AI Adoption NavigatorTech To The Rescue

The rules

Every rule the AI Adoption Navigator applies to reach a result, for anyone reviewing the method. The methodology page explains the same method in short.

What is asked

The questions are the ones asked since October; framework 8.0.0 changed only how they are read. Most are about the organization and asked once. A few are asked again for each workflow in or going into normal operations: a piece of work AI is part of, such as ranking food-parcel applications. Some appear only when an earlier answer calls for them.

Asked
Questions
Once, about the organization
45, of which 19 appear only when an earlier answer calls for them
For each piece of AI work on the map of AI use
4
For each workflow running or about to run in normal operations
4

How a stage is decided

Each stage has deciding conditions, which place it, and readiness conditions, which confirm it. The deciding conditions that say how far AI goes form the stage line, which belongs to no domain. Stage 4’s line, for example, is a workflow whose week-long failure would harm the people served, running in normal operations. Every other condition belongs to one capacity domain.

A condition about a workflow is met only when every workflow it applies to meets it. If AI ranks food-parcel applications and also sets the order of home visits, both workflows need a way to appeal. Stage 4 is the exception: one workflow that meets all of Stage 4’s conditions is enough.

The organization’s stage is its highest confirmed stage, or the stage just above it if that one is conditional. Only one stage can be conditional. The stage above a conditional stage gets no status, but its deciding conditions still show as gaps.

Stage
Condition
Capacity domain
2 Exploring
People use AI in their own work, however unevenly.
Stage line, in no domain
Stage
Condition
Capacity domain
3 Systematizing
AI use becomes expected in selected areas rather than exceptional.
Stage line, in no domain
The organization runs AI pilots on selected workflows, or has run the Adoption Loop on them more than once.
Stage line, in no domain
A named AI owner, champion, or adoption group has time and authority.
Leadership and ownership
People share what works and what fails across the organization.
Evidence and learning
Stage
Condition
Capacity domain
4 Innovating
AI is integral to a workflow that matters to mission delivery or organizational performance.
Stage line, in no domain
The workflow operates repeatedly outside a temporary demo environment.
Stage line, in no domain
The workflow is redesigned rather than merely supplemented with an AI tool.
People, workflows and change
Evidence is used to improve the workflow.
Evidence and learning
Governance, maintenance, dependency, and failure handling are active operating practices.
Governance, risk and accountability
Accountable AI adoption and technical ownership.
Leadership and ownership
Data/context ownership and quality controls.
Data and organizational context
Stage
Condition
Capacity domain
5 Enabling
At least two independent organizations use one of them in their own work today.
Stage line, in no domain
Other organizations obtain demonstrated value from the capability.
Evidence and learning
Sustainable funding.
Resources and sustainability

Readiness conditions

Any missing readiness condition keeps the stage from being confirmed. More than half of those that apply must be in place for the stage to be conditional; with half or fewer, it is not reached.

Stage
Capacity domain
Condition
3 Systematizing
Leadership and ownership
Workflow and use-case prioritization.
Leadership and ownership
Regular implementation cadence.
Leadership and ownership
Where there is an AI owner: a successor arranged, so the work survives their leaving.
Leadership and ownership
Where this is the highest stage whose deciding conditions are met: at least two people answered, and they agree on the deciding answers of this stage and of the stages below.
People, workflows and change
Team education and learning rhythm.
People, workflows and change
Explicit map of changing tasks, roles, review, and exceptions.
People, workflows and change
The people who do the work take part in shaping how AI is used.
Data and organizational context
Data/context inventory and maintenance responsibility.
Technology, implementation and resilience
Shared licenses or approved tool stack.
Technology, implementation and resilience
Technical support model.
Technology, implementation and resilience
Vendor and partner dependency view.
Technology, implementation and resilience
Where the work is done pro bono: someone independent checks its quality, and a handover is agreed.
Governance, risk and accountability
Safe-use and data-handling norms.
Governance, risk and accountability
Criteria for deciding which workflows are ready for operational deployment.
Governance, risk and accountability
Where AI affects the people served: participation by affected users or credible representatives.
Governance, risk and accountability
Where AI affects the people served: documented limits on automated decisions.
Governance, risk and accountability
Where AI affects the people served: qualified and accountable human review.
Governance, risk and accountability
Where AI affects the people served: a clear method to question, correct, or appeal outcomes where relevant.
Governance, risk and accountability
Where AI affects the people served: testing for exclusion, bias, and differential harm.
Governance, risk and accountability
Where AI affects the people served: communication that makes the role of AI understandable.
Governance, risk and accountability
Where AI affects the people served: safeguards appropriate to the political and surveillance environment.
Evidence and learning
Baseline and intended outcome for priority workflows.
Evidence and learning
Where the organization measures its AI work: results are judged by outcomes, not by the volume of output.
Resources and sustainability
Budget, credits, and protected implementation time.
Stage
Capacity domain
Condition
4 Innovating
Leadership and ownership
A decision process for continuation, expansion, constraint, or retirement.
Leadership and ownership
Where this is the highest stage whose deciding conditions are met: at least two people answered, and they agree on the deciding answers of this stage and of the stages below.
People, workflows and change
Staff or user acceptability checks.
Technology, implementation and resilience
Security and privacy stance.
Technology, implementation and resilience
Maintenance and incident ownership.
Technology, implementation and resilience
Vendor, model, data, and partner dependency review.
Technology, implementation and resilience
Portability, rollback, or migration path.
Governance, risk and accountability
A risk and consequence assessment.
Governance, risk and accountability
Human review, escalation, exception, and appeal rules where relevant.
Evidence and learning
A defined user need and intended outcome.
Evidence and learning
A baseline and evaluation plan.
Evidence and learning
Monitoring after deployment.
Evidence and learning
A review of failures and a learning cadence after deployment.
Resources and sustainability
Cost and consumption controls.
Resources and sustainability
Where the deployment depends on temporary funding: a plan for continuing the work when it ends.
Stage
Capacity domain
Condition
5 Enabling
Leadership and ownership
Clear ownership and accountability.
Leadership and ownership
Where this is the highest stage whose deciding conditions are met: at least two people answered, and they agree on the deciding answers of this stage and of the stages below.
People, workflows and change
Support model for adopters.
Technology, implementation and resilience
Partner and corporate influence boundaries.
Technology, implementation and resilience
Interoperability and portability strategy.
Governance, risk and accountability
Mission-control guardrails.
Governance, risk and accountability
Standards and governance.
Governance, risk and accountability
Credibility with the communities and organizations affected.
Evidence and learning
Evidence that the infrastructure improves adoption or outcomes.
Resources and sustainability
Distribution and implementation partnerships.

