TL;DR: Teams often delay customer research until a concept, prototype, budget, and internal narrative already exist. At that point, research may no longer test whether an opportunity deserves investment; it can become a search for evidence that justifies prior commitments. The problem is not the research method but the sunk costs, identities, and organizational politics surrounding how findings are interpreted. Early research does not need to prove that a market exists. Its value is to expose assumptions, reveal how customers actually experience and address a problem, and create an evidence-based decision gate before further capital, time, and credibility are committed. The sponsor’s task is therefore to determine whether the next investment will reduce consequential uncertainty—or merely protect what the organization has already spent.
A recent exchange about user research stayed with me.
The original conversation was about a tool designed to reduce the operational friction of research. Recruiting appropriate participants takes time, arranging interviews can become a calendar exercise, conducting the conversations requires preparation, and making sense of what people said is rarely as simple as summarizing a transcript. Anyone who has tried to organize customer research inside a busy organization will recognize that friction.
The tool was intended to make all of this easier, and that is useful. Yet the part of the conversation that stayed with me was not the tool. It was the timing.
Teams do not postpone customer conversations simply because research is difficult to organize. Sometimes that is the genuine reason. At other times, operational friction provides a convenient explanation for a deeper problem: the project has already progressed too far, and worse, resources have already been invested in developing a solution for what I would call merely a “possible innovation candidate.”
By the time research is proposed, there is often more than an idea. A concept has been developed, a prototype exists, and a roadmap is beginning to take shape. Several internal supporters have attached their names to the initiative. Someone has written a business case, secured initial funding, or presented the opportunity to senior management. The team has invested time, the sponsor has invested credibility, and the organization has gradually started treating the project as something that exists rather than something that might deserve to exist.
Then someone asks for user research.
The activities may still look the same. Participants are recruited, interviews are conducted, observations are analyzed, and findings are presented. However, the job that research is being asked to perform has changed.
Early research asks what the organisation needs to learn before it commits. Late research is more likely to ask whether enough evidence can be found to justify what the organization has already started.
That is the moment research risks moving away from discovery and towards approval-seeking. It becomes solutionism with a research layer added afterwards.
The same interview can serve a different purpose
The difference between early and late research is not primarily methodological. The same interview guide, recruitment process, and analysis method could be used at either stage. What changes is the organizational environment in which the evidence will be interpreted.
Early in an initiative, negative evidence threatens an idea. Later, the same evidence may threaten a prototype, a roadmap, a team’s objectives, a sponsor’s judgement, and a narrative that has already travelled through the organization.
This matters because people do not assess new information independently of what they have already invested. Barry Staw’s foundational work on escalation of commitment showed that decision-makers can continue committing resources to an unsuccessful course of action, particularly when they feel personally responsible for the original decision [1]. Arkes and Blumer later described the sunk-cost effect as the tendency to continue an endeavor because money, time, or effort has already been invested, even though those past investments should not determine whether further investment is justified [2].
Late research enters precisely this environment. The team is no longer learning only about customers, markets, and business models. It is also negotiating with everything that has already been spent and promised.
This does not mean that the team deliberately manipulates the research. Most people involved will be sincere. They want to learn, they care about the project, and they may genuinely believe that further development will reveal its potential. Yet sincerity does not remove bias. A team can ask an honest question while unconsciously preferring the answer that allows it to continue.
The expense of late research therefore does not lie mainly in recruiting participants or conducting interviews. It lies in the amount of accumulated commitment that the findings must overcome.
Innovation work is learning before commitment
I have encountered this pattern several times in my professional work.
I have been invited into innovation and venture projects that appeared advanced from the outside. The teams had prototypes, presentations, roadmaps, market estimates, and a coherent story. Considerable work had clearly taken place, and the quality of the artifacts often created the impression that the initiative itself was equally mature.
Then we began examining what the project was built on.
Which assumption was the prototype intended to test? What were potential customers doing instead? What would make them abandon an established workaround? Who experienced the struggle, who controlled the budget, and who would carry the operational and political cost of switching? Which observations supported the current direction, and what finding would cause the team to reconsider it?
That is usually when the character of the conversation changes.
The problem is rarely that the team is weak or careless. Most teams are responding rationally to the system around them. They have been rewarded for building something, aligning stakeholders, making progress visible, refining the narrative, and defending the next stage of funding.
All of those activities can be useful. None of them proves that the initiative deserves to exist or continue.
They demonstrate that the team has been active. They do not demonstrate that consequential uncertainty of feasibility has been reduced.
This distinction separates innovation work from delivery work. When the customer problem is sufficiently understood, the relevant market mechanisms are known, the business model has been established, and the primary challenge is reliable execution, the organization is doing delivery. The work may still be difficult and uncertain in many practical ways, but the underlying direction is no longer the main question.
Innovation begins where material uncertainty remains.
A good innovator is therefore not someone who already knows the answer. A good innovator knows how to learn before the organization makes being wrong unnecessarily expensive.
