Independent franchisee Ted Taylor, formerly a 25-year semiconductor industry veteran, runs three co-branded Schlotzsky’s and Cinnabon restaurants in New Mexico. He uses OpenAI technology, specifically ChatGPT and ChatGPT Work, to speed up back-office tasks that customers never see: menu pricing, payroll, labor analysis, cash reconciliation, policy writing, and supply or repair requests. The story reports concrete reductions: monthly cash reconciliation is down from about three hours to 5–10 minutes, a 94% reduction; menu repricing that used to take five days is done in 1–2 hours; repricing time is reduced by 95%; and each payroll run is four hours shorter.

For menu repricing, Ted first uses ChatGPT Work to research pricing targets and strategies based on local competitors’ pricing trends, portion sizes, and relative food quality. He then supplies a categorized list of menu items with current in-store and delivery prices, specifies how much overall prices should increase and which items need larger or smaller increases, and can add item-level sales history and supplier prices. ChatGPT Work returns a spreadsheet with recommended prices and can estimate sales impacts from price changes, optimize around cost-of-goods targets, and relate changes to strategy concerns such as customer retention, avoiding price shocks, and positioning add-ons. On his first pricing run, Ted did the analysis manually in parallel and found ChatGPT Work‘s local comparison deeper; it helped him avoid increases that would have made some items uncompetitive, so he switched to using it for first recommendations.

On labor, Ted gave ChatGPT Work read-only access to sales and labor data. The analysis compares scheduled, budgeted, and actual hours against forecast and actual sales, reviews the previous 12 weeks to catch stores that routinely run above or below forecasts, and flags overtime, shifts running longer than planned, and coverage gaps. For scheduling, Ted supplied an employee roster with role, training, availability, and pay, plus staffing needs at different sales levels. He and the manager compared ChatGPT Work‘s proposed schedule with the manager’s version, identified missing rules about adding staff during busy periods, and refined the instructions. ChatGPT Work now produces a full schedule for that location, which the manager reviews and adjusts before entering into the restaurant’s scheduling system. Ted estimates better scheduling could eventually reduce hourly labor costs by one to two percentage points of sales across all three restaurants, adding $50,000 to $100,000 in annual free cash flow that could go toward pay, bonuses, investment, or profit distributions.

Ted also uses ChatGPT Work for day-to-day operations. When New Mexico began requiring restaurants to keep a written procedure for bodily-fluid incidents, he drafted one in about 30 seconds. He drafted a social media policy for the employee handbook that an outside HR firm later reviewed without changes. To reconcile cash, he uploads each store’s cash transaction data and bank records, and ChatGPT Work compares transactions against deposits; what used to take three hours now takes 5–10 minutes. He also built a web portal with ChatGPT Work where employees can request supplies or repairs, with notifications and status tracking.

His workflow is deliberately conservative: he gives ChatGPT Work the same data and guidelines he would use, compares its output with how he would do the work, and reviews results before implementing. The article is a customer story rather than an independent benchmark, and the labor-cost figure is Ted’s estimate rather than a measured result.