The restaurant line was distributed computing before anyone called it that.
I'm Donnie McClanahan — multi-location 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.
I've spent 20 years running restaurants: traditional cafés, QSR, corporate dining programs, government food service contracts, and automated micro markets. Multiplee 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.
Featured in Square's official publications
Named Author Page
Published contributor on Square's business insights blog with a dedicated author profile.
View on Square →Building the Future with Square AI
Featured case study on using AI for menu optimization, labor analysis, and data-driven operational decisions.
Read Article →Square AI Beta Insights
Operational case studies from the beta program — the AI query that saved a lunch shift and uncovered hidden margin opportunities.
Read Article →Top 5 Myths About AI and Small Business
Quoted as a practitioner debunking common misconceptions about AI adoption for small business operators.
Read Article →"AI doesn't replace your judgment — it gives your judgment better information to work with."Donnie McClanahan
Where operations meets intelligence
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.