Executive Guide
Executive Guide to AI Readiness
Most AI programs fail for predictable reasons: unclear ownership, weak foundations, and misalignment across roles. This guide explains how to measure readiness before scaling and how to decide what to Stop, what to Test, and what to Go.
Looking for a concrete example? View the sample AI Readiness report.
Definition
What AI readiness actually means
Readiness is not enthusiasm or ambition. It is whether an organization can execute AI initiatives safely and scale them without excessive rework. The AI Readiness Framework evaluates five foundations and cross-role alignment to produce a sequencing recommendation.
- Readiness is the ability to execute safely, not the desire to adopt AI
- Strong foundations reduce delivery risk and rework
- Alignment and governance determine whether pilots can scale
Patterns
Why AI initiatives fail in practice
Failures concentrate at transition points, not in the technology itself. These patterns repeat across industries and organization sizes.
- Pilots succeed but cannot scale because data is not production-ready
- No single owner is accountable when outcomes disappoint
- Leadership expects scale while technical teams see missing foundations
- Governance surfaces late and blocks deployment
- Teams use unsanctioned AI tools without oversight
For more on governance-specific failure modes, see AI Governance and Risk.
Model
The Stratify model in one view
The Stratify assessment produces a Stop, Test, or Go recommendation based on foundation strength, cross-role alignment, and governance posture. This sequencing model replaces ambiguous “maturity scores” with a clear decision.
Stop
Pause scale until critical constraints are addressed.
Test
Validate key assumptions through bounded pilots before committing to scale.
Go
Proceed where foundations and alignment support confident execution.
The sequencing model is explained in detail in the AI Readiness Framework.
Pillars
The five pillars
The AI Readiness Index evaluates these five pillars. Each pillar score contributes to the overall readiness assessment and sequencing recommendation.
People
- Defined roles and accountability for AI outcomes
- Skills assessment and capability building
- Cross-functional coordination and change readiness
Data
- Data quality, accessibility, and documentation
- Governance for data used in AI systems
- Infrastructure capacity for production workloads
Business
- Use case prioritization tied to measurable outcomes
- Executive ownership of AI investments
- Clear success criteria before commitment
Governance
- Ownership for model decisions in production
- Escalation paths and approval thresholds
- Human review requirements defined
Technology
- Architecture that supports safe deployment
- Clear separation of experimentation and production
- Monitoring and observability for model behavior
Signals
Signals leaders miss
Beyond structured assessments, qualitative signals often reveal execution risk that quantitative metrics miss.
Shadow AI usage
Teams adopting tools outside sanctioned processes, creating governance exposure.
Competitive risk perception
Pressure to move fast without foundations, leading to rework and delivery failure.
Perception gaps
Leadership and technical teams hold different views of readiness and risk.
Technical confidence
How confident technical teams are that leadership understands data constraints.
Process
How to use this guide
1. Diagnose
Run an AI Readiness Assessment to surface foundation gaps, alignment issues, and governance constraints.
2. Align
Use the perception gap analysis to reconcile differences between leadership, technical, and operational views.
3. Pilot
For Test recommendations, run bounded pilots that validate assumptions without committing to scale.
4. Scale
For Go recommendations, proceed with initiatives where foundations and alignment support execution.
Sequencing
When to Stop, Test, or Go
Stop
Pause scale until constraints are resolved.
- Critical foundation gaps
- No clear ownership
- Material misalignment
- Governance undefined
Test
Validate assumptions through bounded pilots.
- Foundations partially in place
- Alignment gaps manageable
- Risks testable, not structural
- Governance defined but untested
Go
Proceed with confident execution.
- Foundations meet thresholds
- Cross-role alignment consistent
- Governance functioning
- Clear ownership
Want a clear Stop, Test, or Go call for your organization?
The Executive Diagnostic surfaces where foundations and alignment support execution, and where they introduce delivery risk.
Questions
Frequently asked questions
What is the difference between AI maturity and AI readiness?
Maturity measures capability over time. Readiness measures whether foundations, alignment, and governance can support execution now. An organization can be mature but not ready if ownership is unclear or alignment is weak.
Do we need perfect data before we start?
No. You need data that is good enough for the use case, with documented limitations. The assessment surfaces where data gaps create delivery risk and where they do not.
What counts as a bounded pilot?
A pilot with defined scope, success criteria, and constraints that tests key assumptions without committing to scale. It should validate whether foundations hold under real conditions.
Who should own AI governance?
Governance ownership depends on organizational structure, but accountability should be clear. The assessment surfaces where ownership is undefined or contested.
How long does an Executive Diagnostic take?
Most diagnostics complete in one to two weeks. The goal is executive clarity, not a multi-month initiative.
Understand readiness before committing resources.
The Executive Diagnostic produces a sequencing recommendation, perception gap analysis, and 90-day action plan in one to two weeks.
Stratify Insights supports executive teams responsible for delivery, governance, and enterprise outcomes.