That does not mean arriving without expertise. People responsible for innovation should understand customer research, business model logic, assumptions, experiments, evidence, and decision gates. They should know how to distinguish an interesting signal from a convenient anecdote. What they should not do is assume that their experience allows them to know the specific opportunity before reality has had a chance to challenge it.
Research by Camuffo, Cordova, Gambardella, and Spina supports this view. In a randomized controlled trial involving 116 Italian startups, entrepreneurs who were taught to formulate explicit hypotheses and test them systematically performed better and were more likely to pivot than those following more conventional approaches to market feedback [3]. A later replication across 759 firms and four randomized controlled trials found that the scientific approach increased idea termination and was associated with a more selective pattern of strategic change, rather than either refusing to change or pivoting repeatedly [4].
These studies should not be interpreted as proof that every experiment produces a better venture or that uncertainty can be eliminated. Their more relevant contribution is that explicit hypotheses and disciplined tests can help entrepreneurs recognize when an idea should be changed or abandoned.
The central innovation skill is not being right immediately. It is learning fast and early enough that being wrong remains affordable.
What an advanced project may still be hiding
Organizations often mistake the visible development of a project for the reduction of uncertainty behind it.
A polished concept can feel like evidence because it makes the opportunity easier to imagine. Internal alignment can feel like market validation because several influential people now support the idea. A prototype can create the impression that the team has moved closer to a viable business, even when the prototype has not tested the assumptions most likely to make that business fail.
Internal alignment can keep a weak project alive for a long time. It can protect the team, unlock further budget, and make the initiative appear increasingly official. What it cannot do is create demand outside the organization.
By the time research begins, the project may no longer be treated as a hypothesis. It has become someone’s work, someone’s quarterly objective, someone’s internal promise, and sometimes someone’s route to recognition.
That changes the politics of learning.
Early research asks whether the organization should commit. Late research asks whether the organization can still afford to be honest.
This is why positive feedback collected after a prototype already exists must be interpreted carefully. Such feedback is not worthless. It can reveal confusing language, missing functionality, unexpected objections, relevant use cases, and aspects of the customer context that the team has overlooked.
It is nevertheless easy to ask positive feedback to carry more weight than it deserves.
People are often polite when they are shown a new idea. Some enjoy participating in innovation discussions and want to be helpful. Internal sponsors may naturally remember the statements that support the direction they already favour. Teams may interpret general interest as evidence of future adoption.
None of this is equivalent to a customer changing behavior, reallocating a budget, replacing an existing workaround, accepting the cost of switching, or taking a meaningful risk.
The later the research occurs, the easier it becomes to collect evidence that improves and protects the existing story instead of testing whether the story is true.
Early research does not need to prove the market
The argument against early user research is frequently framed in terms of cost and speed. Research is described as expensive, slow, or too academic for the pace at which innovation teams are expected to move.
Thorough research can certainly be demanding. Some questions require broader samples, careful recruitment, specialist expertise, quantitative analysis, or observation over an extended period. A few interviews cannot establish market size, predict adoption rates, prove willingness to pay, or demonstrate that a business will become commercially viable.
However, early research does not need to begin as a definitive study. Its first job is usually to reveal whether the team is building on assumptions that nobody has examined.
In many contexts, a focused team can conduct an initial set of exploratory conversations within a week. Participants may come from existing customers, prospects, professional networks, industry communities, or groups already using an alternative solution. Research platforms may reduce the administrative burden, but the value does not come from speed alone. It comes from asking a clear question of the right people before the answer becomes politically inconvenient.
Eight or ten interviews do not prove that a market exists. They can still reveal that the original problem is poorly framed, that customers experience it differently from the way the team imagined, or that the proposed solution conflicts with how purchasing and switching decisions actually occur.
Research on qualitative saturation is relevant here, although it is often applied too broadly. Guest, Bunce, and Johnson analyzed sixty interviews from a particular study involving a relatively homogeneous population. They found that basic elements of the main themes appeared within the first six interviews and that saturation occurred within the first twelve in that dataset [5]. Their findings do not establish a universal minimum or maximum number of interviews. Sample requirements depend on the question, the diversity of participants, the purpose of the research, and the quality of the analysis. The narrower conclusion is that focused qualitative research can reveal meaningful patterns relatively quickly under appropriate conditions.
That is the role of an early research cycle. It does not purchase certainty. It purchases a better decision about what should happen next.
Assume that two people spend five days preparing, conducting, and synthesizing an initial research cycle, at an internal or equivalent external cost of $1,000 per person per day. The cost would be approximately $10,000.
That amount does not buy proof, but it may buy an important decision point. It can show whether the problem is sufficiently important to justify further investigation, reveal what customers do today instead, and expose whether the struggle creates meaningful cost, delay, risk, frustration, or political difficulty. It can also reveal whether switching would be easy, expensive, unrealistic, or dependent on people who have not yet been involved.
Most importantly, it requires the team to make its assumptions visible.
The team must explain what it currently believes, which conditions need to be true for the initiative to deserve further investment, what would count as a meaningful signal, and what evidence would cause it to change direction or stop.
