When I arrived at Maryland Golf and Country Club a year ago, the kitchen was running on pure will. Staff were promoted before they were ready and lacked technique and guidance. Communication between staff was poor, the team was understaffed, and the operation was held together by sheer determination. What it lacked was a documented, repeatable, scalable infrastructure.
That’s the real story of my first year—not just cooking better food, but building the systems that allow a kitchen to perform consistently at the highest level, regardless of who’s on the line on any given night.
What I didn’t fully anticipate was how large a role artificial intelligence would play in that work. Not AI in the science fiction sense—no robots plating courses, no algorithms inventing recipes. What I’m talking about is AI as a genuine operational partner or assistant. A tool that helps an executive chef think more clearly, document more thoroughly, communicate more effectively, and build knowledge faster than previous kitchen leaders—myself included—could.
This is my first-person account of what it looks like in practice, at a real club, with real staff and members. It’s meant to help others start thinking about what the future may look like with AI in the kitchen.
AI didn’t replace anything—it amplified my ability to document, change, teach, and protect what works across every corner of the operation.
In my opinion, the private club kitchen has always faced a documentation problem. The expectation of quality is high. Members don’t always want restaurant food—they want food prepared the way they like it. But the infrastructure required to deliver that consistency has historically been almost nonexistent.
At some clubs, recipes live in the chef’s head, and that was somewhat true at Maryland Golf and Country Club. Station setups were passed down verbally. Checklists were informal. Banquet production plans weren’t even considered. Labor benchmarking wasn’t done at all. The result was a kitchen that worked when the right people showed up—and struggled when they didn’t.
My first priority was to change that—to build what I’ve come to think of as an institutional kitchen brain: a living library of recipes, systems, templates, and standards that the kitchen could run on independently of any one chef. AI—particularly Claude—became my primary tool for doing this work at speed.
The first major initiative was building a modernist cuisine–style recipe library for MGCC, inspired by some of the top chefs in the industry. The library is built on a standardized Excel workbook format: MGCC branding, structured ingredient columns with percentage scaling, procedure blocks, HACCP and critical control point documentation embedded directly into each recipe, and consistent formatting throughout.
Every recipe in the library auto-scales. Every procedure is written to a standard that allows a cook who has never made the dish to follow it successfully. Every critical point is flagged. AI has been essential to this process—not as a recipe generator, but as a writing and structuring partner. I uploaded every recipe in my personal files into Claude and refined the formatting. This also helped the system learn how I think and write. Now I can describe a dish or upload a menu, and AI helps translate it into clean, precise, publishable language at a pace that would otherwise be impossible.
Beyond the recipe library, the most impactful AI work at MGCC has been in operational systems—the templates, checklists, reports, and communication tools that govern day-to-day event execution.
Banquet execution at a private club lives and dies by preparation. The pre-event checklist we built structures the entire prep window into four milestones: 72 hours, 48 hours, 24 hours, and day-of, with specific assigned tasks at each stage. A pre-shift meeting agenda is embedded directly into the document so that the 15-minute kitchen meeting before each event runs identically every time, regardless of who is leading it.
Every banquet now closes with a structured post-event debrief. The document captures performance ratings across 16 categories, a root-cause issue log, staff recognition and coaching flags, food and beverage production notes, and a formal action item tracker. What AI enabled here was speed of design—building a document of this size would normally take weeks, if not months. Instead, Claude helped produce it in a single session.
The debrief system has changed how we grow. Issues that used to be discussed once and forgotten are now documented, assigned, and tracked to resolution. Staff wins that might have gone unnoticed are now captured and shared. Over twelve months, that becomes meaningful.
A recurring theme of my first year has been developing two key leaders—the chef decCuisine and the banquet chef—into autonomous managers. That development requires clear standards they can measure and execute independently. AI has been central to creating those standards quickly enough to matter.
The growth game plan I developed for the banquet program is a good example. It’s a multi-section document covering goals, development milestones, and event execution benchmarks. This would typically take a chef months of weekend writing to produce. AI compressed that timeline dramatically and helped me think through the structure of the plan.
To make this concrete, the following is an example of how AI-assisted documentation looks at the station level. The garde manger checklist below was built collaboratively with AI. I described the standards and sequence; AI helped structure and articulate them precisely. Every station in our kitchen now has a version of this document.
It would be dishonest to write this without being clear about the limits. AI is not a chef. It cannot taste. It does not know the member celebrating a 40-year anniversary whose favorite dish is a smash burger. It cannot build relationships with a line cook.
Hospitality, at its core, is human. What AI does is remove friction from everything around hospitality—the documentation, communication, planning, and benchmarking—so that chefs have more capacity to deliver the parts only chefs can deliver.
In my experience, that’s an enormous advantage. A kitchen that runs on clear systems and documented standards is a kitchen where the chef can actually be a chef—where high-level thinking can happen, rather than being consumed by operational grunt work.
A little over a year into my tenure at MGCC, the kitchen now runs on documented infrastructure that would have taken years to build. The recipe library is growing. The systems are being tested—and they’re working. The team is developing, and the quality of every plate leaving the kitchen is improving.
AI didn’t cook a single dish. But it helped me build a kitchen that cooks every dish better.
AI documented the standard. I hold the culture.
