The problem this solves
AI initiatives rarely fail on technology; they fail on adoption. The pattern: leadership announces the AI push, a pilot group experiments enthusiastically, and six months later most of the team works exactly as before, because nobody assessed who had the skills, the time, or the incentive to change. Meanwhile anxiety fills the communication vacuum, and the people whose workflows AI would change most were consulted least.
How we work
We assess readiness at three levels. Individual: current AI literacy across roles, from daily power users to people who have never touched a prompt, mapped without embarrassment or blame. Team: how work is structured today, and whether existing processes have room for AI-assisted steps or would need redesign. Organizational: incentives, time budgets, and whether anyone is actually resourced to support adoption after the launch enthusiasm fades.
Method is interviews and a structured skills survey, plus a review of how earlier tool rollouts went in your company, because past adoption behaviour predicts future adoption behaviour better than stated enthusiasm does.
The output is a readiness map by team and role, a skills gap analysis, and a concrete adoption risk assessment: who will run ahead, who will need structured support, and where the initiative is most likely to stall. It feeds directly into training strategy and change execution work.
Deliverables
- AI literacy baseline across roles and teams
- Workflow absorption assessment showing where AI fits current ways of working
- Adoption risk map identifying likely champions and likely stall points
- Skills gap analysis with role-specific development needs
- Readiness findings report with prioritized enablement recommendations
What buyers ask before scoping.
How is this different from the Stakeholder & Adoption Readiness Assessment?
That assessment is change-management generic: it maps stakeholder positions around any major change. This one is AI-specific: it measures actual AI skills, workflow fit, and the particular anxieties AI raises, like job security concerns that no generic change survey surfaces honestly. For an AI program, this is the sharper instrument.
Will our team feel like they are being tested?
Only if it is run carelessly, which is why we do not run it that way. The skills baseline is framed and communicated as input to training investment, not evaluation, and individual results are never reported to managers by name. Honest answers are the entire value of the exercise; we protect the conditions for them.
What do we get that we could not get from a staff survey?
Surveys measure stated attitudes, which for AI are notoriously distorted in both directions. We triangulate: survey plus interviews plus evidence from how past rollouts actually went. The gap between what people say about AI and how they work is exactly what the assessment is designed to expose.
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