I'm Donnie McClanahan. Former Multilocation food service operator, Square AI Champion, and the person bridging the gap between restaurant operations and practical AI implementation. Not hype. Not theory. Operational results.
The operation is the source of truth. The software is the narrator.
If the AI cannot explain its answer in line cook English, it is not finished.
A dashboard nobody opens on Saturday at eight is not a dashboard.
Operational knowledge is the signal. AI is the screen. Both matter.
I've spent 20 years running restaurants: traditional cafés, QSR, corporate dining programs, government food service contracts, and automated micro markets. Multiple locations, every operational problem you can name, and a few you can't.
I was one of Square's earliest AI beta testers. I helped shape their AI product from prototype to public launch, and my work has been featured across Square's newsroom and product publications. I also conducted an independent security assessment that produced a CVSS 7.8-rated vulnerability report, because I'd rather find the cracks before my clients do.
Through Table & Ledger, I help food service operators stop guessing and start using AI to make real decisions. The biggest shift isn't the technology. It's the mindset.
Published contributor on Square's business insights blog with a dedicated author profile.
View on Square →Featured case study on using AI for menu optimization, labor analysis, and data-driven operational decisions.
Read Article →Operational case studies from the beta program. The AI query that saved a lunch shift and uncovered hidden margin opportunities.
Read Article →Quoted as a working operator debunking common misconceptions about AI adoption for small business owners.
Read Article →"The software of the last twenty years made operators click their way to information. The next wave does not ask the operator to go looking. The AI becomes the interface, and operational knowledge decides what it looks like."The landscape of AI is changing so fast this article is developing in real time.
Observations from the intersection of restaurant operations and AI implementation. Operator-level. No hype.
AI in restaurants needs three layers, not two — a system that watches, a system that remembers, and a system that acts. The platforms that bet on open are betting on the operators who matter most over the next decade.
Read →Toast just spent seven figures telling every operator in New York that they are built for busy. The campaign is a brand play. It is also a tell about the AI bet Toast is making for the next three years.
Read →Square just announced ManagerBot publicly this week. I have been using it for weeks. Here is what it actually feels like to manage restaurants with an AI agent riding shotgun.
Read →I work at the intersection of restaurant operations and AI implementation, translating what the technology can actually do into language and systems that make sense for the people running the business.
Practical deployment of AI tools in food service operations. Menu optimization, labor analysis, cost management, and data-driven decision making using platforms operators already have.
Capability elicitation and product architecture analysis for restaurant technology platforms. I find the edge cases, the architectural gaps, and the disconnect between how engineers build and how operators work.
Independent security assessments of AI assistants and business tools. Red team methodology combining social engineering with systematic capability testing.
Multi-location food service consulting. Systems-first approach to staffing, cost control, menu engineering, and workflow optimization. Built on a decade of doing it, not theorizing about it.