{ "schemaVersion": "1.0.0", "releaseVersion": "1.0.0", "releasedAt": "2026-08-04", "publisher": { "name": "Lapemo Systems LLC", "brand": "Large People Model", "url": "https://largepeoplemodel.com" }, "canonicalUrl": "https://largepeoplemodel.com/metrics.json", "canon": { "package": "@lpm/canon", "version": "0.5.0", "sourceRepository": "https://github.com/LayerZero69/lpm-canon.git", "sourceCommit": "d4d36e2fe1d1b0cebf06bbcebfff5806299e21e2" }, "objects": [ { "id": "lpm:metric:ownership-clarity-score", "type": "metric", "slug": "ownership-clarity-score", "title": "Ownership clarity score", "canonicalUrl": "https://largepeoplemodel.com/metrics/ownership-clarity-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" }, { "type": "related_to", "targetId": "lpm:knowledge-object:decision-rights-matrix" } ], "content": { "layer": "identity-and-incentives", "supportStatus": "platform-backed-supported", "definition": "The share of accountable entities that resolve to exactly one named, active, undisputed owner.", "whatItReveals": "Whether critical work has clear ownership or depends on informal consensus.", "whyItMatters": "This metric helps leaders see whether the ownership layer is healthy enough to support execution and AI readiness.", "manualAssessment": "Review outcomes, workflows, decisions, and AI use cases. 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 for transformation work; quarterly for enterprise baseline.", "workedExample": { "scenario": "Operating review across priority outcomes, workflows, systems, and decision types.", "inputs": [ { "label": "Total scoped items", "value": "84" }, { "label": "Items with one active undisputed owner", "value": "61" } ], "calculation": "61 / 84 * 100", "result": "72.6%", "interpretation": "The operating model has meaningful ownership ambiguity.", "recommendedAction": "Assign owners for the highest-risk gaps before expanding the workstream." }, "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 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 owner / total consequential decision types) * 100", "numerator": "Consequential decision types with one named accountable owner", "denominator": "All consequential decision types reviewed", "unit": "percent", "direction": "higher-is-better" }, "requiredInputs": [ "Decision type list", "Named owner", "Approver", "Escalation path" ], "dataSources": [ "Decision Rights Matrix", "Operating review notes", "Governance forum charter" ], "collectionCadence": "Quarterly, or before major transformation and AI governance changes.", "workedExample": { "scenario": "Transformation team reviews critical portfolio, budget, product, risk, and AI decisions.", "inputs": [ { "label": "Decision types reviewed", "value": "32" }, { "label": "Decision types with one owner", "value": "21" } ], "calculation": "21 / 32 * 100", "result": "65.6%", "interpretation": "Decision ownership is below the level needed for fast execution.", "recommendedAction": "Clarify owners and escalation paths for the 11 uncovered 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" ] } } ] }