Innovation Is Not Chess. It Is Poker.
Why good outcomes can hide weak decisions—and bad outcomes can hide sound judgement.
I read Annie Duke’s Thinking in Bets a couple of months ago (I recommend reading it—and no, I have no affiliate incentive for doing so). It sat in the back of my mind the way good books do — not loudly, but present. Then something I saw this week brought it back, and I could not stop thinking about one image in particular.
Annie argues that Chess is played with complete information. Both players can see the same board, the pieces follow stable rules, and no card is dealt by chance. The position may be extraordinarily difficult to evaluate, but nothing relevant is deliberately hidden.
Poker is different. Players act without seeing every card. They infer what others may hold, update their beliefs as new information appears and make commitments without knowing whether the next card will help or hurt them. A strong decision can lose. A weak decision can win.
Most consequential innovation decisions are much closer to poker.
Leaders do not know how customers will behave, how competitors will respond, whether a partner will deliver or whether an emerging technology will mature at the right time. They make commitments using incomplete evidence and assumptions about a future that has not happened yet.
Yet organizations routinely evaluate those decisions as though innovation were chess: the outcome arrives, and management works backwards to decide whether the original move was good.
Psychologists Jonathan Baron and John Hershey demonstrated this tendency experimentally. Participants rated otherwise identical decisions more favorably when the reported outcome was positive and more negatively when it was adverse. They called this outcome bias.[1] A preregistered replication published decades later found the same basic effect: knowledge of the result continued to distort evaluations of the decision that preceded it.[2]
Duke calls the everyday version of this “resulting.”
The moment I encountered the term, I recognized much of what passes for innovation governance.
The organization sees the outcome, not the decision
When an innovation succeeds, the organization searches for the people, methods and decisions that supposedly caused the success. The team becomes a model. Its process is presented as a playbook. The executive sponsor is praised for conviction.
When an innovation disappoints, management performs the same exercise in reverse. It searches for poor execution, insufficient ownership, slow delivery or the person who should have recognized the problem earlier.
Both reactions are understandable. Both can be wrong.
A project may succeed because the market moved in its favor, a competitor withdrew, regulation changed, a sales relationship opened the right door or a customer need suddenly became urgent. The team may have made a series of weak decisions and still received a favorable outcome.
Another team may frame the problem carefully, test its most consequential assumptions and stop after discovering that customer behavior does not support the business case. The visible outcome is that nothing launched. The decision process may nevertheless have saved the organization from a much larger mistake.
The first project is celebrated. The second is often treated as a disappointment.
This creates a dangerous learning system. The organization rewards luck when it resembles success and punishes disciplined judgement when it produces an unattractive outcome.
It then repeats the wrong lessons.
Innovation is usually governed as deterministic execution
Most corporate innovation processes look reassuringly ordered.
There is a funnel, a series of stages, a business case, a roadmap, a steering committee and a launch plan. At each review, leaders ask whether the work is progressing and whether the next stage should be approved.
This architecture is not inherently wrong. It works reasonably well when the problem is known, the solution has been established and the central challenge is execution.
A production facility, regulatory implementation or proven product rollout still contains uncertainty, but the task is largely to deliver something the organization already understands.
Innovation begins from a different position. The problem may be misdiagnosed, the solution may not change behavior, the customer may not pay and the organization may not be able to deliver the model economically.
Under those conditions, a roadmap does not describe what will happen. It records what the team currently hopes will happen.
The mistake is not planning. The mistake is governing an uncertain opportunity as though completing the plan will resolve whether the opportunity was worth pursuing.
Projects then become very good at producing evidence of activity:
interviews completed;
prototypes delivered;
pilots launched;
milestones reached;
partners contacted;
features built.
None of those achievements necessarily answers the investment question.
The useful governance question is not only:
Did the team complete what it promised?
It is also:
What did the organization believe when it made the last commitment, what evidence has appeared since, and should that commitment now change?
Research on entrepreneurial decision-making shows why this matters. In a randomized controlled trial, entrepreneurs trained to articulate theories, derive testable hypotheses and evaluate evidence systematically made more precise decisions and were more likely to pivot away from weak ideas.[3] The method did not remove uncertainty. It changed how they responded to it.
A result contains more than the decision
An innovation outcome is rarely produced by one factor.
It reflects the interaction of:
the original problem diagnosis;
the evidence available when commitments were made;
execution quality;
the team’s ability to adapt;
organizational support;
customer and competitor behavior;
timing;
events nobody controlled.
This is why looking backwards from an outcome is so seductive. Once the result is known, the story becomes easier to construct. The winning move appears obvious. The ignored warning looks decisive. The failed assumption seems as though it should always have been visible.
Outcome knowledge changes how people remember and interpret the information that was available before the result. This is related to hindsight bias: after learning what occurred, people tend to see the event as more predictable than it appeared in advance.[4]
In organizations, this produces clean narratives after messy decisions.
A failed initiative is reconstructed as a sequence of warning signs, even if those signals were ambiguous at the time. A successful initiative is presented as the inevitable consequence of strategic clarity, while the near misses, fortunate timing and external help disappear from the account.
The story becomes useful for reputation management and dangerous for learning.
If leaders want better decisions, they must evaluate choices using the information available when the choice was made, not only the information revealed by the eventual outcome.
