Use-case readiness
Teams have ideas but no prioritisation method.
Better starting point: Score use cases by impact, data readiness, reviewability, change cost, and ownership.
AI readiness assessment · GenAI diagnostic · Adoption starting point
AI readiness assessment for teams choosing the right GenAI work
An AI readiness assessment is not a technology checklist. For most organisations, the harder question is whether teams know where AI should enter work, which use cases deserve priority, what risks need governance, and whether managers can review AI-assisted output without weakening accountability. That is the readiness problem this assessment solves.
Assessment output
An AI readiness assessment should not end with a maturity score. It should identify the workflows that are ready now, the use cases that need preparation, the ideas that should be stopped, and the manager routines needed before a pilot becomes adoption.
The assessment is deliberately practical. I am less interested in whether an organisation can declare itself AI-ready and more interested in whether one team can safely change one important workflow. If the answer is no, the diagnosis should say so. If the answer is yes, the assessment should identify the team, the use case, the owner, the training need, the governance boundary, and the first metric that would prove adoption has begun.
Readiness checks
People readiness
Employees may know tools superficially but managers do not yet know what to review.
Better starting point: Identify training needs by role, function, and workflow.
Governance readiness
Teams move fast but lack rules for privacy, verification, and accountability.
Better starting point: Define practical guardrails and veto conditions before scaling pilots.
What readiness has to prove
A serious assessment should create start, prepare, defer, and stop decisions.
Readiness is not enthusiasm
Employee GenAI resistance research mapThe wiki frames resistance as a response to bad capability diagnosis. Employees are asked to adopt AI before the organisation has defined good AI-assisted work, context boundaries, evidence requirements, and status-preserving ways to learn.
The assessment should name what to stop
AI adoption portfolio methodA readiness pass is useful only if it distinguishes quick wins, lighthouses, strategic bets, and defer-or-kill ideas. Otherwise the organisation turns every attractive AI idea into a pilot backlog.
Context discipline is a readiness signal
Enterprise context engineeringReadiness depends on whether teams know what the AI may see, which source wins when documents conflict, how currentness is recognised, and where sensitive decisions must remain outside the system.
Next questions
If this is the live issue, these are the checks.
What is an AI readiness assessment?
It is a diagnostic that evaluates whether an organisation is ready to use AI productively across workflows, people, governance, data boundaries, review routines, and use cases. For GenAI, it should produce a prioritized adoption roadmap.
Who should participate in the assessment?
The best assessment includes business leaders, L&D or HR, functional managers, technology owners, and people close to recurring workflows. AI adoption is cross-functional, so the assessment should not sit only with IT.
How long does an assessment take?
A focused assessment can be run as a workshop or short diagnostic sprint. The duration depends on the number of functions, but the output should be clear enough to guide the first 30 to 90 days.
Run an AI readiness diagnostic
Share the functions, current AI experiments, and the decisions that feel stuck. The product page shows the shipped work behind this diagnostic.