AI Readiness Framework
Decision clarity before resource commitment.
A structured approach to understanding where your organization stands on AI execution, where alignment gaps exist, and how to sequence initiatives to reduce delivery risk.
Context
AI initiatives fail when foundations and ownership are unclear.
Organizations invest in pilots that cannot scale, deploy tools without governance frameworks, or discover alignment gaps after committing significant resources.
This framework surfaces the gaps that typically derail initiatives: unclear ownership, misaligned expectations across roles, insufficient data foundations, and missing governance structures.
Framework
The five pillars of AI readiness
The AI Readiness Index evaluates these five pillars. Each pillar score contributes to the overall readiness assessment and sequencing recommendation.
People
Organizational structure, skills, and accountability for AI delivery.
- Defined roles and accountability for AI outcomes
- Skills assessment and capability building plans
- Cross-functional coordination and change readiness
Data
The quality, accessibility, and governance required to support AI workloads.
- Data quality, accessibility, and documentation standards
- Governance structures for data used in AI systems
- Infrastructure capacity and scalability
Business
Executive clarity on use cases, outcomes, and investment priorities.
- Clear articulation of AI's role in business strategy
- Use case prioritization tied to measurable outcomes
- Defined success criteria and resource allocation
Governance
Ownership, controls, and review processes that enable responsible deployment.
- Compliance and regulatory alignment
- Risk management frameworks for AI-specific concerns
- Clear guardrails that enable rather than block execution
Technology
Architecture and tooling that can support safe deployment at scale.
- Infrastructure that supports production workloads
- Clear separation of experimentation and production
- Monitoring and observability for model behavior
Pattern
Where initiatives typically fail
Most AI initiatives follow a predictable path. Failures concentrate at the transition points—not in the technology itself.
- Data is not ready when the pilot requires it
- Ownership becomes unclear when transitioning to production
- Governance gaps surface late and block deployment
Sequencing
How readiness is determined
Readiness determines what should happen next. The assessment evaluates foundation strength, cross-role alignment, and governance posture to produce one of three sequencing recommendations.
Stop
Address blockers before proceeding
- Critical gaps exist in core foundations
- Material misalignment across roles
- Governance or ownership issues unresolved
Test
Validate assumptions before committing to scale
- Foundations are partially in place
- Alignment gaps are manageable
- Risks are testable rather than structural
Go
Proceed with confident execution
- Foundations meet required thresholds
- Cross-role alignment is consistent
- Governance and ownership are clearly defined
Indicators
Signals that suggest slowing down
- Data is incomplete, undocumented, or inaccessible
- No clear owner has been designated for AI outcomes
- The underlying problem is communication or incentives, not automation
- The use case requires perfect accuracy with no tolerance for error
- Compliance and regulatory expectations are undefined
- There is no plan to monitor model behavior post-deployment
- Teams are already using unsanctioned AI tools
- AI is on the roadmap but success metrics have not been defined
Outputs
What the diagnostic produces
Immediate
- Executive alignment memo
- Perception gap analysis by role
- Sequencing recommendation with rationale
30-day
- Prioritized action plan
- Ownership assignments
- Governance recommendations
90-day
- Sequenced execution plan
- Six-month directional horizon
- Progress tracking framework
Questions
Common questions
How long does the diagnostic take?
Most diagnostics complete in one to two weeks. The goal is executive clarity, not a multi-month initiative.
Who should participate?
Typically the CTO, CIO, or CDO, along with leaders from data, governance, and business functions actively pursuing AI use cases.
Is this a compliance exercise?
No. The diagnostic produces a decision artifact, not an audit report. The goal is to clarify sequencing and surface alignment gaps.
What does coverage mean in the report?
Coverage indicates the proportion of the assessment completed across participating roles. Lower coverage highlights where additional input would improve confidence.
What happens after the diagnostic?
Teams use the report to align stakeholders, brief leadership, prioritize work, and guide pilot execution.
Understand readiness before committing resources.
The diagnostic surfaces where foundations and alignment support execution, and where they introduce delivery risk.
Stratify Insights supports executive teams responsible for delivery, governance, and enterprise outcomes.