I am a big fan of Kitchen Impossible hosted by Tim Mälzer. Not because I love cooking, but because the program reveals what makes a chef’s expertise so impressive.
In each episode, accomplished chefs send one another to unfamiliar places, where the challenger receives a black box containing a local dish. They can taste, smell, inspect, and take it apart, but they are not given the recipe or a list of ingredients. Working in an unfamiliar kitchen and under severe time pressure, they must determine how the dish was prepared, reconstruct it, and serve their version to people who know the original.
What fascinates me most is how differently the chefs respond to this uncertainty.
Some commit quickly to their first interpretation and begin preparing the complete dish. Others treat the meal inside the box as a system that must first be understood. They separate its components, form hypotheses about ingredients and techniques, and conduct small experiments before committing to the final version.
Even under time pressure, some chefs prepare several variations of a sauce, dough, filling, or cooking method alongside one another. They alter the acidity, temperature, proportions, seasoning, or preparation technique and compare what happens. Each variation helps them understand what a particular variable contributes to the result.
They are not simply searching for something that tastes good.
They are trying to understand causality.
That process tells me something important about expertise. Expertise is not only the ability to recognize the correct answer immediately. It is also the ability to recognize what remains “known unknown”, turn uncertainty into testable hypotheses, and design small actions that produce useful evidence.
This is where innovation work frequently goes wrong.
Instead of preparing small variations and learning what causes what, organizations make one large bet. They commit the budget, build the complete solution, prepare the launch, and invite a large audience to an expensive dinner. Only when the meal is served do they discover that what they prepared does not work, nobody particularly likes it, and the evening has become difficult to recover.
The problem is not necessarily that the first interpretation was foolish. In genuinely uncertain situations, the first interpretation will always be incomplete.
The problem begins when the team treats an untested interpretation as though it were already a reliable recipe.
Tim Mälzer did not know the seasoning, so he created variations
One episode illustrates this experimental mindset particularly well.
In the second episode of the 2016 season, Meta Hiltebrand sent Tim Mälzer to the Zerda Café in Paris. He had to reconstruct a fish couscous and a pigeon pastilla from the Maghreb culinary tradition. He struggled to identify the ingredients and had little confidence that he understood how the dishes had been seasoned.
Instead of selecting one seasoning mixture and committing the entire dish to it, he conducted a small test with several spice combinations. He used the variations to move closer to the flavor he had experienced in the black box. The result was not a perfect reconstruction, but the jury still awarded the first challenge an average of 4.9 points. (rtl.de)
What matters to me is not the score. It is what he did when his expertise met a situation it could not solve through immediate recognition.
He did not stop being an expert because he did not know the answer. His expertise became visible in how he responded to not knowing.
He turned one large, unclear problem into several smaller comparisons. Instead of asking only, “Which seasoning is correct?”, he created alternatives that allowed him to ask, “What changes when I alter this combination?”
That is a very different question.
The first demands an answer. The second creates questions and information.
The distinction is central to innovation. A team can spend months asking whether its complete concept is right. A more useful approach is often to separate the concept into assumptions and ask which small variation would make one of those assumptions more observable.
A chef can compare several spice mixtures. An innovation team might compare different customer groups, problem descriptions, levels of service, pricing structures, onboarding methods, or commitments required from a potential buyer.
The purpose is not to create endless alternatives. It is to make differences informative.
We once turned our entire apartment building into a black-box kitchen
I once initiated a more playful version of this idea in the apartment building where I live.
Each apartment received the same collection of ingredients, which had been chosen randomly by different people. Every household then had to create a dish from them. The apartments on the lower floors prepared the appetizers, those in the middle prepared the main courses, and the apartments on the upper floors were responsible for dessert.
The evening gradually moved upward through the building as we ate our way from floor to floor.
The ingredients were not selected to make the task easy. They included things such as cornflakes and other items that would not naturally appear together in an obvious recipe. Nobody could simply retrieve a familiar dish and follow the instructions.
