Large People ModelHuman Operating Architecture

Template & Working Tool · LPM Knowledge Object

Evidence Checklist

A checklist for confirming whether decisions, controls, owners, and AI outputs are backed by usable evidence.

Checklistv1.0.0DecisionsGovernance

Problem it solves

Slow, unclear, or reversible decisions create execution drag.

Who should use it

Accountable leaders, control owners, and implementation teams

Estimated time

30–45 minutes for a first working session

Three-Step Quick Start

1Select a decision, control, or AI workflow.
2Check evidence quality, ownership, freshness, and location.
3Assign remediation for missing or weak evidence.

Open the public PDF

The PDF action is direct and public. All available packaged formats are also public and require no registration.

Object Overview

What this object is

Evidence Checklist is a reusable LPM knowledge object that helps organizations teams distinguish opinion, stale artifacts, and incomplete records from evidence that can support execution and governance. It gives teams a structured way to make decisions visible, owned, and reviewable.

Why it matters

As companies scale AI, weak operating-model structures become amplified. This object helps prevent ai creates recommendations faster than the organization can responsibly decide. by defining control, evidence, and approval boundaries.

Layer Alignment

Where it fits in LPM

Primary LPM layer

Decision Architecture

Defines how decisions are made, who makes them, what information supports them, and how decisions create traceable commitments.

Supporting layers

Governance

Why it belongs here

This object sits in Decisions because it turns decisions into a concrete artifact with owners, evidence, review cadence, and action paths.

Weakness it exposes

AI creates recommendations faster than the organization can responsibly decide.

Usage

How to use it

1Select the business area, workflow, platform, or AI initiative being assessed.
2Identify the accountable owner and required participants.
3Complete the working DOCX version with the team.
4Use the PDF as the reference guide.
5Capture decisions, gaps, risks, and owners.
6Convert outputs into backlog items, governance actions, or Lapemo onboarding inputs.
7Review on the recommended cadence: Monthly or before approvals.

File Formats

Which file should you use?

PDF

Executive/reference version

Best for education, pre-read, sharing, and workshops.

DOCX

Editable working artifact

Best for facilitation, implementation, and client or internal completion.

Markdown

Website/source version

Best for publishing, documentation, and content reuse.

JSON

Structured knowledge object schema

Best for future Lapemo ingestion, scoring, validation, prompts, and workflows.

Outputs

What the organization should expect

Clearer ownership

Better decision traceability

Reduced ambiguity

Evidence-backed conversations

Better AI readiness

Better handoff into Lapemo later

Evidence readiness view

Evidence gaps

Follow-up actions

Advanced specification, company-size variants, and future product notes

Company Scale

How this changes by company size

500+ employees

Use this to create baseline clarity.

Focus on named owners, simple governance, and reducing informal workarounds.

Included in this object.

5,000+ employees

Use this to standardize across functions and platforms.

Focus on cross-functional ownership, decision rights, evidence, and repeatability.

Included in this object.

10,000+ employees

Use this to create enterprise control and reviewability.

Focus on federation, risk tiers, governance bodies, AI boundaries, and auditability.

Included in this object.

Artifact Content

Source artifact

The full artifact content below is rendered from the Markdown source packaged with Evidence Checklist.

A versioned knowledge object for defining the evidence required before decisions, AI initiatives, controls, recommendations, escalations, and operating-model claims are trusted.

What this object does

The Evidence Checklist turns evidence from scattered documents, dashboards, summaries, and meeting claims into a reusable operating object. It defines what evidence is required, who owns it, where it came from, how fresh it is, how AI touched it, and whether it can be trusted for the decision or control at hand.

Why this matters for AI scaling

AI can make weak evidence look polished. It can summarize stale information, classify bad data, and recommend action from incomplete sources. This checklist forces evidence to stay tied to source, owner, freshness, confidence, lineage, human review, and governance context.

Core principles

Principle	Meaning
Evidence before confidence	Confidence should come from traceable evidence, not status updates, persuasive summaries, or executive optimism.
The claim determines the evidence	Every decision, AI recommendation, risk, control, or transformation claim should state what evidence is required to prove it.
Source systems matter	Evidence should connect to the system, data object, record, owner, timestamp, and lineage path that produced it.
Freshness is a control	Old evidence can be worse than no evidence when decisions, systems, people, or AI behavior have changed.
AI output is not evidence by itself	AI can summarize, classify, and reason over evidence, but final trust depends on source quality and human accountability.
Material decisions need replayability	A future reviewer should be able to see what was known, what was missing, what was accepted, and why the decision was made.
Evidence gaps are operating-model signals	Missing, stale, conflicting, or ownerless evidence exposes gaps across information, ownership, governance, and platform structure.