These questions appear straightforward, but they remove the places in which weak projects hide. Without explicit assumptions, teams can debate features, preferences, opinions, and optimistic projections for months. Once the assumptions are stated clearly, the conversation becomes more demanding because the team has to explain what it believes about reality before reality is asked to respond.
Research is a gate before it becomes a report
The value of early research does not lie primarily in the final report, the workshop output, or the customer quotations selected for a presentation.
Its value lies in the decision it makes possible.
The team may continue because the initial evidence supports further investigation. It may deepen the research because the problem appears material but remains insufficiently understood. It may change direction because a critical assumption has weakened, or it may stop because the opportunity no longer justifies additional investment.
This is a different way of understanding research. It is not an activity attached to a project once the project is already underway. It is a gate placed before the project becomes unnecessarily expensive.
Stage-Gate thinking is useful here, provided that it is not reduced to governance theatre. Its relevant contribution is the discipline of making an explicit decision before additional resources are committed. More recent descriptions of gating systems have also emphasized that these mechanisms need to become faster, more adaptive, and more compatible with iterative development rather than functioning as rigid sequential approval processes [6].
The gate I mean is therefore small, fast, and close to the evidence. Its purpose is not to demonstrate compliance with a process. It is to make it harder for a team or sponsor to hide behind the quality of a presentation.
The relevant cost comparison is not $10,000 for research against zero dollars for doing nothing. It is $10,000 bet now against months in which a multidisciplinary team continues moving in the wrong direction.
A team that keeps designing, building, coordinating, presenting, and governing a weak initiative consumes resources even when no separate research budget is visible. Depending on the organization, the cost of another months may include salaries, technology, prototypes, external partners, management attention, and the opportunity cost of not pursuing something stronger.
Early research appears expensive because it has a visible price. Continuing without learning often appears inexpensive because the cost is distributed across normal project activity.
That accounting illusion makes research look costly and weak projects look cheap.
The cost of not knowing compounds
A late diagnosis is rarely expensive only because the diagnosis itself costs more. It is expensive because the underlying problem has had time to grow.
The same pattern appears in product and innovation work. The later a weak assumption is examined, the more budget, identity, politics, and internal logic become attached to it. Stopping no longer feels like a useful learning outcome. It feels like failure.
Teams therefore continue improving the prototype, sharpening the narrative, and searching for favorable reactions. They find groups that respond positively and develop explanations for those that do not. They call the activity validation, although it may gradually become a negotiation with sunk cost.
A team can be sincere while protecting a weak project. A sponsor can request evidence while rewarding confirmation. An organization can celebrate learning while punishing the first person who recommends stopping.
For that reason, early user research is not only a research topic. It is a capital-allocation topic.
The relevant question is not whether the team has produced something. It is whether the next unit of capital, attention, time, and credibility deserves to be committed.
The sponsor’s real decision
Sponsors therefore have a different job from the one they are often asked to perform.
Their task is not merely to assess whether the team has built something impressive or presented a compelling opportunity. It is to decide whether the next investment will reduce consequential uncertainty or protect costs that have already been incurred.
That question changes the funding conversation.
Before approving a prototype budget, a pilot, a detailed roadmap, or another quarter of development, the sponsor should ask for the assumptions rather than the ambition. The market-size slide and polished concept can wait until the team has explained what must be true.
Which claims are supported by direct observation, and which remain interpretations? What do customers currently do instead? What would make them switch? Which stakeholder experiences the problem, which one controls the budget, and which one can prevent adoption? What finding would cause the team to stop?
An organization is allowed to place a strategic bet even when evidence is limited. Some decisions cannot be tested fully in advance, and uncertainty can never be removed entirely. The organization should nevertheless describe the decision honestly.
When critical assumptions remain unexamined, another round of development is not evidence-based progress.
It is a bet hidden inside delivery language.
Once a project has acquired a team, budget, roadmap, and story, it becomes difficult to stop for reasons that have little to do with customer demand. Escalation of commitment appears in meetings through phrases such as “one more iteration,” “we need better messaging,” “customers do not understand it yet,” or “we have already invested too much to stop.”
Any of those statements may be correct. The problem is that they are often accepted before being tested.
When everyone holds a hammer
There is also a team-design problem beneath all of this.
Many innovation teams are built predominantly around people who are good at creating solutions: engineers, designers, product managers, domain specialists, and business builders. These capabilities are valuable and eventually indispensable.
However, when a team is strong only at shaping solutions, it may remain weak at determining whether the underlying problem deserves a solution in the first place.
That is not a minor imbalance. It allows a team to move quickly in the wrong direction while looking highly productive from the outside.
Not every initiative needs a formal research department before work can begin. Yet someone involved must possess the competence and independence required to investigate the problem, question the framing, distinguish claims from evidence, and recognize when a plausible opportunity is not strong enough.
When those capabilities are missing, the organization should develop them or introduce them before its commitment grows. Otherwise, what appears to be speed may simply be the extension of a weak project’s life.
Early research is not expensive because it delays progress. It is valuable because it reveals whether what the organization calls progress deserves to continue.
Keeping uncertain projects alive without learning is expensive.
And when everyone is holding a hammer, the customer’s struggle has little chance of being understood as anything other than a nail.