Everyone began from the same raw material, but each apartment interpreted the possibilities differently. One household emphasized texture, another sweetness, another contrast, and another found a way to transform an ingredient so that it no longer played its familiar role.
It was not a controlled scientific experiment. We did not standardize quantities, equipment, cooking skill, or preparation time closely enough to make reliable causal claims.
Yet it revealed something that innovation teams regularly forget: the ingredients do not determine the outcome by themselves.
Interpretation, sequencing, technique, proportion, substitution, and combination all matter. Two teams can begin with apparently identical resources and produce completely different outcomes because they have formed different hypotheses about what those resources can become.
The evening also showed why constraints can stimulate exploration and its fun at the same time. Nobody could retreat comfortably into the recipe they already knew. We had to examine what was available, understand the properties of unfamiliar combinations, and improvise.
The strange ingredients were not an inconvenience added to the experiment.
They were what made the experiment useful.
Following a recipe and reconstructing a black box are different kinds of work
Cooking offers a useful way to distinguish three situations that organizations often manage as though they were the same.
In the first situation, you possess a reliable recipe, the specified ingredients, familiar equipment, and a controlled environment. The outcome is never perfectly guaranteed, but the relevant causal relationships are sufficiently understood. If you follow the process competently, you can expect a recognizable result.
This resembles delivery work. The customer, problem, solution, business model, and operating process are understood well enough that the central challenge is execution.
In the second situation, part of the recipe remains known, but you must substitute one ingredient or alter one condition. Perhaps the prescribed fat is unavailable, the flour behaves differently, or the oven produces less predictable heat. The uncertainty is limited, and you can isolate it reasonably well.
This resembles an improvement initiative or a relatively bounded product experiment. The overall system is understood, but one component needs to be tested.
The third situation is the Kitchen Impossible situation. You do not possess the recipe, some ingredients are unfamiliar, the equipment may behave differently, and you have to infer the preparation method from the result in front of you. The only thing you own is your long term adapted and gained expertise.
This is exploration.
The problem is that organizations frequently manage the third situation as though it were the first. They create roadmaps, deadlines, resource plans, and performance targets as if the recipe already existed and the team merely needed to execute it correctly.
When the result disappoints, the first conclusion is therefore often that execution was weak.
Perhaps the team did not cook hard enough.
Adaptive expertise begins when the familiar routine is no longer sufficient
Research on expertise distinguishes between performing efficiently in familiar situations and adapting effectively when the task, method, or desired result is novel.
A systematic review by Bohle Carbonell and colleagues describes adaptive expertise as the ability to maintain high performance when tasks or working methods change. Compared with routine expertise, adaptive expertise is associated with more flexible problem solving, stronger metacognitive abilities, continuous learning, and an understanding of why a method works and under which conditions it should be modified [1].
That distinction helps explain what makes the experimental chefs so impressive.
A routine expert may execute a familiar recipe with extraordinary precision. An adaptive expert can enter an unfamiliar kitchen, encounter a result they cannot immediately explain, and use what they already know to construct a path toward understanding.
The difference is not that the adaptive expert lacks routines. Deep domain knowledge remains essential. Tim Mälzer could not perform the black-box challenge without decades of experience with ingredients, heat, texture, timing, and taste.
However, he does not treat that experience as proof that his first answer must be right. He uses it to generate better hypotheses and more informative tests.
This matters for innovation because experience can play two opposing roles. It can expand the range of patterns a person recognizes, or it can make the first familiar explanation feel too convincing.
Adaptive expertise keeps experience available without making it untouchable.
Large bundled bets produce outcomes but little understanding
Many corporate pilots and startup launches are designed like complete dishes.
The team selects a customer segment, defines a problem, builds a solution, sets a price, chooses a channel, prepares a message, and creates a delivery process. It then presents the entire combination to the market and waits to see what happens.
