⚙ Hi {{first_name_tally|Operator}},

I want you to try something.

Grab a piece of paper. Draw a line down the middle.

On the left side, write down every person at your company who has real influence over your career. Your manager. Your skip-level. The cross-functional leads you depend on. Anyone who could promote you, fire you, or make your life miserable.

On the right side, write down every person outside your company who would take a call from you this week. Not LinkedIn connections. Not college friends who don't know what you do. People who actually know your work and would pick up the phone.

Most operators finish this exercise with 20-40 names on the left.

And 3-5 on the right.

That ratio should scare you.

Because one side of that list disappears the moment you leave your company. The other side travels with you forever.

Michael, our new partnerships manager (and a chef turned COO) and this weeks essay contributor, is one of those on the right for me.

- Rameel

Finding people who can implement them is the #1 bottleneck in AI implementations.

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No consultants pitching transformation decks. No agencies learning on your budget. Just advice based on real world case studies and introductions to vetted experts certified, vetted, and matched to your actual problem.

Every certified partner has multiple case studies verified by the DOI team.

After 100s of audits and AI roadmaps, Digital Operations Institute is ready to go whether you need implementation partners, full time talent in-house, or a second opinion on your quarterly plan.

Caramel is a one shot cook.

It’s a precise science: sugar at the right heat, cream ready, butter within reach, everything pre-measured and staged. Too slow or cold and the sugar crystallizes. Pour the cream too fast and you'll end up wearing it. Add a little water at the wrong moment and hot sugar starts spitting like napalm.

The hairless patch on my forearm is from learning this lesson.

Chef didn't yell when he saw me clutching my arm. The cream still in the walk-in, the thermometer buried under towels, the salt nowhere. All he said was:

Mise en place means "everything in place." It's the first thing they teach you in cooking school and the last thing most people internalize. Because it feels like the work before the work. The boring part you rush through to get to the real thing.

Here's the paradox: mise en place isn't prep. It is the work. The cooking is just the performance.

This week, Anthropic drove the lesson home by launching Claude Cowork AKA "Claude Code for non-code work." You give Claude access to a folder on your computer, and it can read, edit, and create files inside it.

The model isn't the bottleneck anymore. Your folder structure is. 

Now, mise en place defines the slope of your AI adoption curve.

If your knowledge base is a swamp of "Untitled doc (3)" and stale SOPs from two reorgs ago, the agent becomes confidently wrong. The forever game for AI isn't a chat window. It's your documentation showing the way, the requirements, and the process from the work you are doing.

Every wrapper you build today has a half-life. Internal tool, prompt, or automation doesn’t matter. They will all be replaced in time.

Models improve. APIs change. Someone ships a better tool. But the prep work compounds: how things flow, who owns what, where taste gets applied, what good data looks like. Those survive platform churn. 

The foundation organization often doesn’t get done because there is always a sexier win. Who wants to spend days sorting folders and documenting decision rights? 

That's intern work (5 years ago). 

If you want to scale smoothly, then you need to organize the system yourself because mise en place requires taste along with understanding the final dish. And those cannot be delegated.

If your tools can't talk to each other and nobody owns the question of whether they should, then you are stuck before you start.

Maybe it’s a "We can export a CSV" (but only if Bob is online). Or maybe it’s a 20 year old SAP implementation. Most teams hit their AI ceiling before they write a single prompt because they do not have integratable tools. 

Legacy manufacturing has it worst, stuck with ERPs or custom builds from a decade ago.

It takes about 1 month for each year of legacy data. A company with 30 years of history? Plan on a year or two to reach real data clarity. I’ve seen it play out in venture backed startups and decades old holding companies the same.

As the information compounds over time, it also duplicates and obfuscates. 

Most companies get stuck because they grew without understanding their own mise en place. Scattered databases. No cleaning processes or hygiene checks. Multiple "sources of truth" that contradict each other. 

Data ownership means choosing one source of truth per thing (Process, department, object type. Take your pick and don’t duplicate data). You have to enforce it too, so no letting sales keep using their spreadsheets instead of the CRM. 

It means building hygiene into the pipeline because cleaning can't be a quarterly project; it needs to happen at input and be checked at each transfer.

Take an hour with your coffee tomorrow and organize your Google Drive. Truly, it will pay off in spades next time you use deep research. Put every major project in its own folder with consistent naming. 

That's the difference between "Claude, research Project X" returning garbage and returning gold.

The kitchen taught me what most consultants miss: mise en place isn't just "everything in its place." It's everything in its place at the time you'll need it.

With caramel the cream is closer than the butter because it goes in first. The thermometer is visible because you check it constantly. The staging is sequenced to the critical path.

That's why this is an operator's job. Not IT's job. Not a CEOs job. Yours.

You're the only one who sees the whole kitchen. You know what fires first, what can wait, what dependencies exist between departments, what data has to be clean before the downstream process works. 

The critical path lives in your head.

Tools get replaced. Models get replaced. Wrappers get deprecated. But the mise en place of your operations - your process architecture - that doesn’t change. 

That’s your forever layer.