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Large People ModelHuman Operating Architecture
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The seven-layer framework

Read the operating model from ownership outward.

The Large People Model organizes the conditions behind execution into seven connected layers. This page explains the job of each layer, what fails when it is weak, and what to inspect next.

See the complete system

Use this as the overview. Open a full layer page when you need definitions, methods, tools, metrics, and deeper guidance.

The complete system

Seven layers. One dependency system.

The rings make the dependency visible. Start with human ownership at the center and move outward through the structures that carry, control, and eventually amplify work.

Hover over the map or its legend to isolate a layer. The visual is an orientation device, not a maturity score: outer does not mean better, and no layer can safely compensate for a weak foundation beneath it.

Large People Model framework growth ringsSeven concentric half-rings rising from a shared Large People Model core, with a legend naming each framework layer.LPM1234567Large People ModelContinue into the sections below to inspect each layer in depth.Seven Layers1Identity & Incentives2Decision Architecture3Communication Architecture4Information Ecology5Platform Structure6Governance Architecture7AI Amplification
01

Begin at the center

Ownership is the foundation. If nobody owns the outcome, every outer layer inherits ambiguity.

02

Read in dependency order

Each layer carries the conditions created before it. A downstream fix cannot repair a missing upstream foundation.

03

Treat AI as amplification

AI is the outer layer because it magnifies the quality, clarity, and dysfunction of the system underneath it.

Layer 01

Ownership

Identity & Incentives

Primary question

Who owns the outcome?

Purpose in the system

Clarifies who owns outcomes, how accountability is assigned, and whether incentives reinforce the behavior the enterprise needs.

Ownership determines who can make decisions, carry accountability, and govern outcomes across the rest of the operating model.

When this layer is weak

Accountability gaps and misaligned incentives prevent execution from scaling.

  • Everyone participates but no one is accountable.
  • Teams optimize locally instead of enterprise-wide.
  • AI initiatives launch without clear business owners.

Questions to inspect

  1. 01Who owns the outcome?
  2. 02Who can approve, pause, or redirect the work?
  3. 03Are incentives aligned to the enterprise goal?

Evidence to look for

Working artifacts

  • Ownership map
  • Decision rights matrix
  • Incentive alignment checklist

Measures

  • Ownership clarity score
  • Decision owner coverage
  • Initiative owner coverage

AI implication

AI scales activity without accountability when ownership is unclear.

Explore the full layer

Layer 02

Decisions

Decision Architecture

Primary question

How are decisions made and traced?

Purpose in the system

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

Decisions create commitments. Weak decisions create communication overload and execution drift downstream.

When this layer is weak

Slow, unclear, or reversible decisions create execution drag.

  • Decisions are revisited repeatedly.
  • Escalations replace ownership.
  • Teams wait for alignment meetings instead of moving.

Questions to inspect

  1. 01What decisions are slowing execution?
  2. 02Who has the right to decide?
  3. 03What evidence supports the decision?

Evidence to look for

Working artifacts

  • Decision log
  • Decision rights model
  • Escalation map

Measures

  • Decision latency
  • Decision aging
  • Decision reversal rate

AI implication

AI creates recommendations faster than the organization can responsibly decide.

Explore the full layer

Layer 03

Communication

Communication Architecture

Primary question

How does communication create shared understanding?

Purpose in the system

Designs how information, intent, decisions, and commitments move across teams without creating noise or confusion.

Communication moves decisions, context, risks, and commitments through the enterprise.

When this layer is weak

Communication overload creates misalignment, rework, and hidden coordination cost.

  • Too many meetings with unclear decisions.
  • Important context is buried in chat.
  • Teams communicate activity instead of commitment.

Questions to inspect

  1. 01What needs to be communicated, to whom, and why?
  2. 02Which channels carry decisions versus discussion?
  3. 03Where does communication fail to create shared understanding?

Evidence to look for

Working artifacts

  • Communication map
  • Meeting architecture
  • Decision communication protocol

Measures

  • Communication load
  • Meeting density
  • Message-to-decision ratio

AI implication

AI summarizes noise and makes confusion appear organized when communication architecture is weak.

Explore the full layer

Layer 04

Information

Information Ecology

Primary question

What information can leaders trust?

