We are trying to roll out an AI tool that assists our senior estimators in pricing complex construction projects, but they refuse to trust the software's recommendations and prefer their old spreadsheets. How do we use Kolb's Experiential Learning Theory to design a training loop that helps them validate the AI without feeling like their expertise is being replaced?

Question

Tyler Smith · Accepted Answer

Your senior estimators are resisting the AI tool because they feel their years of accumulated expertise are being dismissed by a black box. To break through this resistance, you must guide them through Kolb's Experiential Learning Theory, which moves individuals through a four-stage cycle: concrete experience, reflective observation, abstract conceptualization, and active experimentation.

Do not start by telling them the AI is smarter than their spreadsheets. Instead, begin with a low-stakes concrete experience. Have your estimators run five completed historical projects through the AI tool and compare the software's estimates with their actual past results.

Next, facilitate reflective observation. Ask them to analyze where the AI got the numbers right and where it went wrong. This respects their expertise, placing them in the role of the expert auditor rather than a passive data entry clerk.

Move to abstract conceptualization by helping them formulate rules for when to trust the AI and when to override it. Let them build the manual validation steps that must be completed before any automated estimate is sent to a client.

Finally, encourage active experimentation by having them use the AI on upcoming live bids, running the tool parallel to their traditional spreadsheets. This cyclical learning process allows your veteran team members to build trust in the technology at their own pace, ensuring they retain ownership of the pricing process on your Accountability Chart.