Framework
AI Readiness Framework
A readiness model designed to reduce delivery risk before AI scales into production.
Readiness is not maturity. It is whether foundations, alignment, and governance can support execution.
Definition
What readiness means
Capability and maturity do not equal deployable readiness. An organization can have skilled teams and modern infrastructure but still lack the governance, ownership, or cross-role alignment required to scale AI safely.
The goal is to prevent scaling risk, not to produce a vanity score. Readiness surfaces whether an initiative should proceed, be tested in a bounded way, or wait until foundational gaps are addressed.
The output of the framework is a sequencing recommendation: Stop, Test, or Go.
Structure
The five pillars
The AI Readiness Index evaluates these five pillars. Each pillar score contributes to the overall readiness assessment and sequencing recommendation.
People
#peopleOrganizational structure, skills, and accountability for AI delivery.
Failure mode: Strong pilots that stall at rollout due to unclear ownership.
Data
#dataThe quality, accessibility, and governance required to support AI workloads.
Failure mode: Models amplify inconsistent or incomplete data.
Business
#businessExecutive clarity on use cases, outcomes, and investment priorities.
Failure mode: AI initiatives approved without a measurable outcome.
Governance
#governanceOwnership, controls, and review processes that enable responsible deployment.
Failure mode: Deployment without accountability for decisions.
Technology
#technologyArchitecture and tooling that can support safe deployment at scale.
Failure mode: Experimentation stacks promoted into production.
These are the same five pillars used in the AI Readiness Index, assessment, and report. Link directly to any pillar using /ai-readiness-framework#people, #data, #business, #governance, or #technology.
Alignment
Alignment changes the decision
Cross-role alignment determines whether readiness translates into execution. When leadership, technical, and operational teams hold different views of reality, sequencing decisions break down.
The framework measures perception gaps across roles to surface misalignment before it becomes a delivery problem.
What misalignment looks like
- Leadership expects scale, technical teams see missing foundations
- Functions disagree on ownership for governance
- Teams cannot name a single accountable owner for outcomes
Sequencing
Stop, Test, Go
The framework produces one of three sequencing recommendations. Stop means pause scale-oriented initiatives until constraints are addressed. Test means validate assumptions through bounded pilots before committing. Go means proceed where foundations and alignment support execution.
Stop
Address blockers before proceeding
- Critical gaps in core foundations
- Material misalignment across roles
- Governance or ownership unresolved
Test
Validate before committing to scale
- Foundations are partially in place
- Alignment gaps are manageable
- Risks are testable, not structural
Go
Proceed with confident execution
- Foundations meet thresholds
- Cross-role alignment is consistent
- Governance and ownership defined
Deliverables
What leaders receive
See the full assessment structure at /ai-readiness.
Fit
Who this is for
Best fit
- Leadership teams accountable for AI delivery outcomes
- Organizations moving from pilot to production
- Teams facing pressure to scale without clear sequencing
- Executives who need a decision artifact, not a maturity score
- Functions where AI ownership is unclear or contested
Not a fit
- Early exploration without defined use cases
- Vendor selection or product evaluation
- Compliance audits or regulatory certification
- Teams seeking a maturity benchmark for reporting
- Organizations without executive sponsorship
If you plan to deploy AI, start with readiness.
A clear decision and a 90-day plan beat another pilot.
Stratify Insights supports executive teams responsible for delivery, governance, and enterprise outcomes.