Control map", "Platform inventory" ], "collectionCadence": "For high-impact decisions before approval.", "workedExample": { "scenario": "Pricing decision dependency review.", "inputs": [ { "label": "Upstream decisions", "value": "4" }, { "label": "Affected systems", "value": "6" }, { "label": "Policy checks", "value": "3" } ], "calculation": "4 + 6 + 3", "result": "13 dependencies", "interpretation": "This decision needs explicit sequencing and orchestration.", "recommendedAction": "Create a dependency map before final approval." }, "interpretationBands": [ { "label": "Healthy", "range": "Low and declining", "meaning": "Gaps are being removed on cadence." }, { "label": "Watch", "range": "Flat", "meaning": "The operating model is not improving." }, { "label": "Risk", "range": "Rising", "meaning": "Debt is accumulating faster than teams are resolving it." }, { "label": "Critical", "range": "Concentrated in critical work", "meaning": "Escalate for owner action." } ], "commonPitfalls": [ "Treating high dependency as bad by default", "Missing downstream operational effects" ], "recommendedActions": [ "Map sequencing", "Assign dependency owners", "Review downstream impacts" ], "relatedUseCases": [ "ma-integration", "platform-governance", "product-operating-model" ] }, "traceabilityNote": "Supported by current model", "tags": [ "dependency", "lineage", "decision graph" ] } }, { "id": "lpm:metric:decision-confidence-score", "type": "metric", "slug": "decision-confidence-score", "title": "Decision confidence score", "canonicalUrl": "https://largepeoplemodel.com/metrics/decision-confidence-score", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "decision-architecture", "supportStatus": "platform-backed-supported", "definition": "Model confidence on AI-involved decisions, measured against the required gate.", "whatItReveals": "Whether AI-supported decisions meet the confidence level required for action.", "whyItMatters": "This metric helps leaders see whether the decisions layer is healthy enough to support execution and AI readiness.", "manualAssessment": "For AI-assisted decisions, compare confidence, evidence, human review, and required approval threshold.", "diagnosticQuestions": [ "Where is decision confidence score already visible in the operating model?", "Which owner can improve this decisions signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "ai-readiness", "governance-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Confidence against gate", "formula": "AI/model confidence score compared with required decision gate", "unit": "score", "direction": "higher-is-better" }, "requiredInputs": [ "Model confidence", "Decision risk tier", "Required gate", "Evidence completeness" ], "dataSources": [ "AI decision record", "Governance gate", "Evidence register" ], "collectionCadence": "Every AI-assisted decision above low risk.", "workedExample": { "scenario": "AI-assisted vendor-risk recommendation.", "inputs": [ { "label": "Model confidence", "value": "0.82" }, { "label": "Required gate", "value": "0.90" } ], "calculation": "0.82 compared with 0.90 required gate", "result": "Below gate by 0.08", "interpretation": "The decision requires human review or additional evidence.", "recommendedAction": "Do not automate the action until the gate is met or reviewed." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100", "meaning": "Strong operating foundation." }, { "label": "Watch", "range": "70-84", "meaning": "Adequate with visible improvement areas." }, { "label": "Risk", "range": "50-69", "meaning": "Weak foundation; inspect the lowest layer contributors." }, { "label": "Critical", "range": "<50", "meaning": "Material execution or AI-readiness risk." } ], "commonPitfalls": [ "Treating confidence as correctness", "Ignoring risk tier", "Not checking evidence quality" ], "recommendedActions": [ "Add evidence", "Require human approval", "Lower autonomy" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance" ] }, "traceabilityNote": "Supported by current model", "tags": [ "AI decisions", "confidence", "gates" ] } }, { "id": "lpm:metric:escalation-rate", "type": "metric", "slug": "escalation-rate", "title": "Escalation rate", "canonicalUrl": "https://largepeoplemodel.com/metrics/escalation-rate", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "decision-architecture", "supportStatus": "future-signal-required", "definition": "How often decisions trigger an escalation path.", "whatItReveals": "Whether decision rights are clear enough for normal work to resolve locally.", "whyItMatters": "This metric helps leaders see whether the decisions layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review decision logs and meeting notes for decisions escalated beyond the expected owner or forum.", "diagnosticQuestions": [ "Where is escalation rate already visible in the operating model?", "Which owner can improve this decisions signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "manual-artifact" ], "measurement": { "formula": { "label": "Escalation rate", "formula": "(decisions escalated beyond expected owner or forum / total decisions sampled) * 100", "numerator": "Escalated decisions", "denominator": "Total decisions sampled", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Decision log", "Expected owner/forum", "Escalation flag", "Escalation reason" ], "dataSources": [ "Decision log", "Meeting notes", "Escalation register" ], "collectionCadence": "Monthly by decision type.", "workedExample": { "scenario": "Operating review of cross-functional decisions.", "inputs": [ { "label": "Escalated decisions", "value": "15" }, { "label": "Decisions sampled", "value": "60" } ], "calculation": "15 / 60 * 100", "result": "25%", "interpretation": "Too many decisions exceed their expected owner or forum.", "recommendedAction": "Clarify owner authority and decision thresholds." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Counting healthy risk escalation as failure", "Not capturing informal escalation" ], "recommendedActions": [ "Clarify decision rights", "Train owners", "Review repeated escalation reasons" ], "relatedUseCases": [ "product-operating-model", "transformation-operating-model" ] }, "traceabilityNote": "Requires future connector or instrumentation", "tags": [ "escalation", "decision rights", "future signal" ] } }, { "id": "lpm:metric:channel-fragmentation", "type": "metric", "slug": "channel-fragmentation", "title": "Channel fragmentation", "canonicalUrl": "https://largepeoplemodel.com/metrics/channel-fragmentation", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:communication-architecture" } ], "content": { "layer": "communication-architecture", "supportStatus": "platform-backed-supported", "definition": "How scattered or ungoverned communication is, rather than purpose-fit.", "whatItReveals": "Whether communication channels have clear purpose, ownership, and governance.", "whyItMatters": "This metric helps leaders see whether the communication layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Inventory major communication channels and classify their purpose, owner, governance status, and decision relevance.", "diagnosticQuestions": [ "Where is channel fragmentation already visible in the operating model?", "Which owner can improve this communication signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "maturity-scoring", "manual-artifact" ], "measurement": { "formula": { "label": "Ungoverned channel share", "formula": "(channels without clear purpose, owner, or governance / total active channels) * 100", "numerator": "Channels missing purpose, owner, or governance", "denominator": "Total active communication channels", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Channel inventory", "Channel purpose", "Owner", "Governance status" ], "dataSources": [ "Communication map", "Collaboration tools", "Team operating agreements" ], "collectionCadence": "Quarterly, and during collaboration redesign.", "workedExample": { "scenario": "Enterprise product organization channel inventory.", "inputs": [ { "label": "Active channels", "value": "42" }, { "label": "Channels lacking purpose or owner", "value": "19" } ], "calculation": "19 / 42 * 100", "result": "45.2%", "interpretation": "Communication is likely fragmented and hard to govern.", "recommendedAction": "Retire, consolidate, or assign purpose and owners to unmanaged channels." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Counting inactive channels", "Ignoring meeting forums as channels" ], "recommendedActions": [ "Publish channel purposes", "Assign owners", "Close redundant channels" ], "relatedUseCases": [ "transformation-operating-model", "product-operating-model" ] }, "traceabilityNote": "Supported by current model", "tags": [ "communication", "channels", "fragmentation" ] } }, { "id": "lpm:metric:communication-load", "type": "metric", "slug": "communication-load", "title": "Communication load", "canonicalUrl": "https://largepeoplemodel.com/metrics/communication-load", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:communication-architecture" } ], "content": { "layer": "communication-architecture", "supportStatus": "future-signal-required", "definition": "Volume of communication carried per person or channel.", "whatItReveals": "Whether teams are spending too much energy transmitting messages instead of creating clarity.", "whyItMatters": "This metric helps leaders see whether the communication layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Estimate meeting load, message volume, update frequency, and communication burden across a sample team or workflow.", "diagnosticQuestions": [ "Where is communication load already visible in the operating model?", "Which owner can improve this communication signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "manual-artifact" ], "measurement": { "formula": { "label": "Communication burden", "formula": "messages, updates, and meeting hours per person per week", "unit": "index", "direction": "lower-is-better" }, "requiredInputs": [ "Meeting hours", "Message volume", "Update frequency", "Team size" ], "dataSources": [ "Calendar sample", "Collaboration tools", "Team survey" ], "collectionCadence": "Monthly for overloaded teams; quarterly baseline otherwise.", "workedExample": { "scenario": "Product team communication sample.", "inputs": [ { "label": "Meeting hours per person", "value": "18/week" }, { "label": "Messages per person", "value": "420/week" } ], "calculation": "Review load against team baseline and decision output", "result": "High communication burden", "interpretation": "Communication volume is high enough to require channel and meeting design review.", "recommendedAction": "Reduce recurring meetings and convert status traffic into structured records." }, "interpretationBands": [ { "label": "Healthy", "range": "Low and declining", "meaning": "Gaps are being removed on cadence." }, { "label": "Watch", "range": "Flat", "meaning": "The operating model is not improving." }, { "label": "Risk", "range": "Rising", "meaning": "Debt is accumulating faster than teams are resolving it." }, { "label": "Critical", "range": "Concentrated in critical work", "meaning": "Escalate for owner action." } ], "commonPitfalls": [ "Treating all communication as waste", "Ignoring role differences" ], "recommendedActions": [ "Redesign meeting architecture", "Define channel purposes", "Move decisions into records" ], "relatedUseCases": [ "product-operating-model", "transformation-operating-model" ] }, "traceabilityNote": "Requires future connector or instrumentation", "tags": [ "communication load", "activity", "future signal" ] } }, { "id": "lpm:metric:meeting-density", "type": "metric", "slug": "meeting-density", "title": "Meeting density", "canonicalUrl": "https://largepeoplemodel.com/metrics/meeting-density", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:communication-architecture" } ], "content": { "layer": "communication-architecture", "supportStatus": "future-signal-required", "definition": "How much working time is consumed by meetings.", "whatItReveals": "Whether meetings are becoming a substitute for clear ownership, decisions, and communication design.", "whyItMatters": "This metric helps leaders see whether the communication layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review calendars for a sample group and calculate meeting hours as a share of available work time.", "diagnosticQuestions": [ "Where is meeting density already visible in the operating model?", "Which owner can improve this communication signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "manual-artifact" ], "measurement": { "formula": { "label": "Meeting density", "formula": "(meeting hours / available working hours) * 100", "numerator": "Meeting hours in period", "denominator": "Available working hours in period", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Calendar sample", "Working hours", "Meeting categories" ], "dataSources": [ "Calendar export", "Meeting audit", "Team survey" ], "collectionCadence": "Monthly for overloaded groups.", "workedExample": { "scenario": "Calendar sample for product managers.", "inputs": [ { "label": "Meeting hours", "value": "14.5" }, { "label": "Working hours", "value": "40" } ], "calculation": "14.5 / 40 * 100", "result": "36.3%", "interpretation": "More than a third of work time is in meetings.", "recommendedAction": "Audit meetings that do not produce decisions, commitments, or shared context." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Ignoring meeting quality", "Averaging away overloaded roles" ], "recommendedActions": [ "Remove low-output meetings", "Clarify decision forums", "Set meeting purpose rules" ], "relatedUseCases": [ "transformation-operating-model", "product-operating-model" ] }, "traceabilityNote": "Requires future connector or instrumentation", "tags": [ "meetings", "calendar", "future signal" ] } }, { "id": "lpm:metric:message-to-decision-ratio", "type": "metric", "slug": "message-to-decision-ratio", "title": "Message-to-decision ratio", "canonicalUrl": "https://largepeoplemodel.com/metrics/message-to-decision-ratio", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:communication-architecture" } ], "content": { "layer": "communication-architecture", "supportStatus": "future-signal-required", "definition": "How much communication it takes to produce a decision.", "whatItReveals": "Whether communication is creating decision clarity or just generating more discussion.", "whyItMatters": "This metric helps leaders see whether the communication layer is healthy enough to support execution and AI readiness.", "manualAssessment": "For a set of decisions, estimate the number of meetings, messages, and updates required before the decision was made.", "diagnosticQuestions": [ "Where is message-to-decision ratio already visible in the operating model?", "Which owner can improve this communication signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "manual-artifact" ], "measurement": { "formula": { "label": "Message-to-decision ratio", "formula": "messages or meeting interactions related to a topic / decisions made on that topic", "numerator": "Messages or interactions for the topic", "denominator": "Decisions made on the topic", "unit": "ratio", "direction": "lower-is-better" }, "requiredInputs": [ "Topic sample", "Message count", "Meeting interaction count", "Decision count" ], "dataSources": [ "Collaboration tools", "Meeting notes", "Decision log" ], "collectionCadence": "For high-friction topics during retrospectives.", "workedExample": { "scenario": "Launch-readiness topic over three weeks.", "inputs": [ { "label": "Messages and interactions", "value": "940" }, { "label": "Decisions", "value": "4" } ], "calculation": "940 / 4", "result": "235:1", "interpretation": "Discussion volume is not converting into decisions efficiently.", "recommendedAction": "Assign decision owners and turn the topic into a decision log." }, "interpretationBands": [ { "label": "Healthy", "range": "Low and declining", "meaning": "Gaps are being removed on cadence." }, { "label": "Watch", "range": "Flat", "meaning": "The operating model is not improving." }, { "label": "Risk", "range": "Rising", "meaning": "Debt is accumulating faster than teams are resolving it." }, { "label": "Critical", "range": "Concentrated in critical work", "meaning": "Escalate for owner action." } ], "commonPitfalls": [ "Counting messages without topic filtering", "Ignoring status updates that do not require decisions" ], "recommendedActions": [ "Create decision forum", "Set owner and due date", "Summarize context into one source" ], "relatedUseCases": [ "product-operating-model", "transformation-operating-model" ] }, "traceabilityNote": "Requires future connector or instrumentation", "tags": [ "message volume", "decision clarity", "future signal" ] } }, { "id": "lpm:metric:rework-from-unclear-communication", "type": "metric", "slug": "rework-from-unclear-communication", "title": "Rework from unclear communication", "canonicalUrl": "https://largepeoplemodel.com/metrics/rework-from-unclear-communication", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:communication-architecture" } ], "content": { "layer": "communication-architecture", "supportStatus": "future-signal-required", "definition": "Work redone because communication was unclear or lost.", "whatItReveals": "Where communication failure becomes execution waste.", "whyItMatters": "This metric helps leaders see whether the communication layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review incidents of rework and identify whether the root cause was unclear decision communication, missing context, or conflicting messages.", "diagnosticQuestions": [ "Where is rework from unclear communication already visible in the operating model?", "Which owner can improve this communication signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "manual-artifact" ], "measurement": { "formula": { "label": "Communication-caused rework", "formula": "(rework items attributed to misunderstood communication / total rework items) * 100", "numerator": "Rework items caused by unclear direction, missing context, or misunderstood commitments", "denominator": "Total rework items reviewed", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Rework log", "Cause category", "Original instruction", "Owner review" ], "dataSources": [ "Delivery retrospectives", "Support tickets", "Project reviews", "Meeting audit" ], "collectionCadence": "Monthly in delivery retrospectives.", "workedExample": { "scenario": "Review of rework tickets for a product release.", "inputs": [ { "label": "Communication-caused rework items", "value": "11" }, { "label": "Total rework items", "value": "38" } ], "calculation": "11 / 38 * 100", "result": "28.9%", "interpretation": "Nearly a third of rework is tied to unclear communication.", "recommendedAction": "Improve decision records, briefs, and handoff protocols." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Blaming communication when decision rights are the real cause", "Using vague cause categories" ], "recommendedActions": [ "Use communication protocols", "Record decisions", "Clarify handoffs" ], "relatedUseCases": [ "product-operating-model", "transformation-operating-model" ] }, "traceabilityNote": "Requires future connector or instrumentation", "tags": [ "rework", "communication failure", "future signal" ] } }, { "id": "lpm:metric:source-of-truth-coverage", "type": "metric", "slug": "source-of-truth-coverage", "title": "Source-of-truth coverage", "canonicalUrl": "https://largepeoplemodel.com/metrics/source-of-truth-coverage", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:information-ecology" } ], "content": { "layer": "information-ecology", "supportStatus": "platform-backed-live", "definition": "The share of knowledge domains with a single declared authoritative source.", "whatItReveals": "Whether leaders and AI systems know which source to trust.", "whyItMatters": "This metric helps leaders see whether the information layer is healthy enough to support execution and AI readiness.", "manualAssessment": "List critical knowledge domains and identify whether each has one declared authoritative source.", "diagnosticQuestions": [ "Where is source-of-truth coverage already visible in the operating model?", "Which owner can improve this information signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "ai-readiness", "lapemo-signal" ], "measurement": { "formula": { "label": "Source-of-truth coverage", "formula": "(critical domains with named authoritative source / total critical domains) * 100", "numerator": "Critical domains with authoritative source", "denominator": "Total critical domains reviewed", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Critical domain list", "Authoritative source", "Source owner", "Freshness expectation" ], "dataSources": [ "Source-of-Truth Map", "Data catalog", "Dashboard inventory", "Policy register" ], "collectionCadence": "Quarterly; monthly for AI-critical domains.", "workedExample": { "scenario": "Information review for executive reporting domains.", "inputs": [ { "label": "Critical domains", "value": "26" }, { "label": "Domains with named authoritative source", "value": "17" } ], "calculation": "17 / 26 * 100", "result": "65.4%", "interpretation": "Leaders and AI systems may be relying on conflicting or unofficial sources.", "recommendedAction": "Assign authoritative sources and owners for the 9 uncovered domains." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting dashboards as sources without checking upstream authority", "Ignoring source owners" ], "recommendedActions": [ "Name authoritative sources", "Assign source owners", "Publish freshness rules" ], "relatedUseCases": [ "ai-adoption-readiness", "platform-governance", "ma-integration" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "source of truth", "knowledge domains", "information trust" ] } }, { "id": "lpm:metric:information-freshness", "type": "metric", "slug": "information-freshness", "title": "Information freshness", "canonicalUrl": "https://largepeoplemodel.com/metrics/information-freshness", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:information-ecology" } ], "content": { "layer": "information-ecology", "supportStatus": "platform-backed-live", "definition": "The share of knowledge domains reviewed within their required cadence.", "whatItReveals": "Whether information is current enough to support decisions and AI use.", "whyItMatters": "This metric helps leaders see whether the information layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review each source of truth and record when it was last validated against its required review cadence.", "diagnosticQuestions": [ "Where is information freshness already visible in the operating model?", "Which owner can improve this information signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "ai-readiness", "lapemo-signal" ], "measurement": { "formula": { "label": "Freshness coverage", "formula": "(sources updated within required freshness window / total sources assessed) * 100", "numerator": "Sources updated within required window", "denominator": "Sources assessed", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Source list", "Last updated date", "Required freshness window", "Domain risk" ], "dataSources": [ "Data catalog", "Document metadata", "Dashboard metadata", "Source-of-Truth Map" ], "collectionCadence": "Monthly for high-risk information; quarterly for baseline.", "workedExample": { "scenario": "Review of key executive and AI context sources.", "inputs": [ { "label": "Key sources", "value": "35" }, { "label": "Updated within required window", "value": "22" } ], "calculation": "22 / 35 * 100", "result": "62.9%", "interpretation": "Information is not fresh enough for reliable decisions in several domains.", "recommendedAction": "Set refresh owners and windows by domain risk." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Using one freshness window for every domain", "Counting touched files as refreshed" ], "recommendedActions": [ "Define freshness by risk", "Assign refresh owners", "Retire stale sources" ], "relatedUseCases": [ "ai-adoption-readiness", "ma-integration" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "freshness", "audit cadence", "information ecology" ] } }, { "id": "lpm:metric:information-trust-score", "type": "metric", "slug": "information-trust-score", "title": "Information trust score", "canonicalUrl": "https://largepeoplemodel.com/metrics/information-trust-score", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:information-ecology" } ], "content": { "layer": "information-ecology", "supportStatus": "platform-backed-supported", "definition": "A composite reliability rating per knowledge domain.", "whatItReveals": "Whether information is owned, fresh, governed, versioned, and trusted enough to support decisions.", "whyItMatters": "This metric helps leaders see whether the information layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Score each critical information domain across ownership, freshness, consistency, access, and decision relevance.", "diagnosticQuestions": [ "Where is information trust score already visible in the operating model?", "Which owner can improve this information signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "ai-readiness", "maturity-scoring" ], "measurement": { "formula": { "label": "Information trust composite", "formula": "weighted score across owner clarity, freshness, lineage, conflict control, and access governance", "unit": "score", "direction": "higher-is-better" }, "requiredInputs": [ "Owner clarity score", "Freshness score", "Lineage score", "Conflict score", "Access governance score" ], "dataSources": [ "Source-of-Truth Map", "Data catalog", "Access reviews", "Lineage checks" ], "collectionCadence": "Quarterly; monthly for AI-critical information.", "workedExample": { "scenario": "Composite review of operating information quality.", "inputs": [ { "label": "Owner clarity", "value": "80" }, { "label": "Freshness", "value": "63" }, { "label": "Lineage", "value": "58" }, { "label": "Conflict control", "value": "70" }, { "label": "Access governance", "value": "75" } ], "calculation": "Average of 80, 63, 58, 70, and 75", "result": "69.2", "interpretation": "Information trust is below the level needed for confident AI scale.", "recommendedAction": "Improve lineage and freshness first because they are the weakest contributors." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100", "meaning": "Strong operating foundation." }, { "label": "Watch", "range": "70-84", "meaning": "Adequate with visible improvement areas." }, { "label": "Risk", "range": "50-69", "meaning": "Weak foundation; inspect the lowest layer contributors." }, { "label": "Critical", "range": "<50", "meaning": "Material execution or AI-readiness risk." } ], "commonPitfalls": [ "Averaging scores without reviewing the weakest factor", "Treating trust as sentiment" ], "recommendedActions": [ "Fix lowest contributors", "Publish source owners", "Review AI-critical domains" ], "relatedUseCases": [ "ai-adoption-readiness", "platform-governance" ] }, "traceabilityNote": "Supported by current model", "tags": [ "trust score", "quality", "AI readiness" ] } }, { "id": "lpm:metric:lineage-completeness", "type": "metric", "slug": "lineage-completeness", "title": "Lineage completeness", "canonicalUrl": "https://largepeoplemodel.com/metrics/lineage-completeness", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:information-ecology" } ], "content": { "layer": "information-ecology", "supportStatus": "platform-backed-supported", "definition": "The share of governed outputs whose information chain traces to an owned, governed source.", "whatItReveals": "Whether decisions and outputs can be traced back to reliable evidence.", "whyItMatters": "This metric helps leaders see whether the information layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Select key outputs and trace their upstream information sources, owners, and governance status.", "diagnosticQuestions": [ "Where is lineage completeness already visible in the operating model?", "Which owner can improve this information signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "governance-review", "ai-readiness", "lapemo-signal" ], "measurement": { "formula": { "label": "Lineage completeness", "formula": "(outputs with traceable upstream source, owner, and timestamp / total outputs sampled) * 100", "numerator": "Outputs with complete lineage", "denominator": "Outputs sampled", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Output sample", "Upstream source", "Source owner", "Timestamp", "Transformation path" ], "dataSources": [ "Data lineage tool", "Dashboard inventory", "Knowledge object records", "Audit logs" ], "collectionCadence": "Quarterly; before high-stakes AI deployment.", "workedExample": { "scenario": "Executive dashboard metric lineage review.", "inputs": [ { "label": "Dashboard metrics sampled", "value": "40" }, { "label": "Metrics with complete lineage", "value": "24" } ], "calculation": "24 / 40 * 100", "result": "60%", "interpretation": "Important outputs cannot reliably be traced back to evidence.", "recommendedAction": "Prioritize lineage for metrics used in decisions and AI workflows." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Confusing source links with full lineage", "Not checking owner and timestamp" ], "recommendedActions": [ "Document upstream sources", "Assign owners", "Add timestamps to critical outputs" ], "relatedUseCases": [ "ai-adoption-readiness", "ma-integration" ] }, "traceabilityNote": "Supported by current model", "tags": [ "lineage", "evidence", "traceability" ] } }, { "id": "lpm:metric:information-conflict-rate", "type": "metric", "slug": "information-conflict-rate", "title": "Information conflict rate", "canonicalUrl": "https://largepeoplemodel.com/metrics/information-conflict-rate", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:information-ecology" } ], "content": { "layer": "information-ecology", "supportStatus": "definition-needed", "definition": "How much a domain carries known conflicting or contested information.", "whatItReveals": "Where leaders may be making decisions from conflicting versions of reality.", "whyItMatters": "This metric helps leaders see whether the information layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review critical domains for conflicting reports, definitions, dashboards, or interpretations.", "diagnosticQuestions": [ "Where is information conflict rate already visible in the operating model?", "Which owner can improve this information signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "operating-review", "manual-artifact" ], "measurement": { "formula": { "label": "Information conflict rate", "formula": "(domains with known conflicting definitions or reports / domains reviewed) * 100", "numerator": "Domains with conflicting definitions, reports, or interpretations", "denominator": "Domains reviewed", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Domain list", "Conflicting sources", "Definition owners", "Dispute status" ], "dataSources": [ "Source-of-Truth Map", "Dashboard inventory", "Glossary", "Leadership interviews" ], "collectionCadence": "Quarterly and during integration or reporting redesign.", "workedExample": { "scenario": "Review of reporting domains after integration.", "inputs": [ { "label": "Domains reviewed", "value": "25" }, { "label": "Domains with conflicts", "value": "6" } ], "calculation": "6 / 25 * 100", "result": "24%", "interpretation": "Leaders are likely debating facts instead of decisions.", "recommendedAction": "Resolve definitions and assign authoritative source owners." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Ignoring semantic conflicts", "Counting unresolved debates as normal variance" ], "recommendedActions": [ "Resolve definitions", "Name source of truth", "Retire conflicting dashboards" ], "relatedUseCases": [ "ma-integration", "platform-governance" ] }, "traceabilityNote": "Needs definition decision", "tags": [ "conflict", "definition needed", "information quality" ] } }, { "id": "lpm:metric:platform-ownership-clarity", "type": "metric", "slug": "platform-ownership-clarity", "title": "Platform ownership clarity", "canonicalUrl": "https://largepeoplemodel.com/metrics/platform-ownership-clarity", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:platform-structure" } ], "content": { "layer": "platform-structure", "supportStatus": "platform-backed-live", "definition": "The share of tools and platforms with a single named owner.", "whatItReveals": "Whether critical systems have clear accountability.", "whyItMatters": "This metric helps leaders see whether the platforms layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Inventory key tools and platforms. Confirm whether each has a named owner with decision authority.", "diagnosticQuestions": [ "Where is platform ownership clarity already visible in the operating model?", "Which owner can improve this platforms signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "platform-rationalization", "operating-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Platform owner coverage", "formula": "(tools and platforms with one named accountable owner / total tools and platforms) * 100", "numerator": "Tools and platforms with one named owner", "denominator": "Total tools and platforms inventoried", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Tool inventory", "Platform owner", "Boundary owner", "Ownership status" ], "dataSources": [ "Tool inventory", "CMDB", "Platform map", "Ownership Map" ], "collectionCadence": "Quarterly; before rationalization or AI-enablement programs.", "workedExample": { "scenario": "Enterprise platform ownership inventory.", "inputs": [ { "label": "Tools inventoried", "value": "112" }, { "label": "Tools with one named owner", "value": "79" } ], "calculation": "79 / 112 * 100", "result": "70.5%", "interpretation": "Critical systems may lack clear accountability.", "recommendedAction": "Assign owners to tools used in critical workflows first." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting technical admins as accountable owners", "Ignoring SaaS tools outside IT" ], "recommendedActions": [ "Assign platform owners", "Rationalize ownerless tools", "Review shadow systems" ], "relatedUseCases": [ "platform-governance", "product-operating-model" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "platform ownership", "tool inventory", "systems" ] } }, { "id": "lpm:metric:integration-coverage", "type": "metric", "slug": "integration-coverage", "title": "Integration coverage", "canonicalUrl": "https://largepeoplemodel.com/metrics/integration-coverage", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:platform-structure" } ], "content": { "layer": "platform-structure", "supportStatus": "platform-backed-live", "definition": "The share of tools mapped into a governed boundary and integrated rather than manual.", "whatItReveals": "Whether work moves through governed integrations or relies on manual handoffs.", "whyItMatters": "This metric helps leaders see whether the platforms layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Map tools involved in critical workflows and identify which connections are integrated, manual, or unknown.", "diagnosticQuestions": [ "Where is integration coverage already visible in the operating model?", "Which owner can improve this platforms signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "platform-rationalization", "operating-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Governed integration coverage", "formula": "(tool connections integrated and governed / total required tool connections) * 100", "numerator": "Required connections that are integrated and governed", "denominator": "Total required tool connections", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Workflow map", "Required connections", "Integration status", "Governance status" ], "dataSources": [ "Platform map", "Architecture inventory", "Workflow map", "Integration catalog" ], "collectionCadence": "Quarterly and before platform rationalization decisions.", "workedExample": { "scenario": "Workflow integration review across customer operations.", "inputs": [ { "label": "Required connections", "value": "86" }, { "label": "Integrated and governed", "value": "51" } ], "calculation": "51 / 86 * 100", "result": "59.3%", "interpretation": "Work likely depends on manual bridges and hidden effort.", "recommendedAction": "Prioritize integrations for workflows with high manual handoff rates." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting file exports as governed integrations", "Ignoring integration ownership" ], "recommendedActions": [ "Prioritize critical integrations", "Assign integration owners", "Retire duplicate handoffs" ], "relatedUseCases": [ "platform-governance", "product-operating-model" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "integration", "manual handoffs", "platform coverage" ] } }, { "id": "lpm:metric:manual-handoff-rate", "type": "metric", "slug": "manual-handoff-rate", "title": "Manual handoff rate", "canonicalUrl": "https://largepeoplemodel.com/metrics/manual-handoff-rate", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:platform-structure" } ], "content": { "layer": "platform-structure", "supportStatus": "platform-backed-supported", "definition": "The share of tool-to-tool connections that are manual rather than integrated.", "whatItReveals": "Where work slows, loses context, or becomes error-prone because humans bridge system gaps.", "whyItMatters": "This metric helps leaders see whether the platforms layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review workflows and count where people copy, re-enter, reconcile, or manually move data between tools.", "diagnosticQuestions": [ "Where is manual handoff rate already visible in the operating model?", "Which owner can improve this platforms signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "platform-rationalization", "operating-review" ], "measurement": { "formula": { "label": "Manual handoff rate", "formula": "(manual handoffs / total workflow handoffs) * 100", "numerator": "Manual handoffs", "denominator": "Total handoffs in workflow", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Workflow handoff map", "Handoff type", "System boundary", "Owner" ], "dataSources": [ "Workflow map", "Platform map", "Process mining sample", "Team interviews" ], "collectionCadence": "Monthly for critical workflows.", "workedExample": { "scenario": "Claims workflow handoff review.", "inputs": [ { "label": "Total handoffs", "value": "46" }, { "label": "Manual handoffs", "value": "19" } ], "calculation": "19 / 46 * 100", "result": "41.3%", "interpretation": "The workflow is heavily dependent on people bridging system gaps.", "recommendedAction": "Automate or redesign the highest-volume manual handoffs." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Ignoring manual reconciliation", "Counting every approval as a manual handoff" ], "recommendedActions": [ "Automate high-volume handoffs", "Clarify handoff owners", "Reduce duplicate entry" ], "relatedUseCases": [ "platform-governance", "product-operating-model" ] }, "traceabilityNote": "Supported by current model", "tags": [ "manual handoff", "workflow", "platform gaps" ] } }, { "id": "lpm:metric:workflow-fragmentation-score", "type": "metric", "slug": "workflow-fragmentation-score", "title": "Workflow fragmentation score", "canonicalUrl": "https://largepeoplemodel.com/metrics/workflow-fragmentation-score", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:platform-structure" } ], "content": { "layer": "platform-structure", "supportStatus": "platform-backed-live", "definition": "How far a workflow is split across misaligned platform boundaries.", "whatItReveals": "Whether workflows are structurally aligned to how the organization needs to operate.", "whyItMatters": "This metric helps leaders see whether the platforms layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Map a critical workflow across teams, tools, boundaries, and handoffs. Identify fragmentation points.", "diagnosticQuestions": [ "Where is workflow fragmentation score already visible in the operating model?", "Which owner can improve this platforms signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "platform-rationalization", "maturity-scoring", "lapemo-signal" ], "measurement": { "formula": { "label": "Workflow fragmentation index", "formula": "weighted score across tools, handoffs, ownership breaks, boundary crossings, and duplicate entry", "unit": "score", "direction": "lower-is-better" }, "requiredInputs": [ "Workflow map", "Tool count", "Handoff count", "Ownership breaks", "Duplicate entry points" ], "dataSources": [ "Workflow map", "Platform inventory", "Ownership Map", "Process interviews" ], "collectionCadence": "Quarterly for strategic workflows.", "workedExample": { "scenario": "Product launch workflow review.", "inputs": [ { "label": "Tools", "value": "9" }, { "label": "Handoffs", "value": "18" }, { "label": "Ownership breaks", "value": "5" }, { "label": "Duplicate entries", "value": "4" } ], "calculation": "Weighted risk index from mapped fragmentation factors", "result": "78 / 100 risk", "interpretation": "The workflow is structurally misaligned and likely to lose context.", "recommendedAction": "Redesign ownership and platform boundaries before adding more tools." }, "interpretationBands": [ { "label": "Healthy", "range": "0-25", "meaning": "Risk is low or controlled." }, { "label": "Watch", "range": "26-50", "meaning": "Risk should be reviewed before scale." }, { "label": "Risk", "range": "51-75", "meaning": "Risk is likely to affect outcomes." }, { "label": "Critical", "range": "76-100", "meaning": "Reduce autonomy, scope, or exposure before proceeding." } ], "commonPitfalls": [ "Treating tool count alone as fragmentation", "Ignoring ownership breaks" ], "recommendedActions": [ "Simplify workflow boundaries", "Reduce handoffs", "Assign owners for breaks" ], "relatedUseCases": [ "platform-governance", "product-operating-model" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "workflow fragmentation", "platform boundaries", "tool sprawl" ] } }, { "id": "lpm:metric:tool-duplication-count", "type": "metric", "slug": "tool-duplication-count", "title": "Tool duplication count", "canonicalUrl": "https://largepeoplemodel.com/metrics/tool-duplication-count", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:platform-structure" } ], "content": { "layer": "platform-structure", "supportStatus": "definition-needed", "definition": "The number of overlapping tools serving the same function.", "whatItReveals": "Where the organization