When the result is positive, everyone celebrates. When the result is negative, the arguments begin.
Was the customer wrong, or was the problem poorly described? Was the proposition unattractive, or did the customer simply distrust the company? Was the price too high, or was the expected value too low? Did the channel fail, did the sales team fail, or was the purchasing process impossible from the beginning?
The team has generated an outcome, but the design of the activity makes it difficult to understand what caused it.
Experimentation research has long treated experimentation as a fundamental innovation and problem-solving activity. Stefan Thomke’s study of product development showed that different experimentation strategies can produce substantial differences in development time and cost. In the technical setting he examined, an approach enabling many prototype iterations outperformed a less flexible approach, with a significant portion of the difference attributable to the experimentation strategy itself [2].
Later work by Thomke and David Bell framed product-development tests as information-generating activities and showed that the appropriate frequency and fidelity of testing depend on the cost of each test, the cost of redesign, and the relationship among sequential tests [3].
The managerial implication is not that every organization should run as many tests as possible.
It is that the sequence and design of tests determine how economically the organization learns.
A large pilot that mixes several assumptions may look more realistic, but it can produce less useful information than a series of smaller tests designed around specific uncertainties.
Realism and learning value are not always the same.
Small experiments should reduce uncertainty, not merely create activity
The language of experimentation has become common in innovation. Teams speak about MVPs, prototypes, pilots, tests, and validation.
Not everything described as an experiment actually functions as one.
An activity becomes informative when the team can explain what it believes, which variable or assumption the activity examines, what it expects to observe, and how different results would change the next decision.
Without those elements, a pilot may simply be a small implementation.
The scientific approach to entrepreneurship examined by Camuffo and colleagues is relevant here. In a randomized controlled trial involving 116 startups, entrepreneurs taught to formulate explicit theories and test hypotheses systematically made more precise decisions and were more willing to change direction when the evidence warranted it [4].
A later large-scale replication across 759 firms found that the approach increased idea termination and encouraged a selective pattern of strategic change rather than either refusing to change or pivoting constantly. The authors argue that a scientific approach can develop what they call methodic doubt: an awareness that alternative explanations may fit the situation better than the one currently favored [5].
That is close to what happens when an expert chef places several spice mixtures next to one another.
The chef is not admitting that everything is unknowable. They are making competing explanations observable.
A useful innovation experiment does the same.
Do not confuse one-variable experiments with simplistic thinking
There is an appealing simplicity in changing one factor while keeping everything else constant.
When possible, this helps isolate the effect of the variable and makes interpretation easier. If one sauce contains more acid than another and the remaining conditions are sufficiently similar, the difference in flavor becomes more informative.
Business systems, however, often contain interactions.
A price that works with one value proposition may fail with another. A sales channel that performs well for one customer group may be inappropriate for a second. A new onboarding process may create value only when the service model changes with it.
Experiment-design research therefore warns that varying one factor at a time can be inefficient and may fail to identify interactions. Designed or factorial experiments can examine several factors systematically and reveal how combinations behave, although they also require greater design and analytical discipline [6].
The lesson for innovation teams is not that every business experiment needs to become a statistically sophisticated factorial trial.
It is that the experiment should match the question.
When the team needs to understand the influence of one uncertain component, a simple comparison may be enough. When several components are likely to interact, the team may need a small matrix of deliberately chosen combinations rather than a sequence of unrelated changes.
“Try something and see what happens” is not yet an experimental strategy.
Twenty variations may still fail to reveal the missing ingredient
Experimentation is not magic, and Kitchen Impossible also provides a useful warning about its limits.
In another 2016 episode, Tim Raue sent Tim Mälzer to Hong Kong, where he had to prepare fish maw and sea cucumber. The critical element was the sauce. Mälzer knew that something important was missing and reportedly prepared about twenty versions while searching for the absent note.
