AI Readiness Score

Answer honestly

Each question uses a 1–4 scale (1 = not yet, 4 = strongly in place). 0 / 10 answered

1. 1. We tie AI initiatives to documented business outcomes (not hype demos).

   Not yetDiscussed informallyDocumented for some projectsStandard for new AI work
2. 2. Teams can access relevant data, docs, or knowledge bases for AI projects.

   Mostly siloed or unavailablePartial access with manual effortCentralised for key domainsAPI-ready and well governed
3. 3. Executive or leadership sponsorship exists for AI experimentation.

   No sponsorshipIndividual champion onlyDepartment-level supportOrganisation-level mandate
4. 4. Engineering or ops staff have hands-on experience with LLMs or automation tools.

   No experienceAd hoc experimentsSuccessful internal pilotsProduction experience
5. 5. Human review and escalation paths are defined for AI-assisted decisions.

   Not consideredInformal reviewDocumented for pilotsEnforced in production
6. 6. We measure AI initiatives with clear KPIs (time saved, quality, revenue, etc.).

   No KPIsAnecdotal onlyKPIs for pilotsKPIs tracked ongoing
7. 7. Our systems expose APIs, webhooks, or integration layers suitable for AI tooling.

   Mostly manualSome APIsIntegration patterns in placeMature integration fabric
8. 8. Budget or capacity is allocated for AI experimentation and iteration.

   No budgetAd hoc / unfundedPilot budget approvedOngoing AI line item
9. 9. Privacy, security, and compliance are part of AI planning (not afterthoughts).

   Not addressedInformal awarenessChecklist for projectsLegal/security engaged early
10. 10. We run evals, pilots, or staged rollouts before production AI features.

    Ship directly to usersInformal testingStructured pilotsEval suites before launch