AI Readiness Assessment: 50-Point Enterprise Checklist for 2026

Score each item: 0 (No) / 1 (Partial) / 2 (Yes)

1. We have a centralised data catalogue documenting all major data sources
2. Our data quality is measured and monitored with automated checks
3. We have data lineage tracking from source systems to analytics
4. Our data is accessible via APIs or modern data platforms (not just manual exports)
5. We have a documented data dictionary with business definitions for key columns
6. Data freshness meets business requirements (real-time, hourly, daily as needed)
7. We can provision data environments for experimentation without IT tickets
8. Historical data is retained and accessible for model training (2+ years)
9. Data integration between silos is automated (not manual ETL scripts)
10. We have a feature store or equivalent for reusable ML features

Score: ___ / 20

11. We have a documented AI governance framework with clear accountability
12. AI use cases are classified by risk level before development begins
13. We have a model risk management process (validation, monitoring, retirement)
14. Data privacy impact assessments are conducted for AI projects using personal data
15. We understand which AI regulations apply to our sector (EU AI Act, DORA, NIS2)
16. We have processes for AI explainability and transparency
17. Bias testing is part of our model development lifecycle
18. We maintain an AI model registry with version control and audit trails
19. Human oversight mechanisms are defined for high-risk AI decisions
20. We have an AI ethics review process for sensitive use cases

Score: ___ / 20

21. We have cloud infrastructure capable of running ML training workloads
22. GPU or specialised compute is available for AI/ML development
23. We have CI/CD pipelines that can deploy ML models to production
24. Model serving infrastructure supports A/B testing and canary deployments
25. Monitoring and alerting covers model performance (drift, accuracy degradation)
26. We can scale AI inference capacity based on demand
27. Our infrastructure supports experiment tracking and reproducibility
28. Security controls (encryption, access management, audit logging) cover AI systems
29. We have container orchestration (Kubernetes) or equivalent for AI workloads
30. Our infrastructure can support both batch and real-time AI inference

Score: ___ / 20

31. We have data scientists or ML engineers on staff (or contracted)
32. Our data engineering team can build and maintain ML data pipelines
33. MLOps/platform engineering skills exist to productionise models
34. Business stakeholders understand AI capabilities and limitations
35. We have AI literacy training for non-technical staff
36. Leadership understands AI ROI measurement and realistic timelines
37. We have a talent pipeline for AI roles (hiring, upskilling, or partnering)
38. Cross-functional teams (data + business + engineering) collaborate on AI projects
39. We have access to domain experts who can validate AI outputs
40. Our team has experience deploying AI in production (not just POCs)

Score: ___ / 20

41. Executive sponsorship for AI initiatives exists with budget commitment
42. AI strategy is aligned with business strategy (not a separate technology initiative)
43. We have identified specific business problems that AI can solve (not "deploy AI everywhere")
44. Success metrics for AI projects are defined before development starts
45. Failed AI experiments are treated as learning, not punishment
46. Data sharing between departments is culturally accepted (not hoarded)
47. Change management processes exist for AI-driven workflow changes
48. We have a process for prioritising AI use cases based on business impact and feasibility
49. Internal communication about AI is realistic (not hype-driven)
50. We measure and report on AI value delivery to the board

Score: ___ / 20

• 80-100: AI-ready. You have the foundation for enterprise AI at scale. Focus on execution and scaling.
• 60-79: Mostly ready. Specific gaps need addressing before scaling AI beyond POCs. Prioritise the weakest dimension.
• 40-59: Foundation building. Significant investment needed in data infrastructure and governance before AI will deliver reliable results.
• 20-39: Early stage. Focus on data fundamentals and pilot projects. Don't attempt enterprise-wide AI deployment yet.
• 0-19: Pre-AI. Start with data strategy and infrastructure. AI investment at this stage will likely fail.

Lowest dimension score = your bottleneck. A score of 18/20 in technology means nothing if governance is 4/20. AI readiness is limited by your weakest dimension.

Want the interactive version? Tick items and get a live score with the AI readiness checklist, or take the free 2-minute AI readiness quick score.

When you need an outside view, our AI readiness assessment evaluates these dimensions in depth, with specific remediation recommendations and a prioritised roadmap — fixed fee from €12,000. The 30-minute scoping call is free.