Canonical fields

Field	What it captures	Required?
Evidence ID	Unique identifier for the evidence item or evidence pack	Yes
Supported claim	Decision, risk, control, AI recommendation, initiative outcome, dependency, metric, or operating-model assertion being supported	Yes
Evidence type	Metric, report, policy, decision record, system log, customer signal, employee signal, financial data, audit evidence, AI output, experiment result, data lineage, vendor record, or SME attestation	Yes
Source system / location	Where the evidence lives: Jira, ServiceNow, Salesforce, Workday, Snowflake, Power BI, Confluence, Teams, Slack, GRC tool, model registry, repository, or other system	Yes
Evidence owner	Person or role accountable for evidence accuracy, freshness, and interpretation	Yes
Source owner	System, data, process, control, or platform owner accountable for the underlying source	Yes
Date captured	Date the evidence was pulled, observed, exported, generated, or approved	Yes
Freshness window	How long the evidence can be trusted before revalidation is required	Yes
Confidence rating	Low, medium, high, or verified based on source quality, lineage, recency, completeness, and review state	Yes
Lineage / provenance	How the evidence was created, transformed, summarized, or moved before use	Required for material decisions
Data classification	Public, internal, confidential, restricted, regulated, customer, employee, financial, model, or legal-sensitive	Yes
AI involvement	None, AI summarized, AI classified, AI recommended, AI generated, AI executed, or autonomous AI involved	Yes
Human reviewer	Named person or role that validated evidence when material, risk-bearing, or AI-assisted	Required when material
Contradictory evidence	Known evidence that conflicts with or weakens the claim	Required if known
Evidence gap	Missing evidence, stale evidence, unclear owner, unknown source, broken lineage, or unresolved conflict	Required if present
Decision / object link	Link to Decision Log, Decision Rights Matrix, AI Initiative Owner Register, Escalation Map, Ownership Map, control record, or initiative record	Required when connected
Review status	Draft, submitted, reviewed, verified, rejected, stale, superseded, or archived	Yes
Review date	Date when evidence must be reviewed, refreshed, replaced, or retired	Yes
Version	Artifact version, owner, last reviewed date, and change history	Yes

Scoring logic

Score	Meaning
0	Evidence is informal, anecdotal, stale, undocumented, or dependent on individual interpretation.
1	Evidence exists but source ownership, recency, lineage, confidence, or review status is unclear.
2	Evidence is documented but not consistently tied to claims, decisions, AI output, or governance controls.
3	Evidence is source-linked, owner-backed, fresh, reviewed, confidence-rated, and connected to the decision or operating object.
4	Evidence is governed, lineage-aware, AI-readable, auditable, automatically refreshed or flagged, and connected to live operating-model control.

Evidence health score: average of source quality, owner clarity, freshness, completeness, lineage, contradiction handling, AI review, sensitivity control, and replayability.

Evidence types

Type	Use	Common source
Metric / KPI	Performance, cycle time, cost, quality, productivity, adoption, value, or risk metric	Dashboard, BI tool, warehouse, product analytics
Decision evidence	Facts used to approve, reject, defer, or supersede a decision	Decision Log, meeting record, product brief, governance forum
Control evidence	Proof that policy, approval, risk review, security, privacy, audit, or compliance control occurred	GRC, audit tool, risk register, workflow system
System evidence	Logs, tickets, changes, access records, workflow state, incident records, or integration events	Jira, ServiceNow, GitHub, CI/CD, IAM, observability
Data evidence	Source data, lineage, quality score, definition, data owner approval, or reconciliation result	Warehouse, catalog, BI semantic layer, data quality tool
People evidence	Role, ownership, staffing, readiness, behavior, adoption, training, or incentive evidence	Workday, LMS, survey, org system, enablement record
Customer evidence	Customer feedback, support trend, churn, NPS, satisfaction, contract issue, or external commitment	CRM, support platform, research repository
AI evidence	Prompt, model output, model card, evaluation result, human review, agent action log, or boundary test	Model registry, AI platform, eval harness, agent log
SME attestation	Named expert judgment when system evidence is incomplete or not yet instrumented	Attestation record with owner and expiration date

Quality dimensions

Dimension	Question	Pass condition
Source quality	Is the evidence pulled from an authoritative system or from an informal summary?	Authoritative source identified
Owner quality	Is there a named evidence owner and source owner?	Both owners named
Freshness	Is the evidence recent enough for the decision or risk tier?	Freshness window active
Completeness	Does the evidence cover the whole claim, not just the favorable part?	No material missing fields
Lineage	Can the evidence path be traced from source to use?	Provenance documented
Contradiction check	Has conflicting evidence been surfaced and resolved?	Known contradictions captured
AI review	Was AI-generated or AI-summarized evidence reviewed by a human when material?	Human reviewer named
Sensitivity control	Is data classification and access handling clear?	Classification recorded
Replayability	Can a future reviewer understand what was known and why it was trusted?	Decision/object link present

