# Franchisee reconciles cash in 94% less time with ChatGPT Work

Date: 20 Oct 2026
Released by: OpenAI

## MediaRelease.co Summary

OpenAI published a customer story on Ted Taylor, an independent franchisee running three Schlotzsky’s and Cinnabon restaurants in New Mexico. Using ChatGPT Work, he cut menu repricing from five days to one to two hours and monthly cash reconciliation by 94%. He estimates better scheduling could free up $50,000 to $100,000 a year.

## Original release

An independent Schlotzsky’s and Cinnabon franchisee cut repricing time by 95% and built a workflow that could free up $100K in cash flow. - 95%: Reduction in time required for pricing analysis, from 5 days to 1 to 2 hours - $100K: Maximum annual cash flow that could be freed up with optimized labor workflow - 4: Hours of manual work saved every payroll - 94%: Reduction in time spent on monthly cash reconciliation Ted Taylor entered the restaurant business after a 25-year career in the semiconductor industry. Today, he’s an independent franchisee operating three co-branded Schlotzsky’s and Cinnabon restaurants across New Mexico. Running quick-service restaurants means balancing the customer experience with a lot of work customers never see, like menu pricing, staffing, equipment maintenance, and policies. Ted uses OpenAI technology to get through all of it faster. With ChatGPT, Ted accelerates time-consuming tasks like research and drafting, and he uses ChatGPT Work to gather and analyze volumes of data across pricing, sales, forecasts, schedules, and labor and to get recommendations on where each restaurant needs attention, reducing repricing time by 95% and shaving 4 hours off payroll runs. “ChatGPT Work’s recommendations are so good that it’s much faster to start there and then make adjustments.” —Ted Taylor, independent franchisee, Schlotzsky’s and Cinnabon Repricing menus across three markets in minutes Repricing menus is an annual undertaking that requires a complex analysis of recipe costs, item popularity, local competitor pricing, portion sizes, and the different economics of in-store and delivery orders. Ted uses ChatGPT Work to bring those factors together and get pricing recommendations in 1 - 2 hours instead of 5 days. “It used to take me a few days of work to analyze and come up with item-level pricing; whereas, ChatGPT Work tears through it in a matter of moments and does it more thoroughly.” —Ted Taylor, independent franchisee, Schlotzsky’s and Cinnabon Ted first uses ChatGPT Work to research and establish his pricing targets and pricing strategies based on local competitors’ pricing trends, portion sizes, and relative food quality. He then gives ChatGPT Work a categorized list of menu items with their current in-store and delivery prices. He specifies how much he wants each set of prices to increase overall and which items should receive larger or smaller increases, largely based on the earlier analysis from ChatGPT. He can make the analysis more detailed by adding item-level sales history and supplier prices. ChatGPT Work can use these inputs to estimate sales impacts from price changes and to optimize item-level pricing around cost of goods targets. ChatGPT Work returns a spreadsheet with recommended in-store and delivery prices. It will even relate the pricing changes to an overall pricing strategy designed to optimize business outcomes like customer retention, minimizing price shocks, and best positioning of add-ons. The first time Ted used ChatGPT Work for pricing, he also ran the analysis manually. “ChatGPT Work made a deeper local comparison and helped me avoid increases that would have made some items uncompetitive,” he said. Ted immediately switched to using ChatGPT to develop the first recommendations. Finding ways to optimize labor and free up cash flow Ted uses ChatGPT Work to help manage labor costs in two ways: reviewing how labor is tracking across his three restaurants and testing a better way to build the weekly schedule. For the labor analysis, Ted gave ChatGPT Work read-only access to his sales and labor data. When he runs the analysis, it compares scheduled, budgeted, and actual hours with forecast and actual sales. It also reviews the previous 12 weeks to account for stores that routinely come in above or below their sales forecasts. The resulting report flags overtime, shifts running longer than planned, and gaps in coverage, and highlights what a manager should review. For the scheduling test, Ted gave ChatGPT Work an employee roster with each person’s role, training, availability, and pay, along with the store’s staffing needs at different sales levels. He and the manager compared ChatGPT Work’s proposed schedule with what she would have built, identified missing rules about when to add staff during busy periods, and refined the instructions for ChatGPT Work. ChatGPT Work now produces a full schedule for that location. The manager checks it over, makes adjustments if needed, and enters it into the restaurant’s scheduling system. Ted estimates that better scheduling could eventually reduce hourly labor costs by one to two percentage points of sales across all three restaurants, which could add $50,000 to $100,000 in free cash flow annually that can be utilized for increased pay, bonuses, investment in the business, or increased profit distributions. Accelerating back-office tasks with ChatGPT Work Ted also uses ChatGPT Work for day-to-day operations, from developing HR policies to reconciling cash and managing supply and repair requests. When New Mexico started requiring restaurants to keep a written procedure for bodily-fluid incidents, Ted used ChatGPT to write a draft in about 30 seconds. When Ted needed a social media policy for his employee handbook, ChatGPT helped him quickly draft guidance that an outside HR firm later reviewed without making any changes. Ted uses ChatGPT Work to reconcile cash monthly across his three restaurants. He uploads each store’s cash transaction data and bank records, and ChatGPT Work compares the transactions with the corresponding deposits. Monthly reconciliation used to take three hours, and now it takes 5-10 minutes. He also cut four hours of manual work from every payroll cycle by using ChatGPT Work to summarize payroll inputs. To help keep restaurants running smoothly, Ted also used ChatGPT Work to build a web portal where employees can request supplies or repairs. The portal notifies him through a dedicated email address and tracks each request’s status. More time to improve the business, not just run it Ted’s approach to using ChatGPT Work is practical for any small-business owner. He gives ChatGPT the same data and guidelines he’d use to make a decision and compares its output with how he’d do the work himself. As he gains confidence in the results, he expands his use to more tasks around the business, always reviewing the results before implementing. For Ted, the value of ChatGPT Work goes beyond hours saved. Now he can do analysis he might otherwise skip and spend more time on customers, employees, and growing the business.

## Key details

- Issued by: OpenAI
- Published: 20 Oct 2026
- Publisher country: United States
- Subject country/region: United States
- Topics: AI

Original source: https://openai.com/index/ted-taylor-restaurant
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