Purpose in the system

Defines how trusted information is created, maintained, accessed, refreshed, and used across the enterprise.

Information turns communication into reusable operating memory, evidence, and decision context.

When this layer is weak

Leaders cannot make confident decisions when information is duplicated, stale, conflicting, or hard to trust.

  • Multiple sources of truth exist for the same topic.
  • Reports conflict across teams.
  • Decisions rely on stale or undocumented information.

Questions to inspect

  1. 01What information do leaders trust?
  2. 02Where is the source of truth?
  3. 03Who owns freshness and quality?

Evidence to look for

Working artifacts

  • Source-of-truth map
  • Information ownership register
  • Data lineage map

Measures

  • Source-of-truth coverage
  • Information freshness
  • Information trust score

AI implication

AI accelerates the spread of outdated or conflicting information when trust and ownership are weak.

Explore the full layer

Layer 05

Platforms

Platform Structure

Primary question

Where does work actually move?

Purpose in the system

Maps how tools, systems, workflows, and integrations shape how work actually moves through the enterprise.

Platforms operationalize work, decisions, information, and governance through tools and workflows.

When this layer is weak

Work fragments across systems, creating manual handoffs, duplicate effort, and poor visibility.

  • Teams use different tools for the same workflow.
  • Work moves through manual handoffs.
  • Platform ownership is unclear.

Questions to inspect

  1. 01Which platforms carry critical work?
  2. 02Where does work leave one system and enter another?
  3. 03Who owns the platform workflow?

Evidence to look for

Working artifacts

  • Platform map
  • Workflow inventory
  • Integration map

Measures

  • Tool duplication count
  • Workflow fragmentation score
  • Integration coverage

AI implication

Governed agents act across fragmented systems without reliable operating boundaries when platform structure is weak.

Explore the full layer

Layer 06

Governance

Governance Architecture

Primary question

How is risk controlled without freezing execution?

Purpose in the system

Defines the controls, policies, review loops, and decision boundaries that keep execution safe without slowing it unnecessarily.

Governance sets the operating boundaries for safe, scalable, and trusted execution.

When this layer is weak

Governance is either too slow to support execution or too weak to manage risk.

  • Approvals are unclear or excessive.
  • Policy exceptions are hard to track.
  • Governance happens after work is already in motion.

Questions to inspect

  1. 01What must be governed?
  2. 02Who approves exceptions?
  3. 03Which controls are preventive versus reactive?

Evidence to look for

Working artifacts

  • Governance decision tree
  • Control map
  • Risk acceptance register

Measures

  • Approval cycle time
  • Policy exception rate
  • Control coverage

AI implication

AI scales faster than oversight, auditability, and risk ownership when governance is weak.

Explore the full layer

Layer 07

AI Amplification

AI Amplification

Primary question

Will AI amplify clarity or chaos?

Purpose in the system

Determines whether AI improves the operating model or amplifies the dysfunction already inside it.

AI touches every layer and amplifies the quality, clarity, or dysfunction of the system underneath it.

When this layer is weak

AI pilots create activity but fail to become trusted enterprise capability.

  • Many AI pilots, few scaled outcomes.
  • AI use cases lack owners, controls, and adoption paths.
  • Agents automate fragmented workflows.

Questions to inspect

  1. 01What is AI amplifying?
  2. 02Who owns the AI-supported outcome?
  3. 03What decisions can AI support versus make?

Evidence to look for

Working artifacts

  • AI readiness assessment
  • Agent accountability checklist
  • Human-in-the-loop model

Measures

  • AI readiness score
  • AI use case owner coverage
  • Human-in-the-loop clarity

AI implication

AI makes the hidden operating model more powerful before it is understood.

Explore the full layer

Turn the overview into a diagnosis

Find the weakest foundation before selecting the intervention.

The three-minute readiness check scores all seven layers and points to the operating condition that deserves attention first.

Take the readiness check

Continue into the system

Use only the depth your current question requires.

The layer pages carry the detailed guidance. These references connect the framework to its laws, maturity model, implementation path, tools, evidence, and learning system.

Return to this overview when you need to see the dependency order again. Move into a reference when you need to define, measure, or change a specific operating condition.