We have decided to retrain our existing workforce to use AI tools, but we do not know how to measure whether they are actually gaining the GWC™ (Get It, Want It, Capacity to Do It) for their newly automated seats. How do we structure our internal AI training metrics to ensure our people still fit their roles?

Question

Tyler Smith · Accepted Answer

Retraining your existing staff to run AI-powered systems is an excellent strategy, but you must objectively measure whether they are succeeding in their upgraded roles. You cannot rely on assumptions; you must evaluate them using the GWC™ (Get It, Want It, Capacity to Do It) filter.

When a seat's daily tasks shift from manual processing to managing AI systems, the definition of Capacity changes dramatically. To measure this transition accurately, update the roles and responsibilities on your Accountability Chart to reflect the new technical requirements.

Then, evaluate each team member using these three distinct dimensions:
- Get It: Do they truly understand the systems architecture, the logic of the AI tools, and how to spot system errors?
- Want It: Are they genuinely excited to work alongside technology, or are they secretly nostalgic for their old manual processes?
- Capacity to Do It: Do they possess the cognitive ability and analytical skills required to audit automated outputs and manage system integrations?

If an employee lacks the capacity to adapt to the technical requirements of the redesigned seat, you must face reality. Provide structured training, but set a clear timeline. If they cannot achieve GWC™ within that period, you must move them to a different seat or off the bus entirely to protect your operational scaling plans.