AI WORKFORCE READINESS
AI readiness is not about turning everyone into an expert. It is about knowing where people are today, what level of capability their roles require, and how to build practical, responsible use across the organization.
Most organizations already have some combination of AI tools, experimentation and executive interest. What they often do not have is a clear picture of workforce capability. Some employees are already integrating AI into everyday work. Some are occasional users with no consistent process. Some are interested but have barely started. Some are using tools without clear organizational guidance. Managers are often unsure what “good” should look like for different roles.
AI adoption is not the same as AI competency.
AI competency is not binary. “User / non-user” and “beginner / advanced” are too crude to guide workforce strategy. The 7 Levels gives organizations a more useful progression model.
Level 1 — The Unaware
Level 2 — The Curious Observer
Level 3 — The Casual Consumer
Level 4 — The Purposeful Practitioner
Level 5 — The Integrated Power User
Level 6 — The AI Builder
Level 7 — The AI Developer
The assessment can be used at the individual level, but its real strategic value grows when it is applied across a workforce. Organizational outputs can include workforce distribution across all seven levels, team and business-unit heatmaps, role and job-family comparisons, current-level versus target-level gaps, training and governance routing, and an organizational AI-readiness view that leaders can revisit over time.
Not everyone should aim for Level 7. A technician, salesperson, manager, finance professional and executive can all be appropriately AI-capable at different levels. The point is to establish what capability is appropriate for the role — then develop toward it.
Move78 has mapped the 7 Levels against current public workforce research to create working regional distribution estimates for Canada, the United States, Europe and the global workforce. This work translates published research on AI awareness, frequency of use, workplace adoption and advanced application into the 7 Levels framework. It gives leaders an external reference point for internal results and helps distinguish a normal early-stage workforce from a material capability gap.
These regional views are benchmarking models built from public research, not a claim that every worker in those regions has completed the Move78 assessment.
Your best AI users are usually not the biggest problem. A handful of power users does not create an AI-capable organization. Raise the Floor focuses on increasing the practical baseline of capability across the broader workforce.
The 7 Levels tells you where people are. Raise the Floor helps you move the organization forward.
For Levels 1–3, targeted development builds confidence, practical usage, better prompting, verification habits, safe use and meaningful workplace application. For Levels 4–7, governance, leadership, mentoring and advanced-use guidance help stronger users raise capability without creating uncontrolled shadow practices. Organization-wide, the program establishes common language, role expectations, approved-use guidance and measurement that can be repeated as capability changes.
Raise the Floor is designed around doing, not watching. Learning can be customized around the work employees actually perform: writing, research, customer communication, analysis, meeting preparation, knowledge retrieval, documentation, decision support, workflow improvement and role-specific use cases.
The current on-demand model uses level-targeted tracks for Levels 1–3, a shared core spine, hands-on “Pause and Do” exercises, progress nudges, a light human check-in and reassessment so completion is tied to progression rather than seat time.
Privacy and confidentiality • Human review and verification • Approved versus prohibited use • Accuracy and hallucination awareness • Copyright and sensitive information • Role-specific governance and employee-facing usage guidance
Leaders get a baseline instead of assumptions, a common language for AI capability, clearer role expectations, targeted learning investment instead of one-size-fits-all training, a governance path for advanced users and a way to measure progression over time.
Employees get learning that starts at the right level, practical examples connected to real work, clear next steps instead of vague pressure to “use more AI”, better judgment about when to trust, check or reject AI output, and a visible path from curiosity to purposeful capability.
You do not need everyone at Level 7. But you probably cannot afford large parts of the organization remaining at Levels 1 and 2 while AI becomes embedded in everyday work.