has redundant capabilities, duplicated work, or unclear platform strategy.", "whyItMatters": "This metric helps leaders see whether the platforms layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Group tools by function, team, domain, and workflow. Identify overlaps that create cost or confusion.", "diagnosticQuestions": [ "Where is tool duplication count already visible in the operating model?", "Which owner can improve this platforms signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "platform-rationalization", "manual-artifact" ], "measurement": { "formula": { "label": "Duplicate tool count", "formula": "count(tools serving overlapping function in the same domain or workflow)", "unit": "count", "direction": "lower-is-better" }, "requiredInputs": [ "Tool inventory", "Function category", "Domain", "Workflow", "Owner" ], "dataSources": [ "Tool inventory", "Spend data", "Platform map", "Team surveys" ], "collectionCadence": "Quarterly and before renewal cycles.", "workedExample": { "scenario": "Sales operations tool rationalization.", "inputs": [ { "label": "Overlapping reporting tools", "value": "5" }, { "label": "Overlapping intake tools", "value": "3" } ], "calculation": "5 + 3", "result": "8 duplicate tools", "interpretation": "Tool overlap is likely creating cost, context fragmentation, and ownership confusion.", "recommendedAction": "Review duplicates by owner, cost, integration, and workflow dependency." }, "interpretationBands": [ { "label": "Healthy", "range": "Low and declining", "meaning": "Gaps are being removed on cadence." }, { "label": "Watch", "range": "Flat", "meaning": "The operating model is not improving." }, { "label": "Risk", "range": "Rising", "meaning": "Debt is accumulating faster than teams are resolving it." }, { "label": "Critical", "range": "Concentrated in critical work", "meaning": "Escalate for owner action." } ], "commonPitfalls": [ "Calling all overlap bad without checking use case", "Ignoring local regulatory needs" ], "recommendedActions": [ "Group by function", "Rationalize renewals", "Assign platform direction" ], "relatedUseCases": [ "platform-governance", "ma-integration" ] }, "traceabilityNote": "Needs definition decision", "tags": [ "tool duplication", "definition needed", "platform strategy" ] } }, { "id": "lpm:metric:control-coverage", "type": "metric", "slug": "control-coverage", "title": "Control coverage", "canonicalUrl": "https://largepeoplemodel.com/metrics/control-coverage", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" } ], "content": { "layer": "governance-architecture", "supportStatus": "platform-backed-live", "definition": "The share of governed decision types and AI-enabled areas covered by an active control.", "whatItReveals": "Whether the organization has sufficient controls around the areas that need governance.", "whyItMatters": "This metric helps leaders see whether the governance layer is healthy enough to support execution and AI readiness.", "manualAssessment": "List governed decisions, AI-enabled workflows, and sensitive domains. Confirm whether each has active controls.", "diagnosticQuestions": [ "Where is control coverage already visible in the operating model?", "Which owner can improve this governance signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "governance-review", "ai-readiness", "lapemo-signal" ], "measurement": { "formula": { "label": "Control coverage", "formula": "(governed areas with active controls / total governed areas requiring controls) * 100", "numerator": "Governed areas with active controls", "denominator": "Governed areas requiring controls", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Governed areas", "Active controls", "Control owner", "Risk tier" ], "dataSources": [ "Control map", "Risk register", "AI governance checklist", "Policy inventory" ], "collectionCadence": "Monthly for AI and regulated work; quarterly baseline otherwise.", "workedExample": { "scenario": "Review of AI-enabled and regulated workflow areas.", "inputs": [ { "label": "Governed areas", "value": "44" }, { "label": "Areas with active controls", "value": "31" } ], "calculation": "31 / 44 * 100", "result": "70.5%", "interpretation": "Governance coverage is not yet strong enough for broad scale.", "recommendedAction": "Add active controls to high-risk uncovered areas first." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting policies as controls without active operation", "Ignoring control owner" ], "recommendedActions": [ "Map controls to risk areas", "Assign control owners", "Review uncovered AI workflows" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance", "platform-governance" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "controls", "governance", "AI readiness" ] } }, { "id": "lpm:metric:policy-exception-rate", "type": "metric", "slug": "policy-exception-rate", "title": "Policy exception rate", "canonicalUrl": "https://largepeoplemodel.com/metrics/policy-exception-rate", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" } ], "content": { "layer": "governance-architecture", "supportStatus": "platform-backed-supported", "definition": "How often controls are bypassed or risks formally accepted.", "whatItReveals": "Whether governance is being followed, bypassed, or used to document accepted risk.", "whyItMatters": "This metric helps leaders see whether the governance layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review exceptions, bypasses, and accepted risks over a period. Compare them to total governance reviews.", "diagnosticQuestions": [ "Where is policy exception rate already visible in the operating model?", "Which owner can improve this governance signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "governance-review", "operating-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Exception rate", "formula": "(exceptions or bypasses / total governance reviews or controlled actions) * 100", "numerator": "Exceptions, bypasses, or accepted variances", "denominator": "Governance reviews or controlled actions", "unit": "percent", "direction": "lower-is-better" }, "requiredInputs": [ "Exception log", "Bypass reason", "Review count", "Risk tier" ], "dataSources": [ "Governance workflow", "Risk register", "Audit records", "Control map" ], "collectionCadence": "Monthly in governance review.", "workedExample": { "scenario": "Control review across policy-governed requests.", "inputs": [ { "label": "Exceptions", "value": "18" }, { "label": "Reviews", "value": "120" } ], "calculation": "18 / 120 * 100", "result": "15%", "interpretation": "Exceptions are common enough to inspect policy fit and control design.", "recommendedAction": "Categorize exceptions by policy friction, risk acceptance, and owner behavior." }, "interpretationBands": [ { "label": "Healthy", "range": "0-10%", "meaning": "Low and manageable." }, { "label": "Watch", "range": "11-20%", "meaning": "Rising signal that should be reviewed." }, { "label": "Risk", "range": "21-35%", "meaning": "High enough to indicate structural friction." }, { "label": "Critical", "range": ">35%", "meaning": "Likely blocking execution or governance quality." } ], "commonPitfalls": [ "Treating all exceptions as failures", "Not separating approved risk from bypass" ], "recommendedActions": [ "Review repeated exception reasons", "Update unrealistic policies", "Escalate risky bypasses" ], "relatedUseCases": [ "platform-governance", "ai-adoption-readiness" ] }, "traceabilityNote": "Supported by current model", "tags": [ "exceptions", "accepted risk", "controls" ] } }, { "id": "lpm:metric:approval-cycle-time", "type": "metric", "slug": "approval-cycle-time", "title": "Approval cycle time", "canonicalUrl": "https://largepeoplemodel.com/metrics/approval-cycle-time", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" } ], "content": { "layer": "governance-architecture", "supportStatus": "platform-backed-supported", "definition": "The time taken to move an item through required approval.", "whatItReveals": "Whether governance is creating necessary control or unnecessary delay.", "whyItMatters": "This metric helps leaders see whether the governance layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Track when approvals are requested, reviewed, decided, and communicated.", "diagnosticQuestions": [ "Where is approval cycle time already visible in the operating model?", "Which owner can improve this governance signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "governance-review", "operating-review" ], "measurement": { "formula": { "label": "Approval cycle time", "formula": "average(approval decision timestamp - approval request timestamp), with median and p90", "unit": "days", "direction": "lower-is-better" }, "requiredInputs": [ "Approval request timestamp", "Approval decision timestamp", "Approver", "Risk tier" ], "dataSources": [ "Governance workflow", "Decision log", "Ticketing system" ], "collectionCadence": "Monthly by approval type.", "workedExample": { "scenario": "Governance approval review.", "inputs": [ { "label": "Approvals sampled", "value": "45" }, { "label": "Average cycle time", "value": "6.8 days" }, { "label": "p90 cycle time", "value": "18 days" } ], "calculation": "Average and p90 elapsed time from request to decision", "result": "6.8 days average; 18 days p90", "interpretation": "A small number of approvals may be creating the lived governance drag.", "recommendedAction": "Inspect p90 approvals and redesign bottleneck paths." }, "interpretationBands": [ { "label": "Healthy", "range": "Within approval SLA", "meaning": "Approvals move as designed." }, { "label": "Watch", "range": "1-25% over SLA", "meaning": "Review repeat delays." }, { "label": "Risk", "range": "26-75% over SLA", "meaning": "Governance is slowing work." }, { "label": "Critical", "range": ">75% over SLA", "meaning": "Approval design requires executive attention." } ], "commonPitfalls": [ "Only reporting average", "Not segmenting by risk tier" ], "recommendedActions": [ "Review p90 delays", "Clarify approver authority", "Create risk-tiered paths" ], "relatedUseCases": [ "platform-governance", "transformation-operating-model" ] }, "traceabilityNote": "Supported by current model", "tags": [ "approval", "cycle time", "governance drag" ] } }, { "id": "lpm:metric:governance-bottleneck-count", "type": "metric", "slug": "governance-bottleneck-count", "title": "Governance bottleneck count", "canonicalUrl": "https://largepeoplemodel.com/metrics/governance-bottleneck-count", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" } ], "content": { "layer": "governance-architecture", "supportStatus": "platform-backed-supported", "definition": "Controls overdue for review, or concentrated on a single approver, that create a choke point.", "whatItReveals": "Where governance capacity or design is slowing execution.", "whyItMatters": "This metric helps leaders see whether the governance layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Identify overdue controls, overloaded approvers, repeated approval queues, and review bottlenecks.", "diagnosticQuestions": [ "Where is governance bottleneck count already visible in the operating model?", "Which owner can improve this governance signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "governance-review", "operating-review" ], "measurement": { "formula": { "label": "Governance bottlenecks", "formula": "count(overdue controls + overloaded approvers + approval queues over threshold)", "unit": "count", "direction": "lower-is-better" }, "requiredInputs": [ "Overdue controls", "Approver workload", "Queue age", "Thresholds" ], "dataSources": [ "Control map", "Governance workflow", "Risk register", "Approval queue" ], "collectionCadence": "Monthly; weekly during major launches or AI rollout.", "workedExample": { "scenario": "Governance capacity review.", "inputs": [ { "label": "Overdue controls", "value": "7" }, { "label": "Overloaded approvers", "value": "3" }, { "label": "Queues over SLA", "value": "4" } ], "calculation": "7 + 3 + 4", "result": "14 bottlenecks", "interpretation": "Governance design or capacity is slowing execution.", "recommendedAction": "Redesign approval paths and distribute authority where appropriate." }, "interpretationBands": [ { "label": "Healthy", "range": "Low and declining", "meaning": "Gaps are being removed on cadence." }, { "label": "Watch", "range": "Flat", "meaning": "The operating model is not improving." }, { "label": "Risk", "range": "Rising", "meaning": "Debt is accumulating faster than teams are resolving it." }, { "label": "Critical", "range": "Concentrated in critical work", "meaning": "Escalate for owner action." } ], "commonPitfalls": [ "Chasing faster approvals without redesign", "Ignoring approver concentration" ], "recommendedActions": [ "Rebalance approvers", "Clarify thresholds", "Retire unnecessary controls" ], "relatedUseCases": [ "transformation-operating-model", "platform-governance" ] }, "traceabilityNote": "Supported by current model", "tags": [ "bottleneck", "overdue controls", "review capacity" ] } }, { "id": "lpm:metric:risk-acceptance-lineage", "type": "metric", "slug": "risk-acceptance-lineage", "title": "Risk acceptance lineage", "canonicalUrl": "https://largepeoplemodel.com/metrics/risk-acceptance-lineage", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" } ], "content": { "layer": "governance-architecture", "supportStatus": "platform-backed-supported", "definition": "Every accepted risk traced to who accepted it, when, and why.", "whatItReveals": "Whether risk acceptance is accountable and auditable.", "whyItMatters": "This metric helps leaders see whether the governance layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review accepted risks and confirm each has a named approver, rationale, timestamp, and affected area.", "diagnosticQuestions": [ "Where is risk acceptance lineage already visible in the operating model?", "Which owner can improve this governance signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "governance-review", "ai-readiness", "lapemo-signal" ], "measurement": { "formula": { "label": "Risk acceptance lineage completeness", "formula": "(accepted risks with approver, rationale, timestamp, affected area, and review date / total accepted risks) * 100", "numerator": "Accepted risks with complete lineage", "denominator": "Total accepted risks", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Accepted risk", "Approver", "Rationale", "Timestamp", "Affected area", "Review date" ], "dataSources": [ "Risk register", "Governance workflow", "Audit log", "Control map" ], "collectionCadence": "Monthly for active risk portfolio.", "workedExample": { "scenario": "Review of accepted AI and platform risks.", "inputs": [ { "label": "Accepted risks", "value": "22" }, { "label": "Risks with complete lineage", "value": "13" } ], "calculation": "13 / 22 * 100", "result": "59.1%", "interpretation": "Risk acceptance is not consistently accountable or auditable.", "recommendedAction": "Require complete lineage before risk acceptance is valid." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting risk notes without approval", "Missing review dates" ], "recommendedActions": [ "Complete missing lineage", "Assign risk review owners", "Escalate untraceable accepted risks" ], "relatedUseCases": [ "agent-workforce-governance", "ai-adoption-readiness" ] }, "traceabilityNote": "Supported by current model", "tags": [ "risk acceptance", "auditability", "lineage" ] } }, { "id": "lpm:metric:ai-use-case-owner-coverage", "type": "metric", "slug": "ai-use-case-owner-coverage", "title": "AI use case owner coverage", "canonicalUrl": "https://largepeoplemodel.com/metrics/ai-use-case-owner-coverage", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" } ], "content": { "layer": "ai-amplification", "supportStatus": "platform-backed-live", "definition": "The share of AI tools and agents with a single named human owner.", "whatItReveals": "Whether AI-supported work has clear human accountability.", "whyItMatters": "This metric helps leaders see whether the ai amplification layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Inventory AI use cases, copilots, tools, and agents. Confirm each has a named owner or supervisor.", "diagnosticQuestions": [ "Where is ai use case owner coverage already visible in the operating model?", "Which owner can improve this ai amplification signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "ai-readiness", "governance-review", "lapemo-signal" ], "measurement": { "formula": { "label": "AI owner coverage", "formula": "(AI use cases, tools, or agents with one named human owner / total AI use cases, tools, or agents) * 100", "numerator": "AI items with one named human owner", "denominator": "Total AI items inventoried", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "AI use case inventory", "Human owner", "Supervisor", "Autonomy level" ], "dataSources": [ "AI readiness diagnostic", "Agent accountability checklist", "AI inventory" ], "collectionCadence": "Monthly during AI rollout.", "workedExample": { "scenario": "AI use case inventory review.", "inputs": [ { "label": "AI use cases", "value": "25" }, { "label": "Use cases with named human owner", "value": "18" } ], "calculation": "18 / 25 * 100", "result": "72%", "interpretation": "Too many AI use cases lack clear human accountability.", "recommendedAction": "Assign owners before expanding use cases or autonomy." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting vendor or tool as owner", "Ignoring supervisor role for agents" ], "recommendedActions": [ "Name human owners", "Define supervisors", "Block ownerless AI scale" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "AI ownership", "agents", "accountability" ] } }, { "id": "lpm:metric:human-in-the-loop-clarity", "type": "metric", "slug": "human-in-the-loop-clarity", "title": "Human-in-the-loop clarity", "canonicalUrl": "https://largepeoplemodel.com/metrics/human-in-the-loop-clarity", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" } ], "content": { "layer": "ai-amplification", "supportStatus": "platform-backed-supported", "definition": "The share of AI-touched decisions with an explicit autonomy level and defined oversight.", "whatItReveals": "Whether humans know when AI supports, recommends, acts, or requires approval.", "whyItMatters": "This metric helps leaders see whether the ai amplification layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Classify AI-touched decisions by autonomy level and identify oversight requirements.", "diagnosticQuestions": [ "Where is human-in-the-loop clarity already visible in the operating model?", "Which owner can improve this ai amplification signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "ai-readiness", "governance-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Oversight clarity coverage", "formula": "(AI-touched decisions with autonomy level and oversight defined / total AI-touched decisions) * 100", "numerator": "AI-touched decisions with explicit autonomy and oversight", "denominator": "Total AI-touched decisions", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "AI-touched decision list", "Autonomy level", "Oversight owner", "Approval rule" ], "dataSources": [ "AI governance checklist", "Decision Rights Matrix", "Agent accountability checklist" ], "collectionCadence": "Monthly for AI programs.", "workedExample": { "scenario": "AI decision oversight review.", "inputs": [ { "label": "AI-touched decisions", "value": "31" }, { "label": "Decisions with explicit oversight", "value": "14" } ], "calculation": "14 / 31 * 100", "result": "45.2%", "interpretation": "Humans may not know when to review, approve, or intervene.", "recommendedAction": "Define oversight and autonomy levels before increasing AI scope." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Saying human-in-the-loop without naming the loop", "Ignoring autonomy levels" ], "recommendedActions": [ "Define autonomy tiers", "Assign oversight owners", "Add approval gates" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance" ] }, "traceabilityNote": "Supported by current model", "tags": [ "human in the loop", "autonomy", "oversight" ] } }, { "id": "lpm:metric:agent-action-auditability", "type": "metric", "slug": "agent-action-auditability", "title": "Agent action auditability", "canonicalUrl": "https://largepeoplemodel.com/metrics/agent-action-auditability", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" } ], "content": { "layer": "ai-amplification", "supportStatus": "platform-backed-supported", "definition": "The share of agent actions backed by a complete, immutable audit record.", "whatItReveals": "Whether governed agent activity can be reviewed, audited, and trusted.", "whyItMatters": "This metric helps leaders see whether the ai amplification layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review whether agent actions are logged with actor, action, context, decision, approval, and outcome.", "diagnosticQuestions": [ "Where is agent action auditability already visible in the operating model?", "Which owner can improve this ai amplification signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "ai-readiness", "governance-review", "lapemo-signal" ], "measurement": { "formula": { "label": "Agent action auditability", "formula": "(agent actions with complete audit record / total agent actions sampled) * 100", "numerator": "Agent actions with actor, action, context, approval, and outcome", "denominator": "Total agent actions sampled", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Agent action logs", "Actor", "Action", "Context", "Approval", "Outcome" ], "dataSources": [ "Agent logs", "Audit logs", "Governance workflow", "Agent accountability checklist" ], "collectionCadence": "Continuous for governed agents; monthly sample review.", "workedExample": { "scenario": "Audit sample of governed agent actions.", "inputs": [ { "label": "Agent actions sampled", "value": "1,200" }, { "label": "Actions with complete audit record", "value": "1,050" } ], "calculation": "1,050 / 1,200 * 100", "result": "87.5%", "interpretation": "Auditability is strong but not complete enough for higher-risk autonomy.", "recommendedAction": "Close logging gaps before increasing agent permissions." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100%", "meaning": "Strong enough for normal operating review." }, { "label": "Watch", "range": "70-84%", "meaning": "Usable, but gaps should be assigned owners." }, { "label": "Risk", "range": "50-69%", "meaning": "Weak enough to slow execution or create risk." }, { "label": "Critical", "range": "<50%", "meaning": "Do not scale the related workflow without intervention." } ], "commonPitfalls": [ "Counting logs without approval or outcome", "Sampling only successful actions" ], "recommendedActions": [ "Complete audit schema", "Review failed actions", "Block unaudited high-risk actions" ], "relatedUseCases": [ "agent-workforce-governance", "ai-adoption-readiness" ] }, "traceabilityNote": "Supported by current model", "tags": [ "agents", "auditability", "governance" ] } }, { "id": "lpm:metric:ai-adoption-readiness-score", "type": "metric", "slug": "ai-adoption-readiness-score", "title": "AI adoption readiness score", "canonicalUrl": "https://largepeoplemodel.com/metrics/ai-adoption-readiness-score", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" } ], "content": { "layer": "ai-amplification", "supportStatus": "platform-backed-live", "definition": "A single headline score for how ready the organization is to safely scale AI.", "whatItReveals": "Whether the foundation layers are mature enough to support responsible AI adoption.", "whyItMatters": "This metric helps leaders see whether the ai amplification layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Assess ownership, decisions, information, platforms, governance, and AI oversight together instead of treating AI readiness as a purely technical question.", "diagnosticQuestions": [ "Where is ai adoption readiness score already visible in the operating model?", "Which owner can improve this ai amplification signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "ai-readiness", "maturity-scoring", "lapemo-signal" ], "measurement": { "formula": { "label": "AI readiness composite", "formula": "weighted composite of ownership, decision, information, platform, governance, and AI oversight readiness", "unit": "score", "direction": "higher-is-better" }, "requiredInputs": [ "Ownership score", "Decision score", "Information score", "Platform score", "Governance score", "AI oversight score" ], "dataSources": [ "AI readiness diagnostic", "LPM diagnostic", "Knowledge Objects", "Operating review" ], "collectionCadence": "Before AI scale decisions and quarterly during rollout.", "workedExample": { "scenario": "AI scale-readiness review.", "inputs": [ { "label": "Ownership", "value": "73" }, { "label": "Decision", "value": "66" }, { "label": "Information", "value": "69" }, { "label": "Platform", "value": "62" }, { "label": "Governance", "value": "71" }, { "label": "AI oversight", "value": "58" } ], "calculation": "Average of six readiness contributors", "result": "66.5", "interpretation": "AI readiness is risky, with AI oversight and platform structure as the weakest contributors.", "recommendedAction": "Improve the weakest layer contributors before scaling autonomy." }, "interpretationBands": [ { "label": "Healthy", "range": "85-100", "meaning": "Strong operating foundation." }, { "label": "Watch", "range": "70-84", "meaning": "Adequate with visible improvement areas." }, { "label": "Risk", "range": "50-69", "meaning": "Weak foundation; inspect the lowest layer contributors." }, { "label": "Critical", "range": "<50", "meaning": "Material execution or AI-readiness risk." } ], "commonPitfalls": [ "Treating readiness as an AI-tool assessment only", "Ignoring layer caps" ], "recommendedActions": [ "Improve weakest layer", "Set scale gates", "Reassess after remediation" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance" ] }, "traceabilityNote": "Backed by current platform data", "tags": [ "AI readiness", "maturity", "readiness score" ] } }, { "id": "lpm:metric:ai-amplification-risk", "type": "metric", "slug": "ai-amplification-risk", "title": "AI amplification risk", "canonicalUrl": "https://largepeoplemodel.com/metrics/ai-amplification-risk", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" } ], "content": { "layer": "ai-amplification", "supportStatus": "platform-backed-supported", "definition": "The risk that AI is being scaled faster than the human structure can govern.", "whatItReveals": "Whether AI is likely to amplify clarity or chaos.", "whyItMatters": "This metric helps leaders see whether the ai amplification layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Compare AI deployment speed and autonomy against the weakest foundation layer in the operating model.", "diagnosticQuestions": [ "Where is ai amplification risk already visible in the operating model?", "Which owner can improve this ai amplification signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "ai-readiness", "governance-review", "lapemo-signal" ], "measurement": { "formula": { "label": "AI amplification risk gap", "formula": "AI scale pressure - operating model readiness", "unit": "score", "direction": "lower-is-better" }, "requiredInputs": [ "AI scale pressure", "Operating model readiness", "Autonomy level", "Control coverage" ], "dataSources": [ "AI readiness diagnostic", "Governance review", "AI inventory", "Operating model review" ], "collectionCadence": "Monthly during AI scale programs.", "workedExample": { "scenario": "Enterprise AI rollout risk review.", "inputs": [ { "label": "AI scale pressure", "value": "82" }, { "label": "Operating model readiness", "value": "66" } ], "calculation": "82 - 66", "result": "16 point risk gap", "interpretation": "AI is scaling faster than the operating model can govern.", "recommendedAction": "Slow expansion or improve governance, ownership, and information trust." }, "interpretationBands": [ { "label": "Healthy", "range": "0-25", "meaning": "Risk is low or controlled." }, { "label": "Watch", "range": "26-50", "meaning": "Risk should be reviewed before scale." }, { "label": "Risk", "range": "51-75", "meaning": "Risk is likely to affect outcomes." }, { "label": "Critical", "range": "76-100", "meaning": "Reduce autonomy, scope, or exposure before proceeding." } ], "commonPitfalls": [ "Measuring AI enthusiasm instead of scale pressure", "Ignoring foundation-layer weakness" ], "recommendedActions": [ "Reduce autonomy", "Strengthen controls", "Improve readiness before scale" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance" ] }, "traceabilityNote": "Supported by current model", "tags": [ "AI amplification", "risk", "governance" ] } }, { "id": "lpm:metric:ai-decision-risk-score", "type": "metric", "slug": "ai-decision-risk-score", "title": "AI decision risk score", "canonicalUrl": "https://largepeoplemodel.com/metrics/ai-decision-risk-score", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "measures", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" } ], "content": { "layer": "ai-amplification", "supportStatus": "definition-needed", "definition": "A risk rating of a decision type being AI-assisted.", "whatItReveals": "Where AI involvement in high-stakes, low-reversibility, or low-confidence decisions requires stronger oversight.", "whyItMatters": "This metric helps leaders see whether the ai amplification layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review AI-assisted decision types by stakes, reversibility, model confidence, human review, and governance tier.", "diagnosticQuestions": [ "Where is ai decision risk score already visible in the operating model?", "Which owner can improve this ai amplification signal?", "What evidence would make the metric trustworthy enough for an operating review?" ], "useTypes": [ "diagnostic", "ai-readiness", "governance-review" ], "measurement": { "formula": { "label": "AI decision risk score", "formula": "weighted score across stakes, reversibility, model confidence, data trust, human oversight, and governance tier", "unit": "score", "direction": "lower-is-better" }, "requiredInputs": [ "Decision stakes", "Reversibility", "Model confidence", "Information trust", "Oversight level", "Governance tier" ], "dataSources": [ "AI governance checklist", "Decision record", "Risk register", "Model evaluation notes" ], "collectionCadence": "For every high-impact AI-assisted decision type.", "workedExample": { "scenario": "AI-assisted eligibility decision review.", "inputs": [ { "label": "High stakes", "value": "30" }, { "label": "Low reversibility", "value": "20" }, { "label": "Confidence gap", "value": "15" }, { "label": "Weak oversight", "value": "20" }, { "label": "Weak lineage", "value": "10" } ], "calculation": "30 + 20 + 15 + 20 + 10", "result": "95 / 100 risk", "interpretation": "This decision should not be automated without stronger gates and oversight.", "recommendedAction": "Require human approval and improve evidence lineage before deployment." }, "interpretationBands": [ { "label": "Healthy", "range": "0-25", "meaning": "Risk is low or controlled." }, { "label": "Watch", "range": "26-50", "meaning": "Risk should be reviewed before scale." }, { "label": "Risk", "range": "51-75", "meaning": "Risk is likely to affect outcomes." }, { "label": "Critical", "range": "76-100", "meaning": "Reduce autonomy, scope, or exposure before proceeding." } ], "commonPitfalls": [ "Letting model confidence overpower high stakes", "Ignoring reversibility" ], "recommendedActions": [ "Lower autonomy", "Add approval gates", "Improve evidence and lineage" ], "relatedUseCases": [ "ai-adoption-readiness", "agent-workforce-governance" ] }, "traceabilityNote": "Needs definition decision", "tags": [ "AI decision risk", "definition needed", "oversight" ] } }, { "id": "lpm:knowledge-object:agent-accountability-checklist", "type": "knowledge-object", "slug": "agent-accountability-checklist", "title": "Agent Accountability Checklist", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/agent-accountability-checklist", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "supports", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:human-in-the-loop-model" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-use-case-governance-register" } ], "content": { "kind": "checklist", "summary": "A checklist for assigning human ownership, escalation, evidence, and control paths to governed agents.", "purpose": "Help AI and risk leaders confirm that every agentic workflow has accountable owners, bounded permissions, human review, and audit-ready evidence.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Agent use case", "Business owner", "Permissions", "Human review path", "Control evidence" ], "outputs": [ "Agent accountability record", "Escalation gaps", "Control actions" ], "howToUse": [ "Name the agent and workflow boundary.", "Assign business, technical, governance, and human review owners.", "Score readiness and document unresolved risks." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Before launch and quarterly", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Agent-Accountability-Checklist-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Agent-Accountability-Checklist-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Agent-Accountability-Checklist-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-agent-accountability-checklist-skill-v1.json", "access": "public", "available": true } }, "tags": [ "agents", "accountability", "ai governance" ] } }, { "id": "lpm:knowledge-object:ai-governance-checklist", "type": "knowledge-object", "slug": "ai-governance-checklist", "title": "AI Governance Checklist", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/ai-governance-checklist", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "supports", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-use-case-governance-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:risk-acceptance-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:human-in-the-loop-model" } ], "content": { "kind": "checklist", "summary": "A reusable governance checklist for AI use cases, controls, approvals, oversight, and evidence.", "purpose": "Help leaders evaluate whether AI initiatives have the operating controls needed before scaling into production workflows.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "AI use case inventory", "Policy requirements", "Risk assessment", "Approval path", "Monitoring plan" ], "outputs": [ "Governance readiness score", "Approval gaps", "Control remediation actions" ], "howToUse": [ "Review the AI use case against each governance domain.", "Document evidence and unresolved gaps.", "Assign control owners and approval actions." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Before launch and every control cycle", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-AI-Governance-Checklist-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-AI-Governance-Checklist-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-AI-Governance-Checklist-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-ai-governance-checklist-skill-v1.json", "access": "public", "available": true } }, "tags": [ "ai", "governance", "controls" ] } }, { "id": "lpm:knowledge-object:ai-initiative-owner-register", "type": "knowledge-object", "slug": "ai-initiative-owner-register", "title": "AI Initiative Owner Register", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/ai-initiative-owner-register", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "supports", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ownership-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-use-case-governance-register" } ], "content": { "kind": "register", "summary": "A register for making AI initiative ownership visible across sponsors, operators, data, platforms, controls, and outcomes.", "purpose": "Prevent AI work from scaling without accountable business ownership and clear supporting owner roles.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "AI initiative list", "Sponsors", "Business outcomes", "Data sources", "Platform owners" ], "outputs": [ "Owner register", "Unassigned ownership gaps", "AI accountability coverage" ], "howToUse": [ "List active and planned AI initiatives.", "Name the owner roles required for each initiative.", "Escalate initiatives with missing or weak ownership." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Monthly during AI portfolio review", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-AI-Initiative-Owner-Register-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-AI-Initiative-Owner-Register-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-AI-Initiative-Owner-Register-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-ai-initiative-owner-register-skill-v1.json", "access": "public", "available": true } }, "tags": [ "ai portfolio", "ownership", "register" ] } }, { "id": "lpm:knowledge-object:ai-use-case-governance-register", "type": "knowledge-object", "slug": "ai-use-case-governance-register", "title": "AI Use Case Governance Register", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/ai-use-case-governance-register", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "supports", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:risk-acceptance-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:control-map" } ], "content": { "kind": "register", "summary": "A governance register for AI use cases, risk tiers, approvals, controls, evidence, and monitoring status.", "purpose": "Create a single operating record for AI use case governance across business, technology, risk, and compliance owners.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Use case description", "Risk tier", "Data classification", "Approvers", "Control evidence" ], "outputs": [ "AI governance register", "Approval status", "Risk and evidence log" ], "howToUse": [ "Capture each AI use case and its risk profile.", "Link owners, controls, and evidence.", "Review approval and monitoring status in governance forums." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Monthly and before production release", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-AI-Use-Case-Governance-Register-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-AI-Use-Case-Governance-Register-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-AI-Use-Case-Governance-Register-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-ai-use-case-governance-register-skill-v1.json", "access": "public", "available": true } }, "tags": [ "ai governance", "register", "risk" ] } }, { "id": "lpm:knowledge-object:channel-purpose-guide", "type": "knowledge-object", "slug": "channel-purpose-guide", "title": "Channel Purpose Guide", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/channel-purpose-guide", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:communication-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:communication-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:meeting-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-communication-protocol" } ], "content": { "kind": "guide", "summary": "A guide for defining what each communication channel is for, what belongs there, and what must move elsewhere.", "purpose": "Reduce communication overload by clarifying channel purpose, decision use, evidence expectations, and escalation norms.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Current channels", "Meeting patterns", "Decision records", "Team norms" ], "outputs": [ "Channel purpose model", "Communication rules", "Channel cleanup actions" ], "howToUse": [ "Inventory the channels teams use.", "Define purpose, owner, and appropriate content for each channel.", "Move decisions and durable knowledge into governed records." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or after tool changes", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Channel-Purpose-Guide-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Channel-Purpose-Guide-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Channel-Purpose-Guide-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-channel-purpose-guide-skill-v1.json", "access": "public", "available": true } }, "tags": [ "communication", "channels", "collaboration" ] } }, { "id": "lpm:knowledge-object:communication-map", "type": "knowledge-object", "slug": "communication-map", "title": "Communication Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/communication-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:communication-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:channel-purpose-guide" }, { "type": "related_to", "targetId": "lpm:knowledge-object:meeting-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:escalation-map" } ], "content": { "kind": "map", "summary": "A map of how information, decisions, updates, escalations, and commitments move across teams.", "purpose": "Expose communication paths that create delay, overload, duplicated updates, or missing decision evidence.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Stakeholders", "Meeting forums", "Channels", "Escalation paths", "Decision records" ], "outputs": [ "Communication flow map", "Overload risks", "Improvement actions" ], "howToUse": [ "Map the current communication flow for a workflow or initiative.", "Identify gaps between updates, decisions, and records.", "Redesign the forums and channels that carry critical work." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or during operating model redesign", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Communication-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Communication-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Communication-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-communication-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "communication", "mapping", "coordination" ] } }, { "id": "lpm:knowledge-object:control-map", "type": "knowledge-object", "slug": "control-map", "title": "Control Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/control-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:risk-acceptance-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:evidence-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" } ], "content": { "kind": "map", "summary": "A map of controls, owners, evidence sources, review cadence, and AI exposure across a workflow.", "purpose": "Help governance teams see whether controls are owned, evidenced, reviewed, and ready for AI-amplified work.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Control inventory", "Policy requirements", "Evidence sources", "Owners", "AI touchpoints" ], "outputs": [ "Control ownership map", "Evidence gaps", "Review actions" ], "howToUse": [ "Select a workflow or risk domain.", "Map each control to owner, evidence, cadence, and system.", "Flag controls affected by automation or AI decisions." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Each control cycle", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Control-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Control-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Control-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-control-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "controls", "governance", "evidence" ] } }, { "id": "lpm:knowledge-object:data-lineage-map", "type": "knowledge-object", "slug": "data-lineage-map", "title": "Data Lineage Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/data-lineage-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:information-ecology" }, { "type": "related_to", "targetId": "lpm:knowledge-object:source-of-truth-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:information-ownership-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:control-map" } ], "content": { "kind": "map", "summary": "A map for tracing data from source to transformation, metric, decision, control, and AI use.", "purpose": "Make the path of trusted information visible before teams automate decisions or scale AI outputs.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Data sources", "Transformations", "Reports", "Metric definitions", "AI use cases" ], "outputs": [ "Lineage map", "Trust gaps", "Data ownership actions" ], "howToUse": [ "Choose a critical metric, decision, or AI workflow.", "Trace source systems and transformations.", "Identify ownerless, stale, or uncertified data paths." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or when sources change", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Data-Lineage-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Data-Lineage-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Data-Lineage-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-data-lineage-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "data", "lineage", "information" ] } }, { "id": "lpm:knowledge-object:decision-communication-protocol", "type": "knowledge-object", "slug": "decision-communication-protocol", "title": "Decision Communication Protocol", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/decision-communication-protocol", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:decision-architecture" }, { "type": "supports", "targetId": "lpm:layer:communication-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-log" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "related_to", "targetId": "lpm:knowledge-object:communication-map" } ], "content": { "kind": "protocol", "summary": "A protocol for communicating decisions with context, owner, evidence, commitment, and follow-up path.", "purpose": "Ensure decisions become traceable commitments rather than scattered updates or meeting memories.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Decision owner", "Decision context", "Evidence", "Impacted teams", "Follow-up actions" ], "outputs": [ "Decision communication protocol", "Commitment record", "Follow-up responsibilities" ], "howToUse": [ "Define what counts as a decision.", "Use the protocol for material tradeoffs and commitments.", "Publish decisions where affected teams can find and act on them." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Monthly in decision forums", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Decision-Communication-Protocol-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Decision-Communication-Protocol-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Decision-Communication-Protocol-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-decision-communication-protocol-skill-v1.json", "access": "public", "available": true } }, "tags": [ "decisions", "communication", "protocol" ] } }, { "id": "lpm:knowledge-object:decision-log", "type": "knowledge-object", "slug": "decision-log", "title": "Decision Log", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/decision-log", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "related_to", "targetId": "lpm:knowledge-object:evidence-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-communication-protocol" } ], "content": { "kind": "log", "summary": "A log for recording material decisions, owners, evidence, rationale, commitments, and reversal triggers.", "purpose": "Reduce decision debt by making key choices traceable, reviewable, and connected to accountable follow-through.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Decision statement", "Decision owner", "Evidence", "Options", "Commitments" ], "outputs": [ "Decision record", "Decision aging view", "Reversal or escalation triggers" ], "howToUse": [ "Capture the decision and owner at the moment it is made.", "Record rationale, evidence, and expected action.", "Review aging, reversals, and unresolved commitments." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Weekly or biweekly for active initiatives", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Decision-Log-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Decision-Log-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Decision-Log-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-decision-log-skill-v1.json", "access": "public", "available": true } }, "tags": [ "decision log", "commitments", "traceability" ] } }, { "id": "lpm:knowledge-object:decision-rights-matrix", "type": "knowledge-object", "slug": "decision-rights-matrix", "title": "Decision Rights Matrix", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/decision-rights-matrix", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "supports", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-log" }, { "type": "related_to", "targetId": "lpm:knowledge-object:escalation-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ownership-map" } ], "content": { "kind": "matrix", "summary": "A matrix for clarifying who recommends, decides, contributes, approves, and escalates recurring decisions.", "purpose": "Make decision authority visible enough to reduce delay, repeated escalation, and ambiguous approval paths.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Decision types", "Roles", "Approval constraints", "Escalation triggers" ], "outputs": [ "Decision rights matrix", "Authority gaps", "Escalation rules" ], "howToUse": [ "List recurring decisions that slow work.", "Assign rights by role and decision type.", "Review unclear or conflicting authority with sponsors." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or after reorganizations", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Decision-Rights-Matrix-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Decision-Rights-Matrix-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Decision-Rights-Matrix-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-decision-rights-matrix-skill-v1.json", "access": "public", "available": true } }, "tags": [ "decision rights", "matrix", "authority" ] } }, { "id": "lpm:knowledge-object:escalation-map", "type": "knowledge-object", "slug": "escalation-map", "title": "Escalation Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/escalation-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:decision-architecture" }, { "type": "supports", "targetId": "lpm:layer:communication-architecture" }, { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "related_to", "targetId": "lpm:knowledge-object:communication-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:risk-acceptance-register" } ], "content": { "kind": "map", "summary": "A map of what should escalate, when, to whom, with what evidence, and what decision is required.", "purpose": "Replace informal escalation paths with clear thresholds, owners, and evidence expectations.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Risk triggers", "Decision owners", "Service thresholds", "Evidence sources" ], "outputs": [ "Escalation map", "Trigger definitions", "Escalation owner gaps" ], "howToUse": [ "Define recurring escalation scenarios.", "Assign thresholds, owner paths, and required evidence.", "Review whether escalations create decisions or simply more meetings." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or after incidents", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Escalation-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Escalation-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Escalation-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-escalation-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "escalation", "decisions", "risk" ] } }, { "id": "lpm:knowledge-object:evidence-checklist", "type": "knowledge-object", "slug": "evidence-checklist", "title": "Evidence Checklist", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/evidence-checklist", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:decision-architecture" }, { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-log" }, { "type": "related_to", "targetId": "lpm:knowledge-object:control-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:knowledge-freshness-review" } ], "content": { "kind": "checklist", "summary": "A checklist for confirming whether decisions, controls, owners, and AI outputs are backed by usable evidence.", "purpose": "Help teams distinguish opinion, stale artifacts, and incomplete records from evidence that can support execution and governance.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Decision or control", "Evidence sources", "Owner", "Review cadence", "Audit requirement" ], "outputs": [ "Evidence readiness view", "Evidence gaps", "Follow-up actions" ], "howToUse": [ "Select a decision, control, or AI workflow.", "Check evidence quality, ownership, freshness, and location.", "Assign remediation for missing or weak evidence." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Monthly or before approvals", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Evidence-Checklist-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Evidence-Checklist-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Evidence-Checklist-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-evidence-checklist-skill-v1.json", "access": "public", "available": true } }, "tags": [ "evidence", "auditability", "governance" ] } }, { "id": "lpm:knowledge-object:human-in-the-loop-model", "type": "knowledge-object", "slug": "human-in-the-loop-model", "title": "Human-in-the-Loop Model", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/human-in-the-loop-model", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "supports", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:risk-acceptance-register" } ], "content": { "kind": "model", "summary": "A model for defining where humans review, approve, override, monitor, or stop AI-assisted work.", "purpose": "Clarify the human control points required to use AI safely in operational workflows.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "AI workflow", "Risk tier", "Decision authority", "Review roles", "Override rules" ], "outputs": [ "Human review model", "Control point map", "AI escalation actions" ], "howToUse": [ "Map the AI-assisted workflow.", "Define review, approval, override, and monitoring points.", "Assign owners and evidence for each human control." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Before launch and after incidents", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Human-in-the-Loop-Model-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Human-in-the-Loop-Model-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Human-in-the-Loop-Model-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-human-in-the-loop-model-skill-v1.json", "access": "public", "available": true } }, "tags": [ "human review", "ai controls", "model" ] } }, { "id": "lpm:knowledge-object:incentive-alignment-checklist", "type": "knowledge-object", "slug": "incentive-alignment-checklist", "title": "Incentive Alignment Checklist", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/incentive-alignment-checklist", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ownership-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-initiative-owner-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "kind": "checklist", "summary": "A checklist for spotting where incentives, measures, rewards, and accountability pull teams away from enterprise outcomes.", "purpose": "Help leaders identify incentive conflicts that create local optimization, accountability gaps, or resistance to operating model change.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Goals", "KPIs", "Compensation signals", "Team incentives", "Enterprise outcomes" ], "outputs": [ "Incentive conflict view", "Alignment gaps", "Leadership actions" ], "howToUse": [ "Compare stated outcomes with the measures and rewards teams actually feel.", "Identify conflicts and missing accountability.", "Assign changes to metrics, ownership, or governance." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly and during planning", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Incentive-Alignment-Checklist-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Incentive-Alignment-Checklist-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Incentive-Alignment-Checklist-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-incentive-alignment-checklist-skill-v1.json", "access": "public", "available": true } }, "tags": [ "incentives", "ownership", "alignment" ] } }, { "id": "lpm:knowledge-object:information-ownership-register", "type": "knowledge-object", "slug": "information-ownership-register", "title": "Information Ownership Register", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/information-ownership-register", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "supports", "targetId": "lpm:layer:information-ecology" }, { "type": "related_to", "targetId": "lpm:knowledge-object:source-of-truth-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:knowledge-freshness-review" }, { "type": "related_to", "targetId": "lpm:knowledge-object:data-lineage-map" } ], "content": { "kind": "register", "summary": "A register for assigning owners to data, knowledge, documentation, definitions, and evidence sources.", "purpose": "Make information ownership explicit so teams and AI systems rely on current, trusted, accountable context.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Information assets", "Source systems", "Owners", "Freshness rules", "Use cases" ], "outputs": [ "Information owner register", "Ownerless knowledge gaps", "Freshness actions" ], "howToUse": [ "Inventory critical data and knowledge assets.", "Assign accountable owners and update cadence.", "Flag ownerless or stale sources used by decisions or AI." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Monthly or quarterly", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Information-Ownership-Register-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Information-Ownership-Register-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Information-Ownership-Register-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-information-ownership-register-skill-v1.json", "access": "public", "available": true } }, "tags": [ "information", "ownership", "knowledge" ] } }, { "id": "lpm:knowledge-object:integration-map", "type": "knowledge-object", "slug": "integration-map", "title": "Integration Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/integration-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:platform-structure" }, { "type": "related_to", "targetId": "lpm:knowledge-object:platform-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:data-lineage-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:control-map" } ], "content": { "kind": "map", "summary": "A map of system integrations, owners, data flows, failure points, and operational dependencies.", "purpose": "Help technology and operations leaders see where platform connections create execution, data, or control risk.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Systems", "Data flows", "Integration owners", "Failure modes", "Business dependencies" ], "outputs": [ "Integration map", "Dependency risks", "Owner and control actions" ], "howToUse": [ "Choose a workflow or platform domain.", "Map systems, integrations, owners, and dependencies.", "Flag brittle, duplicated, or ownerless connections." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or before platform changes", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Integration-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Integration-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Integration-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-integration-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "integration", "platform", "systems" ] } }, { "id": "lpm:knowledge-object:knowledge-freshness-review", "type": "knowledge-object", "slug": "knowledge-freshness-review", "title": "Knowledge Freshness Review", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/knowledge-freshness-review", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:information-ecology" }, { "type": "related_to", "targetId": "lpm:knowledge-object:information-ownership-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:source-of-truth-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:evidence-checklist" } ], "content": { "kind": "review", "summary": "A review for checking whether critical knowledge remains current, owned, trusted, and safe for AI retrieval.", "purpose": "Prevent stale or ownerless knowledge from creating execution errors, policy drift, or poor AI outputs.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Knowledge assets", "Last updated date", "Owner", "Usage", "AI retrieval exposure" ], "outputs": [ "Freshness review", "Stale knowledge list", "Update actions" ], "howToUse": [ "Select a knowledge domain.", "Review ownership, freshness, source trust, and usage.", "Prioritize updates where stale content affects decisions or AI." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Monthly for AI-exposed knowledge", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Knowledge-Freshness-Review-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Knowledge-Freshness-Review-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Knowledge-Freshness-Review-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-knowledge-freshness-review-skill-v1.json", "access": "public", "available": true } }, "tags": [ "knowledge", "freshness", "information" ] } }, { "id": "lpm:knowledge-object:meeting-architecture", "type": "knowledge-object", "slug": "meeting-architecture", "title": "Meeting Architecture", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/meeting-architecture", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:communication-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:communication-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:channel-purpose-guide" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-communication-protocol" } ], "content": { "kind": "architecture", "summary": "A reusable model for designing meetings around decisions, information flow, escalation, and commitments.", "purpose": "Turn meetings from recurring coordination drag into an intentional operating system for decisions and follow-through.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Meeting inventory", "Decision forums", "Participants", "Artifacts", "Follow-up paths" ], "outputs": [ "Meeting architecture", "Forum cleanup list", "Decision and update rules" ], "howToUse": [ "Inventory recurring meetings.", "Define the purpose, owner, decision rights, and outputs for each forum.", "Remove or redesign forums that do not move work." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Meeting-Architecture-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Meeting-Architecture-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Meeting-Architecture-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-meeting-architecture-skill-v1.json", "access": "public", "available": true } }, "tags": [ "meetings", "communication", "operating rhythm" ] } }, { "id": "lpm:knowledge-object:ownership-map", "type": "knowledge-object", "slug": "ownership-map", "title": "Ownership Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/ownership-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "supports", "targetId": "lpm:layer:decision-architecture" }, { "type": "supports", "targetId": "lpm:layer:information-ecology" }, { "type": "supports", "targetId": "lpm:layer:platform-structure" }, { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "supports", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "related_to", "targetId": "lpm:knowledge-object:information-ownership-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-initiative-owner-register" } ], "content": { "kind": "map", "summary": "A living control artifact that connects outcomes, decisions, execution, information, platforms, governance, and AI ownership.", "purpose": "Help companies scaling AI define and maintain accountable ownership across the full operating model.