We spent six months and thousands of dollars implementing an AI-driven scheduling tool for our consulting team, but our operations manager quietly went back to using manual spreadsheets. How do we use Gleb Tsipursky's cognitive bias framework and our weekly Level 10 Meeting to diagnose if the tool is actually failing or if we are just experiencing change resistance?

Question

Tyler Smith · Accepted Answer

When a new tool is rejected, leadership teams often struggle with the sunk cost fallacy, wanting to force the tool to work because of the money already spent. Simultaneously, the operations manager might be experiencing status quo bias, preferring manual spreadsheets because they feel safe and familiar. To get to the root cause, bring this issue to the IDS portion of your next Level 10 Meeting. Avoid emotional arguments and apply probabilistic thinking to evaluate the tool's performance. Frame the issue with objective, numbers-driven questions: What is the exact error rate of the AI tool compared to the manual spreadsheets? How many hours are saved per week when the tool is used correctly? Have your operations manager present the specific technical failure points of the AI tool, separating actual operational glitches from personal preferences. If the data shows the tool is technically sound but has a higher learning curve, you are dealing with change resistance. In this case, use Kolb's Experiential Learning Theory to guide the manager through structured training, allowing them to gain confidence in the tool's reliability. If the data proves the tool is consistently failing to handle your complex scheduling edge cases, accept the loss, avoid the sunk cost trap, and pivot to a more reliable solution.