Version for 500+ employee companies

Area	Typical condition	LPM standard
Primary problem	Evidence lives in people's heads, decks, Slack/Teams threads, spreadsheets, and founder/operator memory	Create a simple evidence standard before AI pilots and scaling decisions become opinion-driven
Minimum evidence pack	Claim, source, owner, date, confidence, decision link, evidence gap, next review date	Keep it lightweight and repeatable
Owner standard	Every evidence item has one evidence owner and one source owner	No ownerless evidence
AI standard	AI output can support analysis but cannot be the only evidence for customer, employee, financial, or control decisions	Human validation required
Review rhythm	Review before major decisions, AI pilots, funding approvals, operating-model changes, and quarterly planning	Do not overbuild governance too early
Website artifact use	Download as a workshop checklist and AI readiness self-assessment	Simple, founder-to-executive friendly

Version for 5,000+ employee companies

Area	Typical condition	LPM standard
Primary problem	Evidence is fragmented across functions, platforms, BI tools, vendors, PMO rituals, risk teams, and transformation programs	Create cross-functional evidence standards tied to decisions, owners, and governance
Minimum evidence pack	Claim, source system, owner, lineage, freshness, confidence, data classification, AI involvement, reviewer, contradictory evidence, decision/object link	Make evidence reviewable across functions
Owner standard	Evidence owner, source owner, decision owner, control owner, and AI boundary owner where relevant	Separate evidence ownership from decision authority
AI standard	AI-summarized or AI-recommended evidence requires provenance, reviewer, source quality score, and review status	AI cannot hide weak source data
Review rhythm	Review at portfolio, governance, risk, architecture, AI initiative, and value realization checkpoints	Connect to operating rhythms
Website artifact use	Download as an enterprise operating template and Lapemo onboarding object	Prepared for structured ingestion

Version for 10,000+ employee companies

Area	Typical condition	LPM standard
Primary problem	Evidence conflicts across business units, regions, legal entities, systems of record, data domains, governance forums, and AI platforms	Federate evidence capture while centralizing standards, lineage, controls, and replayability
Minimum evidence pack	Full evidence object with claim, authority source, lineage, owner chain, confidence score, sensitivity, control mapping, AI trace, contradictions, retention, and supersession rules	Enterprise-grade evidence control
Owner standard	Evidence owner, source owner, data owner, system owner, control owner, legal/entity owner, decision owner, AI boundary owner, and value owner where needed	Complexity requires formal role separation
AI standard	AI use requires prompt/model trace, evaluation result, human review, automated monitoring where available, and stop-control evidence for autonomous workflows	AI evidence must be auditable and revocable
Review rhythm	Review through enterprise governance, risk, audit, regulatory, portfolio, model-risk, and operating-model control cadences	Material evidence must be replayable
Website artifact use	Download as an executive guide and machine-readable control object for large enterprises	Designed for federation and central control

Evidence checklist worksheet

Field	Entry	Guidance
Evidence ID		Unique evidence item or evidence pack identifier
Supported claim		What decision, risk, AI recommendation, control, metric, or operating claim does this support?
Evidence type		Metric, decision record, control evidence, system log, data evidence, AI output, SME attestation, etc.
Source system / location		Where does the evidence live?
Evidence owner		Who owns accuracy and interpretation?
Source owner		Who owns the source system, data, process, or control?
Date captured		When was it captured or approved?
Freshness window		How long can it be trusted?
Confidence rating		Low, medium, high, verified
Lineage / provenance		How did it move from source to use?
Data classification		Public, internal, confidential, restricted, regulated, customer, employee, financial, model, legal-sensitive
AI involvement		None, summarized, classified, recommended, generated, executed, autonomous
Human reviewer		Required when material or AI-assisted
Contradictory evidence		What evidence challenges the claim?
Evidence gap		What is missing, stale, disputed, or ownerless?
Decision / object link		Decision Log, AI Initiative Owner Register, Escalation Map, control record, Ownership Map, etc.
Review status		Draft, submitted, reviewed, verified, rejected, stale, superseded, archived
Review date		When must this be refreshed or retired?

