The Michelin Paradox
Davos has a way of compressing the future into a few days. This year, the contrast was unusually sharp.
On one side: the model providers. They are moving at an extraordinary rate. Capability jumps are arriving faster than most enterprises can absorb, and the confidence in more general intelligence has quietly shifted from if to when. You can feel it in the demos, in the roadmaps, and in the commercial momentum.
On the other side: the enterprise leaders I spoke with. Smart, motivated, well-funded. And still stuck. Not stuck on whether the technology works, but on how to use it consistently, safely, and at scale. Many are wedged between what these systems can do and what their organizations can reliably deliver.
That gap is not primarily a technology constraint. It is an operating constraint.
And here’s the uncomfortable part: many AI products assume a level of coordination and readiness that most organizations have never needed before. Those assumptions are often implicit. That’s not an excuse. It’s a diagnosis that demands action. To win, we have to make those assumptions explicit and build the systems to meet them.
A kitchen story about AI
Here’s the metaphor I keep coming back to. In a Michelin-star kitchen, you can buy the best knives and ovens in the world and still cook badly. Because the craft is not the knife.
The craft is the system: prep, timing, standards, coordination, station ownership, quality checks, service cadence, and the discipline of doing the same thing the same way until it becomes muscle memory. Great tools do not create a great kitchen. They amplify a great kitchen.
AI is similar. AI tools are not the work. The work is the operating system around them.
So what do leaders do, practically, to close the gap?
Make speed a strategy, not a slogan
When technology advances faster than your operating model, “we’re moving fast” becomes meaningless. What matters is cycle time from decision to shipped capability, measured in weeks, not quarters.
Moves that actually change speed:
Run fewer bets at a time. If everything is priority-one, nothing is. Pick a small number of outcomes and starve the rest on purpose.
Shorten the distance between decision and build. Tighten the handoff chain. Fewer committees. Fewer alignment loops. Fewer approval layers.
Create a single weekly service cadence. A recurring rhythm where you review what shipped, what broke, what you learned, and what you are changing next week. No theater. Just evidence.
Treat reuse as a first-class deliverable. Don’t fund dozens of near-identical copilots. Fund shared capabilities that can be consumed across teams.
A useful test: if you paused all new planning for two weeks and asked, “What can we ship with what we already have?” would anything meaningful land? If not, you have an operating problem.
Optimize for outcomes, not checklist progress
Many organizations are building AI programs that feel comforting and produce artifacts: frameworks, policies, training decks, steering committees. Some of that is necessary. But it is also an easy way to look busy without changing work.
Moves that keep you honest:
Pick business measures you already care about (cycle time, defect rate, time-to-insight, win rate, customer satisfaction) and force AI to move those measures.
Stand up tight feedback loops where real users evaluate AI in real workflows every week. Not surveys. Observed use.
Define what good looks like per workflow in plain terms. Example: “Draft a first pass in two minutes, cite sources, flag uncertainty, hand off cleanly to review, and cut review time by 30%.”
Instrument the work, not the narrative. Track adoption, completion, latency, error modes, escalation rates, and human time saved in the flow of work.
If AI progress cannot be seen in existing operational metrics, it is not progress yet.
Build the kitchen, not just the menu
AI does not just add a tool. It changes how decisions get made, how work gets decomposed, and what quality means. The hard part is not building one impressive demo. The hard part is making one new way of working repeatable.
Moves that institutionalize change:
Make a small number of behaviors non-negotiable. Document decisions. Use shared components. Log exceptions. Ship weekly. Measure outcomes.
Clarify decision rights. Who approves production use? Who owns risk calls? Who can reallocate engineering capacity? Ambiguity creates delay.
Build line-cook roles, not just head-chef roles. You need operators who can deploy, monitor, improve, and support AI in production, not only visionaries.
Create a single front door for production AI. One path with clear gates: safety, privacy, evaluation, monitoring, and support. Standardize the boring parts (identity, data access, prompt management, eval harnesses, observability, audit logs) so teams can move fast on the interesting parts.
AI success looks boring when it’s real. It looks like consistency.
A note to the model providers
Model providers have earned their momentum. But there is a growing mismatch between the pace of model iteration and the operational scaffolding enterprises need to succeed.
It is not tenable to ship increasingly powerful systems and leave customers to assemble the operating system alone.
Moves where providers can help:
Make operational assumptions explicit. What governance model does this require? What skills? What monitoring? What failure modes should teams expect?
Ship evaluation and observability as defaults. Not optional best practices, but baked-in primitives.
Offer migration-safe interfaces. Enterprises need stability across versions, predictable behavior changes, and clear upgrade paths.
Provide reference workflows, not just reference prompts. Show complete patterns: data access, tool use, escalation, human review, auditability, and measurement.
The winners will not just build better models. They will build better conditions for success. Not for nothing, but the customers I spoke to are desperate for the help.
Bringing it back to the kitchen
My takeaway from Davos is simple: the knives are getting sharper every month. But most kitchens are not set up to serve at that speed.
Leaders who close that gap will treat AI like a craft discipline, not a tool rollout. They will build the prep stations, define the standards, instrument the line, run a weekly service cadence, and make reuse the default.
Great tools do not make a great kitchen. But in a great kitchen, great tools change what’s possible.
And right now, the opportunity is to become the kitchen that can actually use them. Rare opportunity ahead.