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Large People ModelHuman Operating Architecture
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AI Readiness

AI is ready only when the workflow beneath it is ready.

Evaluate one real workflow across ownership, decisions, communication, information, platforms, governance, and AI boundaries. The weakest foundation limits what is safe to scale.

Take the 3-Minute DiagnosticUse the 30-Day Guide

The diagnostic is directional and self-reported. It does not certify an AI system, verify evidence, or replace a use-case-specific risk review.

Readiness dependency

The Seven Layers beneath one use case

  1. Identity & IncentivesClarify who owns the outcome.01
  2. Decision ArchitectureDefine what AI can influence, recommend, or decide.02
  3. Communication ArchitectureTurn communication into traceable operating context.03
  4. Information EcologyMake information trustworthy enough for AI to use.04
  5. Platform StructurePut AI where work actually happens.05
  6. Governance ArchitectureBuild governance into the flow of work.06
  7. AI AmplificationDecide where AI should assist, advise, automate, monitor, or escalate.07

The Readiness Test

Three questions before any discussion of scale.

These are not another maturity model. They are a plain-language test of whether the operating conditions around one use case are visible enough to examine.

You cannot govern what you have not owned. You cannot automate what you have not governed.

Accountability

Can one human owner explain the outcome and the decision AI may influence?

Look for: Named owner, explicit decision right, and a visible escalation path.

Operating boundaries

Does the workflow use trusted information, known platforms, and embedded controls?

Look for: Owned sources, traceable handoffs, review rules, and a working override path.

Evidence

Can the owner see whether the use case is creating value without hiding risk?

Look for: Quality, exceptions, overrides, adoption, and outcome evidence reviewed over time.

One Workflow Through Seven Layers

Readiness becomes concrete when the work is concrete.

Follow the same hypothetical refund exception used elsewhere on the site. The example shows why an AI recommendation depends on the full operating model around it.

How to use this

Replace the refund request with one workflow from your organization. For each layer, ask whether the named condition exists in the live operating record.

Hypothetical worked example

A refund request that falls outside the standard policy

A customer asks for an exception. A governed agent can collect the facts and recommend a response, but the organization still needs a named human owner, a decision rule, trusted information, and a reviewable record.

  1. 01

    Ownership

    Identity & Incentives

    The head of customer operations owns the quality and risk of refund outcomes.

  2. 02

    Decisions

    Decision Architecture

    A service manager decides exceptions. The agent may recommend, but it does not own the outcome.

  3. 03

    Communication

    Communication Architecture

    The decision, reason, and next action reach the customer, support team, and finance through defined channels.

  4. 04

    Information

    Information Ecology

    The case uses the current refund policy, order history, payment status, and customer record, each with a trusted source.

  5. 05

    Platforms

    Platform Structure

    The service, customer, and payment systems pass the case context without manual copying or hidden side work.

  6. 06

    Governance

    Governance Architecture

    Approval thresholds, an audit record, an escalation path, and a review cadence make the exception controllable.

  7. 07

    AI

    AI Amplification

    The agent assembles evidence and drafts a recommendation. A named human approves the exception and remains accountable.

Result

The customer receives a faster answer, the decision remains traceable, and AI increases capacity without inheriting authority it should not hold.

Seven Concise Checklists

Inspect the gap without repeating the full framework.

Each page is intentionally scoped to AI-readiness questions for that layer. Full layer education remains on the Framework pages.

Identity & IncentivesEvery AI initiative has a named business owner, a clear outcome, defined accountability, and incentives that support the new way of working.
Decision ArchitectureThe organization knows which decisions AI can support, which require human judgment, and how accountability is preserved.
Communication ArchitectureAI-generated context is shared through the right channels, tied to decisions and work, and traceable over time.
Information EcologyAI has access to trusted, owned, current, and governed information connected to real workflows and decisions.
Platform StructureAI is embedded into real workflows and connected to the systems, data, decisions, and governance needed to create value.
Governance ArchitectureGovernance is not a late-stage approval gate. It is built into workflow, decision rights, information use, and platform access.
AI AmplificationThe organization knows where AI belongs, what level of autonomy is safe, who reviews it, and how performance and risk are monitored over time.

Recommended Next Step

Establish the baseline before choosing the AI tool.

The result identifies the weakest foundation, explains the directional maturity placement, and gives you two risks and specific 7-day and 30-day actions.

Take the DiagnosticReview the Maturity Path