AI involvement rules

Level	What it means	Required control
Level 0 - No AI	Evidence is human-created or system-generated without AI interpretation	Normal evidence owner and source owner required
Level 1 - AI summarizes evidence	AI compresses source content into a summary	Source links, reviewer, and summary limitations required
Level 2 - AI classifies evidence	AI assigns category, severity, confidence, ownership, or risk tier	Classification logic and human spot-check required
Level 3 - AI recommends from evidence	AI recommends a decision, priority, risk response, or escalation path	Decision owner and reviewer required before material action
Level 4 - AI generates evidence artifact	AI creates a brief, decision pack, control memo, or evidence synthesis	All claims must be tied to sources and reviewed
Level 5 - AI acts on evidence	AI triggers workflow, routes work, changes state, blocks work, or executes an action	AI boundary owner, stop path, audit trail, and control owner required

Validation rules

Condition	System response	Reason
No supported claim	Block evidence record approval	Evidence must support something specific
No source system or location	Mark low confidence	Evidence without source is weak
No evidence owner	Block material use	Evidence needs accountable interpretation
No source owner	Flag source-governance gap	Underlying source must be accountable
Date captured missing	Require timestamp	Freshness cannot be evaluated
Freshness window expired	Mark stale and require refresh	Old evidence may mislead decisions
Low confidence used for material decision	Require reviewer or alternate evidence	Material decisions need stronger evidence
AI involved but no human reviewer	Block material use	AI-assisted evidence needs accountability
Restricted data without classification	Escalate to control owner	Sensitive data must be governed
Contradictory evidence unresolved	Flag decision risk	Conflicting evidence must be resolved or explicitly accepted
Decision made but no evidence link	Require Decision Log update	Decisions must be replayable
Repeated evidence gap	Create operating-model correction	Recurring gaps point to information ecology or ownership failure

Mapping rules

Target object	Mapping
Ownership Map	Evidence owner, source owner, data owner, system owner, control owner, and AI boundary owner should map to named ownership.
Decision Rights Model	Materiality of evidence determines who can accept weak evidence, approve exceptions, or require more proof.
Decision Rights Matrix	Decision evidence maps to decider, recommender, consulted, informed, implementation owner, and control owner.
Decision Log	Every material decision should link to the evidence that supported it and identify what evidence was missing.
Escalation Map	Stale, missing, contradictory, ownerless, or AI-risk evidence can trigger escalation.
AI Initiative Owner Register	AI initiatives must define evidence for value, risk, model behavior, control readiness, and human review.
Incentive Alignment Checklist	Evidence gaps often reveal teams are rewarded for activity, output, or optics instead of verified outcomes.
Information Ecology	Evidence quality is a direct signal of information health, lineage, trust, and source discipline.
Platform Structure	Evidence should trace to systems, workflows, repositories, observability, and BI/control platforms.
Governance Architecture	Control evidence, audit evidence, privacy evidence, security evidence, and model-risk evidence map to governance owners.

AI prompts

Assess whether this evidence is sufficient to support the stated decision, risk, control, AI recommendation, or operating-model claim.
Classify the evidence by source quality, freshness, owner clarity, lineage, sensitivity, confidence, AI involvement, and review status.
Identify missing evidence, stale evidence, contradictory evidence, weak-source evidence, and ownerless evidence.
Generate an executive-ready evidence summary with claim, source, confidence, gaps, contradictions, reviewer, and next action.
Determine whether this evidence requires a Decision Log entry, Escalation Map trigger, AI Initiative Owner Register update, or governance review.
Detect when AI is being treated as evidence instead of a tool for summarizing or reasoning over evidence.
Generate the correct checklist version for a 500+, 5,000+, or 10,000+ employee company.

Render targets

Output	Use
Word document	Workshop, consulting, operating-model design, internal enablement, website download
PDF guide	Executive education, AI readiness briefing, governance design, transformation playbook
Website page	Downloadable resource and educational landing page
Interactive form	Guided evidence intake and quality scoring
CSV import	Bulk import of evidence items, owners, source systems, classifications, and review status
JSON object	Lapemo ingestion, scoring, validation, and mapping rules
In-app workflow	Live evidence health, decision evidence packs, AI evidence review, and stale evidence alerts

Review triggers

Company crosses 500, 5,000, or 10,000+ employees
New AI initiative, agent, model, automation, or decision-support workflow is introduced
Major decision, escalation, audit finding, incident, or governance exception occurs
Source system, BI tool, data warehouse, GRC tool, workflow platform, or model registry changes
Evidence owner, source owner, data owner, control owner, or AI boundary owner changes
Evidence becomes stale, contradictory, incomplete, disputed, or unsupported by source lineage
Regulatory, legal, privacy, security, financial, customer, or employee-impacting requirement changes

Website positioning copy

A reusable LPM method for defining the evidence required before decisions, AI initiatives, controls, recommendations, escalations, and operating-model claims are trusted.

Future Lapemo Use

The JSON schema turns evidence checklist into software.

Lapemo can use this knowledge object as a guided workflow, scoring model, evidence record, governance input, and operating intelligence object. The schema is public for inspection and evaluation; production ingestion and governed execution remain separate product capabilities.