Should restaurants use AI for menu optimization or trust chef intuition and customer feedback? | AI Insights | Lumman.ai

The next time someone tells you their restaurant has a five-year AI plan, ask them if they also planned their 2028 asparagus risotto in advance. Did they workshop the garnish in a Google Doc? Maybe scheduled quarterly reviews to “iteratively ideate flavor synergies”?

Please.

We’ve got it backwards. Most restaurants don’t need AI manifestos. They need better instincts—sharpened by data, not replaced by it.

Let’s talk about the real tension here: Should restaurants trust chef intuition and good old-fashioned customer feedback? Or embrace AI to tweak menus based on weather patterns, guac sales, and how many people like taking selfies with the truffle fries?

Wrong question.

There is no battle between chef and algorithm. The battle is between stagnation and experimentation.

The Myth of the Infallible Chef

Let’s shatter the sacred illusion first.

"Chef intuition" sounds romantic. You picture a genius in whites, closing their eyes and summoning the next great flavor like it’s a Michelin-starred vision quest. And hey, sometimes it is.

But mostly? Chef intuition is just pattern recognition filtered through ego.

A new dish gets put on the menu because “it killed at that pop-up in Brooklyn three summers ago.” Or because the chef had a formative childhood memory of pickled ramps. That’s fine when you're staging an edible art piece at a $400-per-head tasting menu.

But in a neighborhood bistro or fast-casual spot clawing for margins? That same intuition often spawns a graveyard of personal favorites no one ever actually orders.

Walk into the back of house, and you’ll find evidence: overstocked squid ink, rarely-used beet foam, a special no one's sold since February.

Nostalgia doesn’t pay rent.

AI Doesn’t Kill Creativity. It Kills Delusion.

Here’s where AI actually shines: It whispers what people are doing, not what they say.

Customers tell you they’re “cutting back on carbs,” then add a side of fries.

They’ll rave about your unique seasonal risotto in reviews—and proceed to order the exact same Caesar salad they always get.

The chef sees compliments. AI sees check totals. That matters.

Used right, AI highlights the gap between intention and action. Not to overwrite the chef’s vision, but to sharpen where it lands.

One fast-casual chain noticed that people ordering veggie bowls with guacamole often ditched the rice. That wasn’t in a customer survey—it emerged from pattern detection across millions of micro-decisions. The result? A “keto power bowl” that crushed it.

No chef predicts that with gut feeling alone.

The Sweet Spot is Tension

Not between humans and machines.

Between what feels true and what the data proves.

Think of AI as a spreadsheet-obsessed sous-chef. Its job isn’t to dream up the next big dish. It’s to say, “Hey, boss... this lamb ragu? We’ve sold 4 in 30 days. And everyone expected gnocchi.”

That creates a productive friction. Suddenly, data is a mirror for your assumptions. Sometimes the reflection confirms your genius. Other times, it tells you your sacred menu item is actually dragging down throughput, confusing diners, or just... not worth it.

The beauty is, the machine doesn’t care. It doesn’t need your dish to succeed to feel validated.

But you? You do.

So keep creating. Just use better inputs.

The McDonald’s Principle

Say what you will about the Golden Arches, but they didn’t conquer the world on feel alone.

McDonald's didn't ask chefs for their intuition on regional preferences—they used AI via Dynamic Yield to restructure digital menus in real time. Weather, time of day, even local traffic—all play into what gets pushed to the top of the menu screen.

It’s not just algorithmic wizardry—it’s survival. Lower wait times. More high-margin items sold. Zero guesswork.

For smaller restaurants, the takeaway isn’t “copy McDonald's.”

It’s: You don’t need a crystal ball. You need a daily experiment.

Even something as modest as seeing which specials get photographed most on Instagram can influence layout decisions, section placement, or garnish drama. You want to know what your menu’s clickbait is—and position it accordingly.

Why The Five-Year AI Plan is a Lie

“Five-year AI strategies” sound good to executives, but they’re mostly theater. Elaborate PowerPoints with meaningless timelines and ambitious buzzwords—while your competitors are quietly testing real-time recommendation engines on a rainy Tuesday.

AI evolves too fast for roadmapping. By the time you lock a strategy, the tech it’s built on is outdated.

Good restaurants don’t build their next five years in a Gantt chart. They build their next five nights by trying something different right now.

Dynamic pricing on slow nights? Try it for a month.

Inventory optimization that flags which ingredients go stale first? Set it up today.

Forget committees. Build muscle memory.

Chess, But Make It Dinner

When AI beat Garry Kasparov at chess, people freaked out about the death of human mastery.

What happened instead? "Centaur chess" was born—humans and AIs playing together, outperforming either side alone.

That’s your metaphor.

You want centaur cuisine: a chef making bold, expressive decisions—but checking them against a machine unmoved by nostalgia, bias, or menu politics.

Let AI suggest cutting the tuna tartare (low margin, inconsistent quality), and let the chef respond: “Cool. Let’s rebuild the idea with local trout, and do it better.”

Now you’re playing in the future.

The Real Danger

It’s not using AI.

It’s using it without taste.

Feed your system garbage data (“POS” systems that miss modifiers, inconsistent feedback tags, or Yelp reviews riddled with sarcasm), and AI becomes a very confident fool. It'll normalize blandness. Average everything out. Kill the weird stuff that might’ve popped with just a little refinement.

Cue airport food: efficient, inoffensive, totally forgettable.

Remember: AI optimizes. Humans radicalize.

Let the machine find efficiencies. Let the chef rip the rulebook when inspiration strikes. That's the dance.

When Data Gets It Right

Real-world examples sell the clearest truth. A few worth remembering:

Sweetgreen notices that adding roasted sweet potatoes increases chances of an upsell by 12%. A small ingredient tweak turns into six figures in revenue annually.
A fast-casual brand finds veggie bowl lovers are dropping rice but paying more. Boom: new top-selling keto dish.
Starbucks matches regional orders to weather data and past purchases. You hit “buy” before you know why you were craving cold foam.
A Chicago bistro uses AI to track which specials get photographed most. Suddenly, they know which plates are visual bait—and they lean into it hard, boosting both foot traffic and social shares.

None of this replaces a chef. But all of it expands what’s possible.

What All This Means

If you’re still trying to pick a side—chef vs. algorithm—you’re fighting the wrong war.

This isn’t a debate about authenticity or automation. It’s about relevance.

The best chefs aren’t threatened by AI. They’re curious about it. Savvy enough to know their intuition needs feedback. Confident enough to make the final call.

So ask yourself:

Are you designing your menu with information or instincts? Why not both?
Is that legacy dish beloved... or just legacy?
If a machine told you your top seller was only popular during thunderstorms, would you be surprised? Would you test a new promo the next time it rains?

Let’s End Where We Should: With the Diner

Nobody walks into a restaurant thinking, “God, I hope this place nailed its AI implementation strategy!”

They want to feel something.

To be surprised. To taste something memorable. To be seen.

AI’s job is to make that easier—by clearing out the noise, tracking the patterns, and letting the humans do what only they can: create meaning through meals.

In the end, restaurants aren’t about data, or margins, or IPs.

They’re about experience.

So build one worth having.

And then, maybe, optimize it.