ChatGPT Work cuts franchisee repricing 95%, cash reconciliation to 5–10 minutes

Ted Taylor came to restaurant ownership after a 25-year career in the semiconductor industry, and his approach to ChatGPT Work reflects that background: he gives the tool the same data and guidelines he would use to make a decision, then compares its output with his own judgment before acting. That discipline shows up in how he built the pricing workflow. He first used ChatGPT Work to research local competitors' pricing trends, portion sizes, and relative food quality to set targets, then supplied item-level sales history and supplier prices so the tool could estimate sales impacts and optimize around cost-of-goods goals. The first time he used it for pricing, he also ran the analysis manually; he says ChatGPT Work made a deeper local comparison and helped him avoid increases that would have made some items uncompetitive, and he immediately switched to using it for first recommendations.

The labor side followed a similar pattern of iteration. For scheduling, Taylor gave ChatGPT Work an employee roster with each person's role, training, availability, and pay, along with staffing needs at different sales levels. He and the manager compared the proposed schedule with what she would have built, identified missing rules about when to add staff during busy periods, and refined the instructions. ChatGPT Work now produces a full schedule for that location, which the manager checks and adjusts before entering it into the scheduling system. Taylor frames the broader value not just as hours saved but as capacity to do analysis he might otherwise skip.