Most “knowns” are candidate beliefs
The poker analogy becomes uncomfortable when leaders examine what they actually knew before approving an innovation.
Organizations frequently say they know the customer when they know a segment description. They say they know the problem when they have a collection of internal interpretations, sales anecdotes and market reports. They say they know the economics when they have a spreadsheet whose most important cells contain assumptions about adoption, conversion and future scale.
These inputs are not worthless. They may be the best available starting point.
They are not all knowledge.
In uncertain work, a known should have survived meaningful contact with reality. A customer changed behavior. A buyer accepted a trade-off. A channel converted under plausible conditions. A technical constraint was tested where it actually matters. A partner committed its own resources.
Everything else remains a candidate belief with a level of confidence attached to it.
The distinction is important because commitment tends to turn candidate beliefs into organizational facts. Once a business case has been approved, the assumptions that created it are rarely revisited with the same energy used to defend the project.
Research on theory-driven entrepreneurial decision-making suggests that making the causal logic of an opportunity explicit can improve how entrepreneurs use evidence. A 2025 randomized trial found that theory-of-value training changed how entrepreneurs formed and evaluated their strategies, although the effects depended on the context and should not be interpreted as a universal recipe.[5]
The practical lesson is narrower: leaders cannot evaluate decision quality if the beliefs beneath the decision were never made visible.
Unknowns are not one category
Innovation teams often group uncertainty under a single heading called “risk.”
That hides important differences.
Some uncertainties are known unknowns. The team can name them: Will customers pay? Will users change their workflow? Can the solution be delivered at viable cost? Will procurement accept the model?
These uncertainties are useful because they can guide experiments.
Others are unknown unknowns. They remain outside the frame until the venture encounters a new customer, channel, regulation or operational condition. No workshop can identify all of them in advance.
A third category is more politically difficult: ignored knowns.
These are not missing facts. They are signals the organization has already encountered but does not want to include in the decision:
customers are polite but not urgent;
use declines after the novelty disappears;
sales cannot explain the value without the product team;
the economics depend on a scale the organization cannot credibly reach;
the initiative requires behavioral or organizational change that nobody has authority to impose.
Ignored knowns are often reframed as execution issues because the alternative would be to reconsider the opportunity itself.
A board cannot eliminate unknown unknowns. It can, however, create conditions in which known unknowns are tested and ignored knowns are allowed into the room.
Good innovation teams do not avoid being wrong
Two teams can face similar uncertainty and behave very differently.
One turns its most consequential beliefs into explicit hypotheses. It seeks evidence while commitments are still small, defines what would reduce confidence and changes direction when the evidence no longer supports the original idea.
The other converts assumptions into a roadmap, secures resources and discovers the problem only after the initiative has acquired budget, people and executive identity.
Both teams were uncertain.
Only one designed the work to become less wrong before being wrong became expensive.
Camuffo and colleagues found that a scientific approach reduced the likelihood of continuing with false-positive opportunities while helping entrepreneurs avoid discarding potentially valuable ideas too early.[3] Later research has shown that such approaches can also produce more substantial strategic redirection by making alternative explanations and customer groups easier to consider.[6]
This is a more useful definition of innovation progress than activity or delivery speed.
The relevant measure is not how quickly the project moves through the process.
It is how effectively the process improves the next decision.
Learning from failure is not automatic
Innovation rhetoric often assumes that failure creates learning.
It does not.
A failed project may produce defensiveness, blame, a simplified story or a list of operational corrections that protect the original assumptions. A successful project may produce even less reflection because nobody feels an urgent need to question what happened.
Research in pharmaceutical R&D has found that organizations can learn from small failures, but the effect depends on the conditions under which those failures occur and are interpreted.[7] Failure provides information; it does not guarantee that the organization will use it well.
Experiments create the same problem. Their results are often noisy, multidimensional and open to interpretation. Emerging research suggests that decision-makers may respond more strongly to positive dimensions of ambiguous feedback, especially when the evidence contains conflicting signals.[8]
This means that running more experiments is not enough.
The organization must decide in advance:
what it currently believes;
what result would support that belief;
what result would weaken it;
what action should follow each outcome.
Without those commitments, experiments can become another source of retrospective storytelling.
The governance error
Most innovation governance is designed to approve work, not improve judgement.
Committees request plans, forecasts, milestones and expected returns because these make an initiative easier to compare with conventional investments. Teams learn that approval depends on confidence, so they present uncertainty as something already contained inside the plan.
Assumptions become forecast ranges. Experiments become pilots. Early possibilities become strategic priorities.
The governance system then evaluates the outcome without preserving a reliable record of what was known when the decision was made.
This is the organizational equivalent of seeing the final cards and pretending they were visible all along.
A better system would still care about outcomes. Outcomes matter because organizations cannot survive on elegant reasoning that never creates value.
It would simply refuse to use the outcome as the only verdict on the decision.
The free diagnosis is therefore complete:
Innovation leaders must judge two things separately: the quality of the outcome and the quality of the decision process that preceded it.
A good outcome can come from a weak decision. A bad outcome can follow a strong one.
Until governance can hold both truths at once, the organization will continue rewarding luck, punishing useful learning and scaling the wrong lessons.