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Work domain", "Outcome owner", "Decision owner", "Information owner", "Platform owner", "Governance owner", "AI owner" ], "outputs": [ "Ownership map", "Ownership gap report", "Escalation triggers" ], "howToUse": [ "Select a workflow, capability, product area, or enterprise system.", "Name owners across outcome, decision, execution, information, platform, governance, and AI.", "Score clarity and assign actions for gaps." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or event-driven", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Ownership-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Ownership-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Ownership-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-ownership-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "ownership", "accountability", "operating model" ] } }, { "id": "lpm:knowledge-object:platform-map", "type": "knowledge-object", "slug": "platform-map", "title": "Platform Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/platform-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:platform-structure" }, { "type": "related_to", "targetId": "lpm:knowledge-object:integration-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:tool-rationalization-worksheet" }, { "type": "related_to", "targetId": "lpm:knowledge-object:data-lineage-map" } ], "content": { "kind": "map", "summary": "A map of platforms, workflow ownership, data ownership, users, integrations, and governance exposure.", "purpose": "Make platform sprawl, duplicated systems, unclear ownership, and AI workflow dependencies visible.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Applications", "System owners", "Users", "Integrations", "Critical workflows", "AI touchpoints" ], "outputs": [ "Platform ownership map", "Sprawl risks", "Rationalization actions" ], "howToUse": [ "Inventory platforms in a domain.", "Map owners, workflows, integrations, and evidence.", "Flag duplicative, ownerless, or AI-exposed systems." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or during planning", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Platform-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Platform-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Platform-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-platform-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "platforms", "systems", "sprawl" ] } }, { "id": "lpm:knowledge-object:risk-acceptance-register", "type": "knowledge-object", "slug": "risk-acceptance-register", "title": "Risk Acceptance Register", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/risk-acceptance-register", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:control-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:human-in-the-loop-model" } ], "content": { "kind": "register", "summary": "A register for documenting accepted risks, accountable approvers, rationale, expiration, controls, and review triggers.", "purpose": "Ensure risk acceptance is explicit, time-bound, evidenced, and connected to accountable governance owners.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Risk statement", "Approver", "Rationale", "Controls", "Expiration date", "Review trigger" ], "outputs": [ "Risk acceptance register", "Expired risk list", "Control follow-up actions" ], "howToUse": [ "Record each accepted risk with owner and rationale.", "Link controls, evidence, and expiration.", "Review open risks before they become permanent exceptions." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Monthly or quarterly", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Risk-Acceptance-Register-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Risk-Acceptance-Register-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Risk-Acceptance-Register-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-risk-acceptance-register-skill-v1.json", "access": "public", "available": true } }, "tags": [ "risk", "acceptance", "governance" ] } }, { "id": "lpm:knowledge-object:source-of-truth-map", "type": "knowledge-object", "slug": "source-of-truth-map", "title": "Source of Truth Map", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/source-of-truth-map", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:information-ecology" }, { "type": "related_to", "targetId": "lpm:knowledge-object:information-ownership-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:data-lineage-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:knowledge-freshness-review" } ], "content": { "kind": "map", "summary": "A map of authoritative sources for metrics, policies, knowledge, decisions, records, and AI context.", "purpose": "Help teams know which source to trust, who owns it, and where conflicting information must be resolved.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Information domains", "Systems of record", "Owners", "Conflicting sources", "Use cases" ], "outputs": [ "Source of truth map", "Conflict list", "Information governance actions" ], "howToUse": [ "Identify critical information domains.", "Name the authoritative source and owner.", "Resolve duplicate or conflicting sources that affect execution or AI." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Quarterly or after system changes", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Source-of-Truth-Map-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Source-of-Truth-Map-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Source-of-Truth-Map-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-source-of-truth-map-skill-v1.json", "access": "public", "available": true } }, "tags": [ "source of truth", "information", "data" ] } }, { "id": "lpm:knowledge-object:tool-rationalization-worksheet", "type": "knowledge-object", "slug": "tool-rationalization-worksheet", "title": "Tool Rationalization Worksheet", "canonicalUrl": "https://largepeoplemodel.com/library/knowledge-objects/tool-rationalization-worksheet", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-23", "relationships": [ { "type": "supports", "targetId": "lpm:layer:platform-structure" }, { "type": "related_to", "targetId": "lpm:knowledge-object:platform-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:integration-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:control-map" } ], "content": { "kind": "worksheet", "summary": "A worksheet for evaluating tool duplication, ownership, workflow fit, integration risk, and rationalization priority.", "purpose": "Help leaders reduce platform sprawl without losing the operating context that made tools useful in the first place.", "companyProfiles": [ "500+", "5,000+", "10,000+" ], "inputs": [ "Tool inventory", "Owners", "Use cases", "Costs", "Integrations", "User groups" ], "outputs": [ "Rationalization worksheet", "Retain/replace/retire recommendations", "Migration risks" ], "howToUse": [ "Inventory tools by workflow and owner.", "Assess duplication, integration, cost, control, and AI exposure.", "Prioritize rationalization actions with accountable owners." ], "expectations": [ "Use named owners, current evidence, and explicit decision paths rather than informal assumptions.", "Review the artifact with accountable leaders before treating it as the approved operating record.", "Refresh it when ownership, systems, controls, AI use cases, or company structure changes." ], "reviewCadence": "Planning cycle or platform portfolio review", "files": { "pdf": { "format": "pdf", "label": "Executive reference PDF", "href": "/reusable-artifacts/LPM-Reusable-Tool-Rationalization-Worksheet-v1.pdf", "access": "public", "available": true }, "docx": { "format": "docx", "label": "Editable working artifact", "href": "/reusable-artifacts/LPM-Reusable-Tool-Rationalization-Worksheet-v1.docx", "access": "public", "available": true }, "markdown": { "format": "markdown", "label": "Markdown source copy", "href": "/reusable-artifacts/LPM-Reusable-Tool-Rationalization-Worksheet-v1.md", "access": "public", "available": true }, "json": { "format": "json", "label": "Lapemo skill schema", "href": "/reusable-artifacts/lpm-tool-rationalization-worksheet-skill-v1.json", "access": "public", "available": true } }, "tags": [ "tool sprawl", "platforms", "worksheet" ] } }, { "id": "lpm:use-case:ai-adoption-readiness", "type": "use-case", "slug": "ai-adoption-readiness", "title": "AI Adoption Readiness", "canonicalUrl": "https://largepeoplemodel.com/use-cases/ai-adoption-readiness", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:information-ecology" }, { "type": "applies_to", "targetId": "lpm:layer:platform-structure" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:ai-amplification" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:source-of-truth-map" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:human-in-the-loop-model" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:agent-accountability-checklist" } ], "content": { "audience": [ "cio", "cto", "coo", "ai-transformation-leader" ], "audienceLabel": "CIO, CTO, COO, AI transformation leader", "summary": "Diagnose whether the operating model has enough ownership, decision clarity, information trust, governance, and platform structure to safely scale AI.", "problem": { "heading": "AI adoption fails when the operating model underneath it is unclear.", "body": [ "Most enterprises do not fail at AI because they lack interest, pilots, or tools. They fail because the work AI is supposed to improve is not owned, governed, measured, or connected clearly enough.", "AI pilots can look promising in isolation, but scaling them across the enterprise exposes gaps in ownership, decision rights, information quality, platform boundaries, and risk controls." ], "bullets": [ "AI use cases launch without accountable business owners.", "AI recommendations outpace decision governance.", "Copilots summarize fragmented information.", "Agents automate workflows without clear operating boundaries.", "Governance is added after adoption has already spread." ] }, "whyTraditionalApproachesMissIt": { "heading": "Traditional AI readiness focuses too narrowly on technology.", "body": [ "Many readiness efforts evaluate data, tooling, model access, security, and technical architecture. Those matter, but they do not fully answer whether the enterprise can absorb AI into how work actually gets done." ], "bullets": [ "Technical readiness does not prove ownership readiness.", "Data availability does not prove information trust.", "Governance policies do not prove workflow-level control.", "AI pilots do not prove enterprise adoption capacity." ] }, "lpmDiagnosis": { "heading": "LPM diagnoses AI readiness across all seven layers.", "body": [ "The Large People Model evaluates whether the foundation layers are mature enough for AI to amplify the organization safely. AI readiness is not treated as a standalone technology question. It is treated as an operating model question." ], "bullets": [ "Who owns each AI-supported outcome?", "Which decisions can AI support, recommend, or automate?", "What information can AI safely use?", "Which platforms will AI touch?", "What governance must exist before scale?", "Where could AI amplify ambiguity, noise, or risk?" ] }, "metricsToInspect": [ { "name": "AI adoption readiness score", "whyItMatters": "Shows whether the foundation layers can support AI at scale.", "href": "/metrics/ai-adoption-readiness-score" }, { "name": "AI use case owner coverage", "whyItMatters": "Reveals whether every AI use case has a named accountable owner.", "href": "/metrics/ai-use-case-owner-coverage" }, { "name": "Ownership clarity score", "whyItMatters": "Identifies accountability gaps before AI amplifies work.", "href": "/metrics/ownership-clarity-score" }, { "name": "Human-in-the-loop clarity", "whyItMatters": "Shows whether human oversight is explicit for AI-supported work.", "href": "/metrics/human-in-the-loop-clarity" }, { "name": "Information trust score", "whyItMatters": "Tests whether AI is using reliable, governed information.", "href": "/metrics/information-trust-score" }, { "name": "Control coverage", "whyItMatters": "Checks whether risk controls exist before adoption spreads.", "href": "/metrics/control-coverage" }, { "name": "Agent action auditability", "whyItMatters": "Shows whether automated actions can be traced and reviewed.", "href": "/metrics/agent-action-auditability" }, { "name": "AI amplification risk", "whyItMatters": "Surfaces where AI may scale ambiguity, noise, or governance gaps.", "href": "/metrics/ai-amplification-risk" } ], "recommendedArtifacts": [ { "name": "AI readiness assessment", "purpose": "Score readiness across the layers before scaling adoption." }, { "name": "AI use case owner register", "purpose": "Assign accountable owners to AI outcomes, risks, and workflows." }, { "name": "Decision rights matrix", "purpose": "Define what AI can support, recommend, or automate.", "href": "/library/knowledge-objects/decision-rights-matrix" }, { "name": "Source-of-truth map", "purpose": "Identify information sources safe enough for AI use.", "href": "/library/knowledge-objects/source-of-truth-map" }, { "name": "AI governance checklist", "purpose": "Clarify controls, reviews, escalation, and risk boundaries.", "href": "/library/knowledge-objects/ai-governance-checklist" }, { "name": "Human-in-the-loop model", "purpose": "Document where human judgment remains required.", "href": "/library/knowledge-objects/human-in-the-loop-model" }, { "name": "Agent accountability checklist", "purpose": "Define ownership and auditability for agentic work.", "href": "/library/knowledge-objects/agent-accountability-checklist" } ], "whatGoodLooksLike": { "heading": "AI is scaled into an operating model that can govern it.", "body": [ "Every AI use case has a named accountable owner, clear decision boundaries, trusted information sources, defined platform access, and governance controls that are visible before adoption scales." ], "bullets": [ "AI-supported outcomes are owned.", "AI decision boundaries are documented.", "Human oversight is explicit.", "Information sources are trusted and governed.", "Agents are auditable.", "Leaders can see whether AI is improving the system or amplifying dysfunction." ] }, "lapemoSupport": { "heading": "Lapemo turns AI readiness into operating intelligence.", "body": [ "Lapemo can capture AI use cases, owners, decision types, governance controls, information sources, platform boundaries, and agent records so leaders can monitor readiness and amplification risk over time." ], "bullets": [ "Tracks AI use case ownership.", "Scores readiness across the seven layers.", "Surfaces governance and accountability gaps.", "Connects AI activity to decisions, platforms, and controls.", "Monitors whether AI is scaling faster than the foundation layers can support." ] } } }, { "id": "lpm:use-case:transformation-operating-model", "type": "use-case", "slug": "transformation-operating-model", "title": "Transformation Operating Model", "canonicalUrl": "https://largepeoplemodel.com/use-cases/transformation-operating-model", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:communication-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:information-ecology" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:source-of-truth-map" } ], "content": { "audience": [ "coo", "transformation-leader", "strategy-leader" ], "audienceLabel": "COO, transformation leader, strategy leader", "summary": "Connect strategy, ownership, decisions, communication, and delivery so transformation work does not drift from intent.", "problem": { "heading": "Transformation breaks when strategy cannot travel through the operating model.", "body": [ "Most transformation programs start with a clear strategic intent. The breakdown happens as that intent moves through ownership, decisions, communication, platforms, governance, and delivery teams." ], "bullets": [ "Strategic priorities become disconnected initiatives.", "Teams interpret the transformation differently.", "Decisions stall in steering committees.", "Communication volume increases without improving clarity.", "Progress reports hide operating model bottlenecks." ] }, "whyTraditionalApproachesMissIt": { "heading": "Traditional transformation tracking often measures activity instead of coordination.", "body": [ "Roadmaps, milestones, status reports, and PMO dashboards show whether work is moving. They do not always show whether the operating model is aligned enough for the work to create the intended change." ], "bullets": [ "Green status can hide unclear ownership.", "Milestones can move while decisions remain unresolved.", "Communication plans can increase updates without creating shared understanding.", "Governance forums can approve work without removing bottlenecks." ] }, "lpmDiagnosis": { "heading": "LPM identifies where transformation drift originates.", "body": [ "LPM connects strategy to ownership, ownership to decisions, decisions to communication, communication to information, and governance to execution risk." ], "bullets": [ "Which outcomes are not clearly owned?", "Which decisions are aging or repeatedly escalated?", "Where is communication creating noise?", "Which information sources define progress?", "Which governance controls are helping or blocking execution?" ] }, "metricsToInspect": [ { "name": "Ownership clarity score", "whyItMatters": "Shows whether strategic outcomes have accountable owners.", "href": "/metrics/ownership-clarity-score" }, { "name": "Outcome owner coverage", "whyItMatters": "Identifies transformation work without clear ownership.", "href": "/metrics/outcome-owner-coverage" }, { "name": "Decision latency", "whyItMatters": "Reveals where key choices are slowing execution.", "href": "/metrics/decision-latency" }, { "name": "Decision aging", "whyItMatters": "Surfaces unresolved decisions that create drift.", "href": "/metrics/decision-aging" }, { "name": "Communication channel fragmentation", "whyItMatters": "Shows whether updates are creating noise.", "href": "/metrics/communication-channel-fragmentation" }, { "name": "Information freshness", "whyItMatters": "Checks whether progress signals are current.", "href": "/metrics/information-freshness" }, { "name": "Governance bottleneck count", "whyItMatters": "Identifies controls or forums slowing execution.", "href": "/metrics/governance-bottleneck-count" }, { "name": "Approval cycle time", "whyItMatters": "Shows how governance timing affects transformation flow.", "href": "/metrics/approval-cycle-time" } ], "recommendedArtifacts": [ { "name": "Transformation ownership map", "purpose": "Tie strategic outcomes to accountable owners." }, { "name": "Decision rights matrix", "purpose": "Clarify who can decide, approve, pause, or redirect work.", "href": "/library/knowledge-objects/decision-rights-matrix" }, { "name": "Transformation decision log", "purpose": "Track critical decisions and unresolved tradeoffs." }, { "name": "Communication architecture map", "purpose": "Define how transformation decisions travel." }, { "name": "Source-of-truth map", "purpose": "Anchor reporting to trusted information sources.", "href": "/library/knowledge-objects/source-of-truth-map" }, { "name": "Governance decision tree", "purpose": "Clarify which issues need review or escalation." }, { "name": "Operating model review artifact", "purpose": "Review progress through the LPM layers." } ], "whatGoodLooksLike": { "heading": "Transformation becomes a governed operating system, not a status exercise.", "body": [ "Strategic outcomes are owned, decisions are traceable, communication is intentional, information is trusted, and governance forums remove friction instead of adding it." ], "bullets": [ "Each transformation outcome has an accountable owner.", "Critical decisions have clear rights and timelines.", "Progress reporting is tied to trusted sources.", "Governance focuses on risk, tradeoffs, and bottlenecks.", "Leaders can see where the operating model is drifting." ] }, "lapemoSupport": { "heading": "Lapemo gives transformation leaders an operating model command layer.", "body": [ "Lapemo can connect outcomes, owners, decisions, communication channels, information sources, governance controls, and risk signals so transformation leaders can see where execution is drifting." ], "bullets": [ "Captures ownership and decision lineage.", "Scores maturity across affected layers.", "Surfaces bottlenecks and accountability gaps.", "Tracks governance drift.", "Helps leaders prioritize operating model improvements." ] } } }, { "id": "lpm:use-case:product-operating-model", "type": "use-case", "slug": "product-operating-model", "title": "Product Operating Model", "canonicalUrl": "https://largepeoplemodel.com/use-cases/product-operating-model", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:communication-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:platform-structure" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:platform-map" } ], "content": { "audience": [ "cpo", "product-operations", "technology-leader" ], "audienceLabel": "CPO, product operations, technology leaders", "summary": "Improve product clarity at scale by connecting roadmaps, decision rights, platform structure, ownership, architecture, and delivery.", "problem": { "heading": "Product teams lose clarity as the operating model scales.", "body": [ "Product organizations often start with strong team-level practices, but clarity breaks down across portfolios, platforms, dependencies, architecture, funding, and governance." ], "bullets": [ "Roadmaps are not tied to decision rights.", "Product