None solved the problem.
He eventually stopped. The missing ingredient turned out to be Chinese ham, which was outside the set of possibilities he had been testing. (rtl.de)
This is as relevant to innovation as the successful spice experiment in Paris.
Repeated iteration does not guarantee learning when the hypothesis space is wrong. A team can produce twenty versions of the same value proposition while overlooking that it has selected the wrong customer. It can optimize a product interface while the real barrier sits in procurement. It can test more marketing messages when the underlying problem is not important enough to justify switching.
The quality of an experiment depends partly on the quality of the alternatives it considers.
The Hong Kong episode also shows the importance of stopping. After many variations, sensory fatigue reduced Mälzer’s ability to distinguish the results. Continuing to produce more versions was no longer increasing understanding.
Organizations face the same risk. When a sequence of experiments stops generating new information, persistence can become activity without learning.
A stopping rule is therefore part of experimental discipline.
The purpose of small failures is to prevent one large failure
Small experiments are sometimes dismissed because they appear less serious than building the complete solution.
A rough prototype can look unimpressive. A test with a small number of carefully chosen participants does not create the excitement of a large pilot. Comparing several unfinished alternatives can feel indecisive when leaders expect one clear recommendation.
Yet the modest appearance is part of the economic logic.
Sim Sitkin’s work on learning through failure argues that small, contained losses can support organizational learning and adaptation when they provide information without threatening the survival or identity of the organization [7].
The goal is not to celebrate failure in general. Large, ambiguous, repeated, or politically threatening failures often produce defensiveness rather than learning.
The goal is to design failures that remain small enough to examine.
A chef can afford for three tablespoons of sauce to taste wrong. It is more expensive to discover the same error after serving one hundred plates.
An innovation team can afford for a landing-page proposition, mock sales conversation, concierge service, or pricing test to fail. It becomes harder to respond honestly after the organization has built a platform, hired a team, announced a launch, and promised the results to senior management.
The experiment protects the organization from the scale of its own confidence.
Experiments do more than improve the idea
The immediate purpose of an experiment is usually described as testing an assumption or improving a decision.
There is another important result.
Experiments build expertise.
Each well-designed comparison helps the team understand how the system behaves. It learns which variables matter, which effects interact, where customers behave differently from what they say, and which signals deserve more weight.
The team becomes better not only at developing the current venture, but at recognizing uncertainty in the next one.
This is why black-box experiments should not be treated as isolated project activities delegated to a research specialist. They are a way of developing adaptive expertise across the venture team.
Salespeople learn to separate interest from commitment. Product teams learn to distinguish feature requests from underlying struggles. Sponsors learn to ask what an activity will make more knowable before approving it. Founders learn to remain confident in their capacity to navigate uncertainty without becoming certain that the first recipe is correct.
The experiment improves the dish.
More importantly, it improves the cook.
Innovation is not the expensive dinner
The visible output of innovation is often the complete solution: the product, service, platform, pilot, or business model.
The early work should produce something else first.
It should produce a better understanding of the ingredients, their interactions, the preparation method, and the conditions under which the result becomes valuable.
That is why the natural posture of an innovation team is not to demonstrate how confidently it can execute its first interpretation.
It is to find the smallest useful way to discover where that interpretation may be wrong before inviting everyone to dinner.
Kitchen Impossible is compelling because accomplished chefs are willing to be seen not knowing. They open the black box, create a theory, test it, taste the result, and revise what they believe.
Their expertise does not eliminate uncertainty.
It allows them to work intelligently inside it.
Innovation should do the same.
Before you approve the next pilot
The diagnosis is straightforward: large bundled experiments make it difficult to know what caused the result.
The harder question is what to do with a real initiative asking for another USD 180,000, another four months, or another pilot.
For paid subscribers, the Black Box Experiment Board below turns that decision into a sequence of assumptions, experiments, evidence thresholds and commitment decisions.
