Kaivex AI Readiness Framework.
A six-dimension assessment that turns enterprise AI readiness from a debate into a decision. Score it, plot it on the maturity model, and walk into your next steering meeting with a defensible next move.
Leadership Readiness
Sponsorship, accountability, and tolerance for the operating-model changes AI requires.
Scoring criteria
- A named executive sponsor owns AI outcomes, not just the budget
- AI portfolio decisions happen at a recurring forum with authority
- Leadership has explicitly chosen an ambition tier (efficiency, advantage, reinvention)
- Failure modes — including killing a pilot — are rehearsed, not theoretical
Data Readiness
Trust, accessibility, lineage, and quality of the data AI systems will depend on.
Scoring criteria
- Source-of-truth systems are named and stewarded
- Data lineage is documented for the workflows AI will touch
- Access patterns are governed by policy, not by tribal knowledge
- Quality is measured continuously, not just before a project starts
Technology Readiness
Platform, integration, MLOps, and security posture for production AI.
Scoring criteria
- A reference architecture exists for production AI workloads
- Evaluation, monitoring, and on-call patterns are established
- Secrets, identity, and network segmentation meet enterprise policy
- Model lifecycle (versioning, rollback, regression eval) is operationalized
Process Readiness
How work is designed around AI — not bolted on top of it.
Scoring criteria
- The workflow AI lives inside has a named owner
- Human review points are explicit, including escalation rules
- Exception handling is designed before launch, not discovered after
- Pre/post metrics are instrumented before the pilot ships
Governance Readiness
Policy, review, risk, and audit infrastructure for responsible AI.
Scoring criteria
- Use-case intake includes a risk classification
- Higher-risk systems require named review (legal, risk, security)
- Decision logs and model documentation are retained per policy
- A kill-switch authority and procedure exists for every production system
Workforce Readiness
Skills, change capacity, and adoption design across affected roles.
Scoring criteria
- Roles affected by AI have a written adoption plan
- Training is built around the workflow, not the tool
- Front-line feedback loops into model and process improvement
- Change capacity is sequenced — no role gets two transformations at once
Four bands. One next move per band.
Score each dimension 0–30 (six dimensions × 0–30 = 180 max). Total your score and locate your band. The point of the model is to make the next decision obvious.
Reactive
AI is opportunistic. Foundations missing: sponsor, portfolio, data baseline.
Emerging
A few wins, no operating model. Codify governance and a pilot-to-production process.
Operating
Repeatable production launches. Invest in adoption, evaluation, and cost discipline.
Strategic
AI is a measured portfolio. Optimize for compounding returns and model lifecycle.
Run the readiness assessment with our team.
A facilitated working session converts the framework into a scored, board-ready view of your AI readiness in under a week.