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AI Readiness Assessment

A directional browser-based assessment across strategy, leadership, data, technology, governance, people, operating model and responsible assurance.

Privacy: No name, email address or personal profile is requested. Answers and results stay in this browser session unless you explicitly copy, download, print or send them.

Interpretation: This is a structured reflection tool, not a scientifically validated maturity model, audit, legal opinion or certification. Scores organize the supplied answers into directional priorities.

Step 1 of 4

Step 1 of 4

Strategy and value and Leadership and ownership

Rate the current organizational practice using evidence from the relevant operating scope.

Strategy and value

Connect AI activity to explicit business outcomes, priorities and evidence.

AI opportunities are linked to a specific business decision, workflow or measurable outcome.

Evidence prompt: Look for an approved outcome statement, baseline and accountable owner.

Use cases are prioritized using value, feasibility, data readiness and risk rather than enthusiasm alone.

Evidence prompt: Look for explicit selection criteria and rejected or deferred options.

Success, stop, adapt and scale criteria are defined before implementation begins.

Evidence prompt: Look for decision thresholds and review dates.

Leadership and ownership

Establish accountable sponsorship, decision rights and evidence-based oversight.

Each material AI initiative has an accountable business owner with authority to stop, adapt or scale it.

Evidence prompt: Look for named accountability beyond a technical project lead.

Decision rights across business, technology, data, legal, security and risk are explicit.

Evidence prompt: Look for approval, escalation and exception paths.

Leaders review outcome evidence, risk signals and adoption barriers at an agreed cadence.

Evidence prompt: Look for review records that change priorities or controls.

Step 2 of 4

Data readiness and Technology and integration

Rate the current organizational practice using evidence from the relevant operating scope.

Data readiness

Ensure required data is available, permitted, understood and sustainably owned.

Required data sources, access conditions and permitted uses are identified before solution design.

Evidence prompt: Look for source, purpose, permission and access records.

Data quality, lineage, representativeness and known limitations are assessed for the intended use.

Evidence prompt: Look for measurable quality criteria and documented limitations.

Data ownership, maintenance, retention and change responsibilities are operationally assigned.

Evidence prompt: Look for owners who can resolve defects and approve changes.

Technology and integration

Build secure, observable and maintainable technical foundations for the intended use.

Architecture, integration boundaries and dependencies are defined for the complete workflow, not only the model.

Evidence prompt: Look for system context, interfaces, failure paths and human hand-offs.

Security, reliability, performance, observability and fallback requirements are tested against the intended operating context.

Evidence prompt: Look for non-functional acceptance evidence and operational alerts.

Model, platform and tool choices trace to requirements, constraints and lifecycle cost.

Evidence prompt: Look for documented alternatives and selection rationale.

Step 3 of 4

Governance and controls and People and skills

Rate the current organizational practice using evidence from the relevant operating scope.

Governance and controls

Apply proportionate approval, traceability and change control across the AI lifecycle.

AI initiatives follow a proportionate lifecycle covering intake, review, approval, release, monitoring and retirement.

Evidence prompt: Look for defined gates that vary with impact and risk.

Models, prompts, data dependencies, versions, decisions and material changes are traceable.

Evidence prompt: Look for an inventory and evidence trail that supports reconstruction.

Human review, exception, escalation and override responsibilities are explicit where consequences require them.

Evidence prompt: Look for tested controls rather than policy statements alone.

People and skills

Build the capability to use, challenge, govern and maintain AI-enabled work.

Required roles and capability gaps are defined across users, domain experts, engineering, data and assurance.

Evidence prompt: Look for role-specific tasks and decision responsibilities.

Users are trained to apply, question, verify and escalate AI-supported outputs in context.

Evidence prompt: Look for observed task performance, not attendance alone.

Specialist knowledge is transferred through repeatable practice, review and maintained documentation.

Evidence prompt: Look for reduced dependency on isolated experts.

Step 4 of 4

Operating model and adoption and Risk, ethics and assurance

Rate the current organizational practice using evidence from the relevant operating scope.

Operating model and adoption

Integrate AI into accountable processes, support models and sustainable ownership.

The target workflow, human hand-offs and changed responsibilities are designed explicitly.

Evidence prompt: Look for a future-state process rather than a tool added to the old process.

Adoption, support, feedback and resistance are managed as operating work with accountable owners.

Evidence prompt: Look for usage evidence and closed feedback loops.

Funding, service ownership, maintenance, vendor dependency and retirement are planned beyond the pilot.

Evidence prompt: Look for a credible run model and total lifecycle responsibility.

Risk, ethics and assurance

Identify impact, test safeguards and maintain evidence for responsible operation.

Potential harm, misuse, legal, privacy, security and business-continuity risks are assessed for the specific context.

Evidence prompt: Look for scenario-based impact analysis and accountable acceptance.

Fairness, transparency, explainability and affected-person considerations are addressed proportionately.

Evidence prompt: Look for evidence suited to the decision impact, not generic claims.

Monitoring, incident response, challenge, correction and decommissioning are tested and owned.

Evidence prompt: Look for thresholds, response procedures and retained incident evidence.

Preparing the local assessment…

What the assessment covers

Strategy and value

Connect AI activity to explicit business outcomes, priorities and evidence.

Leadership and ownership

Establish accountable sponsorship, decision rights and evidence-based oversight.

Data readiness

Ensure required data is available, permitted, understood and sustainably owned.

Technology and integration

Build secure, observable and maintainable technical foundations for the intended use.

Governance and controls

Apply proportionate approval, traceability and change control across the AI lifecycle.

People and skills

Build the capability to use, challenge, govern and maintain AI-enabled work.

Operating model and adoption

Integrate AI into accountable processes, support models and sustainable ownership.

Risk, ethics and assurance

Identify impact, test safeguards and maintain evidence for responsible operation.

Each dimension is scored from the supplied answers on a 0–100 directional scale. The overall result is the equally weighted average of all eight dimensions. Priority actions are selected from the lowest-scoring dimensions.