ownership is unclear across platforms.", "Architecture and product decisions move separately.", "Teams wait on dependency decisions.", "Delivery metrics improve while strategic alignment weakens." ] }, "whyTraditionalApproachesMissIt": { "heading": "Team-level agile practices do not solve enterprise product coordination.", "body": [ "Agile rituals, backlogs, roadmaps, and planning cadences help teams execute. They do not automatically resolve cross-product ownership, platform boundaries, decision authority, or governance constraints." ], "bullets": [ "Product teams can be agile while enterprise decisions remain slow.", "Roadmaps can hide unresolved dependencies.", "Platform ownership can be unclear beneath product delivery.", "Governance can approve work without clarifying decision rights." ] }, "lpmDiagnosis": { "heading": "LPM diagnoses the operating system around product delivery.", "body": [ "LPM looks beyond delivery activity to assess whether product ownership, decision architecture, communication, platform structure, and governance support the outcomes the product organization is trying to create." ], "bullets": [ "Who owns the product outcome?", "Which decisions slow product flow?", "Where do dependencies cross platform boundaries?", "Which communication channels carry product decisions?", "How does governance affect velocity and risk?" ] }, "metricsToInspect": [ { "name": "Ownership clarity score", "whyItMatters": "Shows whether product outcomes have accountable owners.", "href": "/metrics/ownership-clarity-score" }, { "name": "Decision latency", "whyItMatters": "Reveals decisions slowing product flow.", "href": "/metrics/decision-latency" }, { "name": "Decision dependency count", "whyItMatters": "Surfaces dependency chains across products and platforms.", "href": "/metrics/decision-dependency-count" }, { "name": "Channel fragmentation", "whyItMatters": "Shows whether product communication creates noise.", "href": "/metrics/channel-fragmentation" }, { "name": "Platform ownership clarity", "whyItMatters": "Identifies unclear ownership across platform boundaries.", "href": "/metrics/platform-ownership-clarity" }, { "name": "Manual handoff rate", "whyItMatters": "Shows where workflow friction remains hidden.", "href": "/metrics/manual-handoff-rate" }, { "name": "Workflow fragmentation score", "whyItMatters": "Measures whether platforms support or fragment flow.", "href": "/metrics/workflow-fragmentation-score" }, { "name": "Approval cycle time", "whyItMatters": "Shows whether governance is helping or slowing delivery.", "href": "/metrics/approval-cycle-time" } ], "recommendedArtifacts": [ { "name": "Product ownership map", "purpose": "Clarify ownership across product outcomes and platforms." }, { "name": "Decision rights matrix", "purpose": "Define product, platform, and architecture decision rights.", "href": "/library/knowledge-objects/decision-rights-matrix" }, { "name": "Product decision log", "purpose": "Trace roadmap and architecture choices." }, { "name": "Platform map", "purpose": "Show platform boundaries and dependencies.", "href": "/library/knowledge-objects/platform-map" }, { "name": "Dependency map", "purpose": "Identify cross-team and cross-platform dependencies." }, { "name": "Architecture decision record", "purpose": "Keep architectural choices visible and reusable." }, { "name": "Governance review model", "purpose": "Clarify how risk and tradeoffs are reviewed." } ], "whatGoodLooksLike": { "heading": "Product delivery is connected to ownership, architecture, platforms, and decisions.", "body": [ "Product teams can move quickly because ownership is clear, decision rights are known, platform boundaries are understood, and governance supports the work instead of slowing it without purpose." ], "bullets": [ "Product outcomes have clear owners.", "Roadmaps reflect real dependencies and decision rights.", "Platform ownership is visible.", "Architecture decisions are traceable.", "Governance creates clarity around risk and tradeoffs." ] }, "lapemoSupport": { "heading": "Lapemo connects product execution to the operating model behind it.", "body": [ "Lapemo can map product outcomes, owners, decisions, platforms, dependencies, and governance controls so product leaders can see the system shaping velocity and clarity." ], "bullets": [ "Tracks ownership across product and platform boundaries.", "Connects product decisions to downstream impacts.", "Identifies platform fragmentation and manual handoffs.", "Surfaces dependency and governance bottlenecks.", "Helps product operations manage operating model health." ] } } }, { "id": "lpm:use-case:ma-integration", "type": "use-case", "slug": "ma-integration", "title": "M&A Integration", "canonicalUrl": "https://largepeoplemodel.com/use-cases/ma-integration", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:communication-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:information-ecology" }, { "type": "applies_to", "targetId": "lpm:layer:platform-structure" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:source-of-truth-map" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:risk-acceptance-register" } ], "content": { "audience": [ "private-equity", "corporate-strategy", "transformation-office" ], "audienceLabel": "Private equity, corporate strategy, transformation office", "summary": "Diagnose duplicate platforms, conflicting decision rights, mismatched governance, and operating model friction after acquisition.", "problem": { "heading": "M&A integration fails when companies merge structures but not operating models.", "body": [ "After an acquisition, leaders often focus on org design, systems consolidation, synergy targets, and integration workstreams. The harder problem is creating one coordination model across two inherited ways of working." ], "bullets": [ "Duplicate platforms support similar workflows.", "Decision rights conflict across legacy organizations.", "Information sources disagree.", "Governance expectations differ.", "Teams interpret accountability differently.", "Cultural norms create communication friction." ] }, "whyTraditionalApproachesMissIt": { "heading": "Traditional integration plans often underdiagnose coordination mismatch.", "body": [ "Integration plans track milestones, cost synergies, systems migration, and org changes. Those are necessary, but they may not expose how ownership, decisions, communication, information, and governance actually need to change after close." ], "bullets": [ "Systems are consolidated without ownership clarity.", "Governance models are copied rather than redesigned.", "Reporting structures change before decision rights do.", "Culture is discussed broadly instead of diagnosed operationally.", "Duplicate platforms are treated as technology issues only." ] }, "lpmDiagnosis": { "heading": "LPM maps the operating model mismatch between organizations.", "body": [ "LPM helps integration leaders compare how each organization assigns ownership, makes decisions, communicates, manages information, structures platforms, and governs risk." ], "bullets": [ "Where do ownership models conflict?", "Which decision rights must be redesigned?", "Which communication patterns are incompatible?", "Which information sources disagree?", "Which platforms overlap or fragment work?", "What governance model should become standard?" ] }, "metricsToInspect": [ { "name": "Ownership clarity score", "whyItMatters": "Shows whether the combined operating model has clear owners.", "href": "/metrics/ownership-clarity-score" }, { "name": "Decision owner coverage", "whyItMatters": "Identifies decisions without accountable owners.", "href": "/metrics/decision-owner-coverage" }, { "name": "Decision reversal rate", "whyItMatters": "Surfaces instability from unresolved authority.", "href": "/metrics/decision-reversal-rate" }, { "name": "Information conflict rate", "whyItMatters": "Shows where inherited sources disagree.", "href": "/metrics/information-conflict-rate" }, { "name": "Source-of-truth coverage", "whyItMatters": "Identifies authoritative information domains.", "href": "/metrics/source-of-truth-coverage" }, { "name": "Tool duplication count", "whyItMatters": "Measures overlapping platforms and systems.", "href": "/metrics/tool-duplication-count" }, { "name": "Platform ownership clarity", "whyItMatters": "Clarifies who owns inherited platforms.", "href": "/metrics/platform-ownership-clarity" }, { "name": "Control coverage", "whyItMatters": "Shows where governance controls are missing or inconsistent.", "href": "/metrics/control-coverage" } ], "recommendedArtifacts": [ { "name": "Integration ownership map", "purpose": "Compare ownership across legacy organizations." }, { "name": "Decision rights comparison", "purpose": "Identify authority conflicts and redesign needs." }, { "name": "Platform overlap inventory", "purpose": "Map duplicate systems and workflows." }, { "name": "Source-of-truth map", "purpose": "Define authoritative information domains.", "href": "/library/knowledge-objects/source-of-truth-map" }, { "name": "Governance model comparison", "purpose": "Compare controls and escalation paths." }, { "name": "Integration operating model canvas", "purpose": "Document the target coordination model." }, { "name": "Risk acceptance register", "purpose": "Track risks accepted during integration.", "href": "/library/knowledge-objects/risk-acceptance-register" } ], "whatGoodLooksLike": { "heading": "The combined company has one visible coordination model.", "body": [ "Leadership can see who owns what, how decisions are made, which platforms are authoritative, what information is trusted, and how governance works across the integrated enterprise." ], "bullets": [ "Conflicting decision rights are resolved.", "Duplicate platforms have a rationalization path.", "Critical information domains have authoritative sources.", "Governance is standardized where needed.", "Teams understand the new operating model." ] }, "lapemoSupport": { "heading": "Lapemo helps integration leaders see operating model convergence.", "body": [ "Lapemo can map ownership, decisions, platforms, information domains, and governance controls across legacy organizations so leaders can identify conflicts, overlaps, and integration priorities." ], "bullets": [ "Captures duplicate platform and ownership patterns.", "Traces decision rights across organizations.", "Identifies broken accountability chains.", "Tracks information and governance convergence.", "Helps monitor integration drift after close." ] } } }, { "id": "lpm:use-case:platform-governance", "type": "use-case", "slug": "platform-governance", "title": "Platform Governance", "canonicalUrl": "https://largepeoplemodel.com/use-cases/platform-governance", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "applies_to", "targetId": "lpm:layer:platform-structure" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:information-ecology" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:platform-map" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:integration-map" } ], "content": { "audience": [ "cio", "enterprise-architect", "platform-owner" ], "audienceLabel": "CIO, enterprise architects, platform owners", "summary": "Reduce tool sprawl and define platform ownership, governance, lifecycle, and integration rules.", "problem": { "heading": "Tool sprawl creates coordination debt.", "body": [ "Enterprises rarely suffer from a lack of tools. They suffer from too many systems with unclear ownership, overlapping purpose, fragmented workflows, and inconsistent governance." ], "bullets": [ "Teams use different tools for similar work.", "Manual handoffs connect critical workflows.", "Platform owners are unclear.", "Governance is inconsistent across tools.", "Information moves without clear lineage.", "AI and agents increase risk by operating across fragmented systems." ] }, "whyTraditionalApproachesMissIt": { "heading": "Traditional platform governance often focuses on inventory, not operating behavior.", "body": [ "Application inventories, architecture reviews, and rationalization efforts are useful, but they often miss how platforms shape day-to-day ownership, decisions, communication, information flow, and governance." ], "bullets": [ "Tool inventories do not show workflow fragmentation.", "Architecture diagrams do not always show decision ownership.", "Rationalization efforts can miss operating model dependencies.", "Governance may focus on approval rather than lifecycle and accountability." ] }, "lpmDiagnosis": { "heading": "LPM diagnoses how platforms shape the operating model.", "body": [ "LPM connects platforms to the work, owners, decisions, information, integrations, and governance rules they support or distort." ], "bullets": [ "Which tools support critical workflows?", "Who owns each platform and boundary?", "Where are manual handoffs creating risk?", "Which tools duplicate purpose?", "Which information sources are authoritative?", "What governance applies across the platform lifecycle?" ] }, "metricsToInspect": [ { "name": "Platform ownership clarity", "whyItMatters": "Shows whether every platform has an accountable owner.", "href": "/metrics/platform-ownership-clarity" }, { "name": "Integration coverage", "whyItMatters": "Measures whether key workflows are connected.", "href": "/metrics/integration-coverage" }, { "name": "Manual handoff rate", "whyItMatters": "Surfaces workflow risk from manual movement.", "href": "/metrics/manual-handoff-rate" }, { "name": "Workflow fragmentation score", "whyItMatters": "Measures fragmentation across tools and systems.", "href": "/metrics/workflow-fragmentation-score" }, { "name": "Tool duplication count", "whyItMatters": "Identifies duplicate systems with overlapping purpose.", "href": "/metrics/tool-duplication-count" }, { "name": "Source-of-truth coverage", "whyItMatters": "Shows where authoritative information is defined.", "href": "/metrics/source-of-truth-coverage" }, { "name": "Control coverage", "whyItMatters": "Checks whether platform controls match risk.", "href": "/metrics/control-coverage" }, { "name": "Governance bottleneck count", "whyItMatters": "Shows where review or approval slows execution.", "href": "/metrics/governance-bottleneck-count" } ], "recommendedArtifacts": [ { "name": "Platform map", "purpose": "Show systems, boundaries, owners, and workflows.", "href": "/library/knowledge-objects/platform-map" }, { "name": "Tool sprawl inventory", "purpose": "Identify duplicate tools and overlapping purpose." }, { "name": "Workflow inventory", "purpose": "Map critical workflows across systems." }, { "name": "Integration map", "purpose": "Document system connections and gaps.", "href": "/library/knowledge-objects/integration-map" }, { "name": "Platform ownership register", "purpose": "Assign owners for lifecycle and boundaries." }, { "name": "Governance decision tree", "purpose": "Clarify review and approval paths." }, { "name": "Lifecycle policy map", "purpose": "Define governance across platform lifecycle stages." } ], "whatGoodLooksLike": { "heading": "Platforms have clear purpose, ownership, lifecycle, and governance.", "body": [ "The enterprise can see which platforms support which workflows, who owns them, how they integrate, what information they carry, and how they are governed across their lifecycle." ], "bullets": [ "Platform ownership is explicit.", "Duplicate tools are identified and rationalized.", "Manual handoffs are reduced or governed.", "Workflows are mapped across platform boundaries.", "Governance is proportional to risk and usage." ] }, "lapemoSupport": { "heading": "Lapemo turns platform governance into operating intelligence.", "body": [ "Lapemo can map platforms, owners, boundaries, integrations, controls, and workflow fragmentation so technology leaders can govern the platform estate as part of the operating model." ], "bullets": [ "Captures platform ownership and boundaries.", "Scores integration and fragmentation signals.", "Identifies manual handoff risk.", "Links platforms to decisions, information, and controls.", "Supports platform rationalization and governance reviews." ] } } }, { "id": "lpm:use-case:agent-workforce-governance", "type": "use-case", "slug": "agent-workforce-governance", "title": "Agent Workforce Governance", "canonicalUrl": "https://largepeoplemodel.com/use-cases/agent-workforce-governance", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-08-04", "relationships": [ { "type": "applies_to", "targetId": "lpm:layer:ai-amplification" }, { "type": "applies_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "applies_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "applies_to", "targetId": "lpm:layer:platform-structure" }, { "type": "applies_to", "targetId": "lpm:layer:information-ecology" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:agent-accountability-checklist" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:human-in-the-loop-model" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:ai-governance-checklist" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "evidenced_by", "targetId": "lpm:knowledge-object:risk-acceptance-register" } ], "content": { "audience": [ "ai-leader", "risk-leader", "technology-executive" ], "audienceLabel": "AI leaders, risk leaders, technology executives", "summary": "Bring governed agents under clear ownership, action boundaries, auditability, approvals, exceptions, and human accountability.", "problem": { "heading": "Governed agents create a new operating model risk.", "body": [ "Agents do not only generate content or recommendations. They can take action, move across systems, trigger workflows, and affect decisions. That makes them part of the operating model." ], "bullets": [ "Agent actions may not have clear human owners.", "Approval rules can be inconsistent.", "Audit trails may be incomplete.", "Agents may operate across fragmented platforms.", "Exceptions may not be traceable.", "Leaders may not know what agents are allowed to do." ] }, "whyTraditionalApproachesMissIt": { "heading": "Traditional AI governance often stops at policy and model risk.", "body": [ "Policies, risk reviews, model evaluations, and security controls matter. But agent governance also requires operating model clarity: who owns agent actions, what decisions agents can support, where approvals occur, and how actions are traced." ], "bullets": [ "Model governance does not define workflow ownership.", "Security review does not define decision accountability.", "Policy does not always translate to action boundaries.", "Auditability must connect agent actions to human oversight." ] }, "lpmDiagnosis": { "heading": "LPM treats agents as participants in the operating model.", "body": [ "LPM diagnoses whether the enterprise has enough ownership, decision architecture, platform structure, governance, and information trust to safely operate agents." ], "bullets": [ "Who supervises each agent?", "What actions can the agent take?", "Which decisions require human approval?", "What systems can the agent access?", "How are actions logged and reviewed?", "What happens when an agent violates a boundary?" ] }, "metricsToInspect": [ { "name": "AI use case owner coverage", "whyItMatters": "Shows whether agentic work has accountable supervision.", "href": "/metrics/ai-use-case-owner-coverage" }, { "name": "Human-in-the-loop clarity", "whyItMatters": "Clarifies which decisions require human review.", "href": "/metrics/human-in-the-loop-clarity" }, { "name": "Agent action auditability", "whyItMatters": "Measures whether actions can be traced and reviewed.", "href": "/metrics/agent-action-auditability" }, { "name": "Control coverage", "whyItMatters": "Checks whether controls exist for agent actions.", "href": "/metrics/control-coverage" }, { "name": "Risk acceptance lineage", "whyItMatters": "Shows whether exceptions and accepted risks are traceable.", "href": "/metrics/risk-acceptance-lineage" }, { "name": "Platform ownership clarity", "whyItMatters": "Identifies ownership across systems agents access.", "href": "/metrics/platform-ownership-clarity" }, { "name": "Information trust score", "whyItMatters": "Tests whether agents use trusted information.", "href": "/metrics/information-trust-score" }, { "name": "AI amplification risk", "whyItMatters": "Surfaces where agents may scale operating model weakness.", "href": "/metrics/ai-amplification-risk" } ], "recommendedArtifacts": [ { "name": "Agent accountability checklist", "purpose": "Define owners, supervisors, and responsibility boundaries.", "href": "/library/knowledge-objects/agent-accountability-checklist" }, { "name": "Agent action register", "purpose": "Track agents, allowed actions, and workflow scope." }, { "name": "Human-in-the-loop model", "purpose": "Clarify approval and escalation requirements.", "href": "/library/knowledge-objects/human-in-the-loop-model" }, { "name": "AI governance checklist", "purpose": "Map controls, reviews, and exceptions.", "href": "/library/knowledge-objects/ai-governance-checklist" }, { "name": "Decision rights matrix", "purpose": "Define decisions agents may support or trigger.", "href": "/library/knowledge-objects/decision-rights-matrix" }, { "name": "Platform access map", "purpose": "Show systems agents can access." }, { "name": "Risk acceptance register", "purpose": "Document accepted risks and accountable approvers.", "href": "/library/knowledge-objects/risk-acceptance-register" }, { "name": "Audit