The protections for the people served

Where AI affects the people the organization serves, seven protections are Systematizing readiness conditions. They are read on each workflow in or going into normal operations that reaches the people served or decides about a person. When no such workflow does, four are read from questions about the organization; the other three have no such question and are marked “Not asked yet”.

Protection
Read on a workflow
With no such workflow
Participation by affected users or credible representatives.
Where it reaches the people served or decides about a person
Read from a question about the organization
Documented limits on automated decisions.
Where it reaches the people served or decides about a person
Not asked yet
Qualified and accountable human review.
Where it reaches the people served or decides about a person
Read from a question about the organization
A clear method to question, correct, or appeal outcomes where relevant.
Where it decides about a person, or nobody knows whether it does
Read from a question about the organization, where AI decides who gets access to a service or resource; otherwise left out
Testing for exclusion, bias, and differential harm.
Where it reaches the people served or decides about a person
Read from a question about the organization
Communication that makes the role of AI understandable.
Where it reaches the people served or decides about a person
Not asked yet
Safeguards appropriate to the political and surveillance environment.
Where it reaches the people served or decides about a person
Not asked yet

When several people answer

Each person answers alone, from their own role. The answers become one set before the engine reads them:

  • on a question about how far the organization has got, the middle answer counts; with an even number of people the more cautious of the two middle answers;
  • a tick-list is combined item by item, by the middle answer for each item;
  • on a question about what is at stake, the most demanding answer counts: who would feel a mistake first, its worst consequence, and whether a workflow decides about a specific person;
  • on a question with several possible answers, every answer anyone gave counts;
  • the map of where the organization uses AI is shared and not voted on;
  • the aspiration answers are those of the person who started the assessment;
  • “We don’t know” does not vote.

From Systematizing, the team’s agreement is a readiness condition: at least two people answered, and they agree on the answers that decide this stage and the stages below. It is checked at the highest stage whose deciding conditions are met. A difference counts against it only when another person’s answer would change a deciding condition.

Every condition that is missing, unconfirmed or not asked yet is a gap. The report turns at most 3 into recommended actions, after what the team must settle. The order:

  1. gaps in the protections for the people served, whether or not support is available;
  2. gaps that support available now can help close;
  3. the other gaps.

Within 2 and 3: first what a workflow in or going into normal operations still lacks, then the rest of the conditional stage, then the deciding conditions of the stage above. When two gaps rank the same, a condition someone said is missing comes before one nobody could confirm; after that, the order of the domains decides.

An action names its support as a support type and a provider profile, never a program. Support counts as available only once its call for applications is checked as open. Until then, the action says “No support listed yet”. The actions are worked out again each time the result is read; the stage, its status and the gaps stay as read.

The next Adoption Loop step

Every condition except the team’s agreement belongs to one step of the Adoption Loop. With no workflow in normal operations, the next step is the first step the organization has not done. With one, it is the step of the first recommended gap.

Where the Navigator decides what the framework leaves open

  • Borderline answers are read strictly: sharing counts only when regular, rules only when written down, a successor for the AI owner only when arranged.
  • “We don’t know” whether a workflow decides about a person counts as yes, so its protections apply.
  • With no workflow in normal operations, Stage 4’s conditions about the workflow are missing, not left out.
  • A workflow counts as in operation only when it runs in normal operations; one going into operation does not.
  • Nine of Enabling’s ten readiness conditions have no question yet: they are marked “Not asked yet” and count as not in place. The tenth is the team’s agreement.

Where the Navigator differs from the framework

Framework 8.0.0 lets one person’s answer confirm Exploring. The Navigator confirms no stage from one person’s answers, because one person gives one view of an organization. The engine’s result is the same; only the report differs. It opens with “One person answered”, shows Resisting and Exploring without a status, and any higher stage as conditional at most. We have proposed the change to the framework’s authors.

No model in the result

No language model places the stage, decides a condition or picks an action. The rules are code, so the same answers, read by the same version of the rules, give the same result.

Versions

Framework 8.0.0, engine v8-engine-1, survey v8-draft-1, combining answers consolidation-3. For now, every result is read again by the current rules each time it is opened; the answers are never changed. Once the rules settle, a result keeps the version that issued it.

Built on the AI Adoption Framework 8.0.0 · engine v8-engine-1Developed by Tech To The Rescue