Challenging a Partner's Read on an AI Business Case

Kanishk was building a business case model for a car repair company evaluating the implementation of an AI-powered vehicle scanning device. The device could scan a car and identify defects, reducing the cost of servicing, appraisal, and overall vehicle acquisition by surfacing issues that a technician might miss.

He projected a savings figure of roughly $300 per outlet. When he presented the model, the partner challenged the number, arguing that because the company primarily operated in APAC, where labor costs are significantly lower, the savings estimate seemed too high. It was a fair point on the surface.

Kanishk had anticipated the labor cost angle in his research. His model captured two distinct benefit streams: the direct cost savings from reduced technician time and appraisal costs, and a tertiary benefit that the partner had not factored in. The AI device's ability to identify more defects than a human technician also reduced the overall acquisition cost of vehicles, opening up a new business opportunity for the company that went beyond simple cost reduction.

Rather than adjusting the model to match the partner's expectation, Kanishk walked through the two-layer logic, acknowledged the partner's point on labor costs, and explained why the second benefit stream was material and distinct from the wage-driven savings. He also offered an alternative, more conservative figure that could be used if both parties remained unconvinced, keeping the conversation constructive.

The partner accepted the reasoning. The tertiary benefit stream was retained in the final model.