review protocol", "purpose": "Define how agent actions are reviewed over time." } ], "whatGoodLooksLike": { "heading": "Agents operate inside clear human accountability and governance boundaries.", "body": [ "Every agent has a supervisor, defined autonomy level, approved action boundaries, trusted information sources, governed platform access, and audit records that connect actions back to accountable humans." ], "bullets": [ "Agent ownership is explicit.", "Autonomy levels are defined.", "Human approval rules are clear.", "Actions are auditable.", "Exceptions are traceable.", "Governance can detect drift before harm occurs." ] }, "lapemoSupport": { "heading": "Lapemo helps govern agents as part of the operating model.", "body": [ "Lapemo can register agents, map supervisors, classify autonomy levels, connect agent activity to decisions and platforms, and monitor governance coverage and auditability." ], "bullets": [ "Tracks agent ownership and supervision.", "Maps autonomy and decision boundaries.", "Captures agent action lineage.", "Connects controls, approvals, and exceptions.", "Surfaces AI amplification risk across the operating model." ] } } }, { "id": "lpm:article:ai-will-not-fix-your-operating-model", "type": "article", "slug": "ai-will-not-fix-your-operating-model", "title": "AI will not fix your operating model.", "canonicalUrl": "https://largepeoplemodel.com/insights/ai-will-not-fix-your-operating-model", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-15", "relationships": [ { "type": "related_to", "targetId": "lpm:layer:identity-and-incentives" }, { "type": "related_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:layer:communication-architecture" }, { "type": "related_to", "targetId": "lpm:layer:information-ecology" }, { "type": "related_to", "targetId": "lpm:layer:platform-structure" }, { "type": "related_to", "targetId": "lpm:layer:governance-architecture" }, { "type": "related_to", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:metric:ai-adoption-readiness-score" }, { "type": "related_to", "targetId": "lpm:knowledge-object:agent-accountability-checklist" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-governance-checklist" } ], "content": { "subtitle": "It will reveal it, stress it, and amplify it.", "excerpt": "AI makes the operating model more powerful. That is the opportunity and the risk. If ownership, decisions, communication, information, platforms, and governance are unclear, AI will not remove the dysfunction. It will scale it.", "category": "founder-essays", "roles": [ "ceo-coo", "cio-cto", "ai-leader", "transformation-leader" ], "problems": [ "ai-adoption-failure", "poor-accountability", "execution-misalignment" ], "tags": [ "AI readiness", "operating model", "AI amplification", "founder essay" ], "readTime": "6 min read", "author": "Chad Stewart", "publishedAt": "2026-06-15", "sections": [ { "heading": "The mistake enterprises are making", "body": [ "Many enterprises are treating AI as if it can compensate for an unclear operating model. The assumption is simple: give teams better tools, automate more work, and productivity will follow.", "That assumption is incomplete. AI can improve speed, pattern recognition, summarization, and automation. But it does not automatically clarify ownership, make accountable decisions, clean up information quality, rationalize platforms, or create governance discipline." ] }, { "heading": "AI scales the system underneath it", "body": [ "Every organization already has an operating model. Some of it is formal. Much of it is hidden. It lives in how decisions really get made, how accountability moves between teams, where information is trusted, which platforms shape workflows, and how governance is applied.", "AI does not sit outside that system. It enters it. Then it amplifies it.", "If the system is clear, AI can accelerate useful work. If the system is fragmented, AI can accelerate fragmentation." ] }, { "heading": "The hidden risks", "body": [ "The most common AI adoption risks are not purely technical. They are operating model risks." ], "bullets": [ "No clear owner for the AI-supported outcome.", "Decisions are recommended faster than leaders can govern them.", "Communication noise becomes summarized noise.", "Information sources are stale, duplicated, or untrusted.", "Agents act across fragmented platforms.", "Governance is applied after adoption has already spread." ] }, { "heading": "What leaders should diagnose first", "body": [ "Before scaling AI, leaders should ask what the organization is asking AI to amplify. The fastest path is not to slow adoption. It is to identify the operating-model conditions AI depends on before the technology spreads across workflows." ], "bullets": [ "Who owns the outcome?", "What decisions can AI support, and who remains accountable?", "What information can AI safely use?", "Which platforms will AI touch?", "What governance boundaries are required?", "How will leaders know whether AI improved the operating model?" ] }, { "heading": "The LPM lens", "body": [ "The Large People Model helps leaders diagnose the operating model before AI scales it. It looks across ownership, decisions, communication, information, platforms, governance, and AI amplification as one connected system.", "The point is not to slow AI down. The point is to make AI worth scaling." ] } ], "takeaway": "AI will not fix the operating model. It will reveal it, stress it, and amplify it. Leaders who understand that first will scale AI with more trust, accountability, and measurable value." } }, { "id": "lpm:article:what-is-decision-architecture", "type": "article", "slug": "what-is-decision-architecture", "title": "What is Decision Architecture?", "canonicalUrl": "https://largepeoplemodel.com/insights/what-is-decision-architecture", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-08", "relationships": [ { "type": "related_to", "targetId": "lpm:layer:decision-architecture" }, { "type": "related_to", "targetId": "lpm:metric:decision-latency" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-log" } ], "content": { "excerpt": "Decision Architecture defines how decisions are made, who makes them, what evidence supports them, and how decisions become traceable commitments.", "category": "framework-education", "roles": [ "product-leader", "transformation-leader", "pmo-strategy-leader" ], "problems": [ "decision-latency", "execution-misalignment" ], "tags": [ "decision architecture", "decision rights", "framework education" ], "readTime": "7 min read", "author": "Chad Stewart", "publishedAt": "2026-06-08", "sections": [ { "heading": "The plain-English definition", "body": [ "Decision Architecture is the operating discipline for turning choices into traceable commitments. It defines who decides, what evidence is required, how tradeoffs are recorded, and when a decision should be revisited.", "In a healthy operating model, decisions do not disappear into meetings, slide decks, chat threads, or executive memory. They become durable signals the organization can act on." ] }, { "heading": "Why it matters", "body": [ "When decision architecture is weak, communication increases but commitment does not. Teams repeat debates, escalate normal choices, and lose the rationale behind work already in motion.", "Decision latency then becomes an execution tax. Work waits, reverses, or splits into local interpretations because the organization cannot see who made the choice, what evidence mattered, or what changed after the decision." ] }, { "heading": "What strong decision architecture includes", "body": [ "Leaders should expect a decision system to clarify both authority and memory. A decision is not complete when someone agrees in a meeting. It is complete when the accountable owner, evidence, implications, and communication path are clear." ], "bullets": [ "Named decision owner and decision type.", "Decision rights for approvers, advisors, veto holders, and informed parties.", "Evidence standard for making the decision.", "Rationale and tradeoffs recorded in a durable location.", "Communication path for affected teams.", "Revisit trigger for when the decision should be challenged or updated." ] }, { "heading": "How leaders can measure it", "body": [ "Decision Architecture becomes measurable when leaders track the time, ownership, and traceability of real decisions. The goal is not to make every decision heavy. The goal is to make important decisions clear enough to carry execution." ], "bullets": [ "Decision latency: days from decision need to accountable decision.", "Decision owner coverage: percentage of critical decisions with one named owner.", "Decision reversal rate: percentage of decisions reopened because ownership, evidence, or constraints were unclear.", "Decision lineage completeness: percentage of decisions with rationale, evidence, owner, and communication captured." ] }, { "heading": "Where to apply it first", "body": [ "Start with decisions that repeatedly slow work: prioritization, funding, risk acceptance, platform ownership, AI approval, and cross-functional tradeoffs. These are the places where unclear decision architecture becomes visible fastest.", "Use the Decision Rights Matrix to define authority, then use the Decision Log to make the decision traceable. Once those objects exist, the decision can move through communication, governance, and metrics without being reinvented." ] } ], "takeaway": "A decision that cannot be traced will eventually become rework." } }, { "id": "lpm:article:what-is-communication-architecture", "type": "article", "slug": "what-is-communication-architecture", "title": "What is Communication Architecture?", "canonicalUrl": "https://largepeoplemodel.com/insights/what-is-communication-architecture", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-06-01", "relationships": [ { "type": "related_to", "targetId": "lpm:layer:communication-architecture" }, { "type": "related_to", "targetId": "lpm:metric:communication-load" }, { "type": "related_to", "targetId": "lpm:knowledge-object:meeting-architecture" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-communication-protocol" } ], "content": { "excerpt": "Communication Architecture designs how intent, decisions, context, and commitments move across the enterprise without creating noise.", "category": "framework-education", "roles": [ "transformation-leader", "product-leader", "hr-people-leader" ], "problems": [ "communication-overload", "execution-misalignment" ], "tags": [ "communication architecture", "signal flow", "meetings" ], "readTime": "7 min read", "author": "Chad Stewart", "publishedAt": "2026-06-01", "sections": [ { "heading": "The plain-English definition", "body": [ "Communication Architecture is the signal design of the operating model. It clarifies which messages need to move, who needs them, which channels carry them, and what context must become durable information.", "It is not a meeting policy or a channel guide by itself. It is the design of how intent, decisions, context, risks, and commitments travel through the organization." ] }, { "heading": "Why more communication often creates less clarity", "body": [ "Most organizations respond to uncertainty by adding meetings, updates, channels, and escalation calls. That creates motion, but not always signal.", "More communication does not create more clarity unless the organization knows which communication creates decisions, commitments, escalation, or operating memory." ] }, { "heading": "What strong communication architecture includes", "body": [ "A strong communication system separates signal from noise. Leaders should be able to see which channels are used for decisions, which are used for awareness, which are used for escalation, and which produce durable records." ], "bullets": [ "Channel purpose for each major communication surface.", "Meeting intent tied to decisions, learning, escalation, or operating review.", "Decision communication rules for affected teams.", "Escalation paths that clarify when a signal needs leadership attention.", "Context-to-record rules for what must move from conversation into durable knowledge.", "Feedback loops for detecting noise, delay, and misunderstanding." ] }, { "heading": "How leaders can measure it", "body": [ "Communication Architecture becomes measurable when leaders track whether communication improves decisions and execution. The question is not how much people communicate. The question is whether communication reduces ambiguity." ], "bullets": [ "Communication load: hours or touchpoints required to align around a decision or outcome.", "Meeting density: recurring meeting hours per role, outcome, or value stream.", "Message-to-decision ratio: volume of discussion required before an accountable decision is made.", "Rework from unclear communication: work repeated because context, owner, or decision intent was misunderstood." ] }, { "heading": "Where to apply it first", "body": [ "Start where teams already feel the pain: executive operating reviews, portfolio decisions, transformation governance, incident escalation, product planning, and AI approval forums.", "Use Meeting Architecture to clarify the purpose of recurring forums, then connect those forums to decision logs, ownership maps, and source-of-truth records. Communication should become a bridge between decisions and execution, not a substitute for either." ] } ], "takeaway": "Communication becomes valuable when it changes shared understanding, decision quality, or action." } }, { "id": "lpm:article:what-is-information-ecology", "type": "article", "slug": "what-is-information-ecology", "title": "What is Information Ecology?", "canonicalUrl": "https://largepeoplemodel.com/insights/what-is-information-ecology", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-05-25", "relationships": [ { "type": "related_to", "targetId": "lpm:layer:information-ecology" }, { "type": "related_to", "targetId": "lpm:metric:information-trust-score" }, { "type": "related_to", "targetId": "lpm:knowledge-object:source-of-truth-map" }, { "type": "related_to", "targetId": "lpm:knowledge-object:information-ownership-register" } ], "content": { "excerpt": "Information Ecology defines how trusted information is created, owned, refreshed, accessed, and used to support decisions.", "category": "framework-education", "roles": [ "cio-cto", "enterprise-architect", "ai-leader" ], "problems": [ "information-distrust", "ai-adoption-failure" ], "tags": [ "information ecology", "source of truth", "trust" ], "readTime": "7 min read", "author": "Chad Stewart", "publishedAt": "2026-05-25", "sections": [ { "heading": "The plain-English definition", "body": [ "Information Ecology is the environment of sources, owners, freshness, permissions, and trust that determines whether information can support decisions, workflows, governance, and AI.", "The word ecology matters. Information does not live in one table, one report, or one repository. It moves through people, systems, processes, incentives, and governance routines." ] }, { "heading": "Why it matters", "body": [ "AI and leaders both depend on information they can trust. When information is stale, duplicated, or unowned, every downstream layer inherits the weakness.", "Bad information ecology creates decision friction, platform workarounds, governance blind spots, and AI hallucination risk. The problem is not only data quality. It is operating-model quality." ] }, { "heading": "What strong information ecology includes", "body": [ "A strong information ecology makes critical knowledge findable, owned, fresh, and trusted. Leaders should know which sources are authoritative, who maintains them, how conflicts are resolved, and where lineage exists." ], "bullets": [ "Named owners for critical information domains.", "Source-of-truth maps for high-value operating data.", "Freshness expectations and review cadence.", "Lineage from source to decision, report, workflow, or AI use case.", "Conflict resolution rules when sources disagree.", "Access and permission boundaries that match risk." ] }, { "heading": "How leaders can measure it", "body": [ "Information Ecology becomes measurable when leaders stop asking whether data exists and start asking whether trusted information can be used confidently in decisions and automation." ], "bullets": [ "Source-of-truth coverage: percentage of critical domains with an authoritative source.", "Information freshness: percentage of critical sources reviewed within their expected cadence.", "Information trust score: user confidence weighted by ownership, freshness, and conflict patterns.", "Lineage completeness: percentage of critical information assets traceable to source and owner.", "Information conflict rate: frequency of contradictory sources for the same operating fact." ] }, { "heading": "Where to apply it first", "body": [ "Start with information that drives decisions or AI: customer truth, employee truth, product truth, financial truth, risk truth, workflow status, platform ownership, and policy boundaries.", "Use the Source of Truth Map to identify authoritative sources, then pair it with the Information Ownership Register so each critical domain has a named steward. That lineage makes the information usable for governance, metrics, and AI." ] } ], "takeaway": "AI-ready information is an operating-model condition, not only a data condition." } }, { "id": "lpm:article:what-is-ai-amplification", "type": "article", "slug": "what-is-ai-amplification", "title": "What is AI Amplification?", "canonicalUrl": "https://largepeoplemodel.com/insights/what-is-ai-amplification", "version": "1.0.0", "status": "published", "source": "large-people-model-website", "publisher": "Lapemo Systems LLC", "lastUpdated": "2026-05-18", "relationships": [ { "type": "related_to", "targetId": "lpm:layer:ai-amplification" }, { "type": "related_to", "targetId": "lpm:metric:ai-amplification-risk" }, { "type": "related_to", "targetId": "lpm:knowledge-object:ai-use-case-governance-register" }, { "type": "related_to", "targetId": "lpm:knowledge-object:human-in-the-loop-model" } ], "content": { "excerpt": "AI Amplification determines whether AI improves the operating model or accelerates the dysfunction already inside it.", "category": "framework-education", "roles": [ "ai-leader", "cio-cto", "ceo-coo" ], "problems": [ "ai-adoption-failure", "governance-drag" ], "tags": [ "AI amplification", "AI readiness", "agents" ], "readTime": "7 min read", "author": "Chad Stewart", "publishedAt": "2026-05-18", "sections": [ { "heading": "The plain-English definition", "body": [ "AI Amplification is the LPM layer that asks what AI will make stronger. It evaluates whether AI is scaling clarity, trust, and accountability or accelerating ambiguity, fragmentation, and risk.", "It is the final layer because AI depends on the layers underneath it. Ownership, decisions, communication, information, platforms, and governance all become inputs to what AI can safely do." ] }, { "heading": "Why it matters", "body": [ "The more capable AI becomes, the more important the underlying operating model becomes. AI does not erase ownership, decision rights, information quality, platform structure, or governance.", "When those layers are clear, AI can help people move faster with better context. When they are weak, AI often makes the weakness travel faster and farther." ] }, { "heading": "What strong AI amplification includes", "body": [ "Strong AI amplification starts with operating boundaries. Leaders need to know which outcomes AI supports, what decisions it can influence, what information it can use, and where a human remains accountable." ], "bullets": [ "Named human owner for each AI-supported outcome.", "Defined autonomy level for each AI workflow or agent.", "Approved information sources and permission boundaries.", "Decision rights for recommendations, approvals, overrides, and exceptions.", "Audit trail for important AI actions and recommendations.", "Governance gates tied to risk, confidence, and impact." ] }, { "heading": "How leaders can measure it", "body": [ "AI Amplification becomes measurable when leaders connect adoption metrics to operating-model conditions. Usage alone does not prove readiness. The question is whether AI is improving outcomes without creating hidden risk." ], "bullets": [ "AI use case owner coverage: percentage of AI use cases with a named accountable owner.", "Human-in-the-loop clarity: percentage of AI workflows with defined review, escalation, and override rules.", "Agent action auditability: percentage of consequential actions traceable to source, policy, and owner.", "AI adoption readiness score: composite view of ownership, information trust, governance, and platform readiness.", "AI amplification risk: likelihood that AI will scale unclear ownership, untrusted information, or weak controls." ] }, { "heading": "Where to apply it first", "body": [ "Start with AI use cases that touch decisions, customers, employees, financial exposure, regulatory risk, or cross-platform workflows. These are the places where AI can create value and risk quickly.", "Use the AI Use Case Governance Register to map ownership and risk, then use the Human-in-the-Loop Model and Agent Accountability Checklist to define supervision before scale." ] } ], "takeaway": "The AI question is not only what the model can do. It is what the enterprise is ready to amplify." } } ] }