Score

Artificial Intelligence Readiness Assessment

We have a clear AI strategy and senior leadership actively champions AI integration across the business.

Leadership supports AI but relies heavily on external experts to drive the agenda.

We have no formal leadership engagement with AI strategy.

Leadership is skeptical about AI's relevance to our core business.

We have structured AI training and upskilling programs for all levels of the workforce.

We provide access to AI tools, but formal training is limited or ad-hoc.

Workforce AI preparation is handled by individual initiative, with no organizational support.

There is currently no focus on workforce AI preparation.

Q2. Workforce AI preparation

Our data infrastructure is robust, with clear, enforced governance and quality standards for AI.

We have data platforms in place, but governance is inconsistent or under-developed.

Data infrastructure is siloed and fragmented; we lack consistent governance.

We do not have a defined data infrastructure for AI.

Q3. Data infrastructure & governance maturity

We have comprehensive, formalized AI ethics and risk management policies.

We have basic risk assessment processes for AI projects, but policies are evolving.

We rely on general IT/legal policies; there is no specific AI focus.

We have no established risk or ethical guardrails for AI usage.

Q4. Ethical & risk guardrails

We use a clear, value-driven framework to prioritize AI initiatives aligned with business goals.

Prioritization is based on departmental needs or technical feasibility rather than holistic strategy.

AI projects are prioritized opportunistically as they arise.

There is no formal prioritization for AI projects.

Q5. Prioritization framework for AI use cases

We actively encourage experimentation and celebrate learning from failure in AI projects.

Experimentation is allowed, but there is a low tolerance for project failures.

AI experimentation is generally limited to specific teams or roles.

The organization is risk-averse and generally discourages AI experimentation.

Q6. Psychological safety experimenting with AI

We have a dedicated AI team or CoE with clear pathways for talent development.

AI capabilities are distributed across existing IT/data teams.

We rely heavily on external partners for all AI talent needs.

We have no clear AI talent or capability structure.

Q7. AI talent & capability structure

We have a deliberate, strategic blend of building, buying, and partnering for AI.

We primarily buy or partner for AI solutions, with little internal build capability.

We prefer to build internal AI solutions whenever possible.

We have no clear strategy on whether to build, buy, or partner for AI.

Q8. Build/buy/partner strategy

We have clearly defined KPIs to measure the business impact of AI initiatives.

We measure some operational metrics, but business impact is hard to quantify.

We rely on subjective feedback or ad-hoc reporting for AI impact.

We do not currently measure the impact of AI initiatives.

Q9. Measuring AI impact

All consequential AI decisions have human-in-the-loop oversight and clear accountability.

We have no formal process for human oversight of consequential AI decisions.

We have clear, audited processes for overseeing consequential AI decisions.

Consequential AI decisions are currently managed by the technical team alone.

Q10. Governance of consequential AI decisions

We proactively manage the emotional and cultural impact of AI adoption on our teams.

We recognize the cultural impact of AI but lack a structured approach to manage it.

We treat AI change as a technical implementation with little focus on cultural impact.

We have not considered the cultural impact of AI.