“I’ve seen this before.”
That can be reassuring when you’re deciding where to take a business next. Someone recognises the situation. They know what worked elsewhere and have a view on what to do.
When performance is disappointing, a familiar explanation offers a way forward. When results are strong, it can make the next investment feel like an obvious extension of what already works.
I value experience. But I think we give the phrase “pattern recognition” a rather easy ride.
What have we recognised? The cause of a result? A few familiar features? Or an opportunity to use an approach we already favour?
If your experience is part of why you were hired, questioning your first interpretation can feel uncomfortable. People expect you to know what to do. “I’ve seen this before” carries authority; “I need to understand what’s different here” can feel harder to say, particularly when everyone wants a decision.
Yet experience can shape the investigation itself, directing our attention towards supporting clues before we realise how much of the answer we’ve already accepted.
And when the numbers look good, who wants to be the person questioning the growth plan?
When success makes the next move look obvious
Imagine a B2B software business doing well in its established market. Customers are getting value, retention is healthy and the company has developed a repeatable way to win business.
An adjacent segment looks attractive. Early campaigns draw interest, prospective customers recognise the problem, and the first contracts are signed. A senior leader has entered a similar market before and recommends increasing investment.
There’s a credible case. Product sees a use case it already supports. Marketing sees a responsive audience. Finance sees the possibility of higher contract values.
You present the plan to the board. Marketing commits spend and Product moves other work aside.
Then Customer Success brings some awkward evidence.
The new customers need more implementation work than the plan allowed, more ongoing support and changes to the product. Some lack the internal resources the offer assumes they have.
The specialist helping a new account is unavailable to an existing customer. Product postpones an improvement promised elsewhere. Customer Success spends more time managing expectations, while the new-segment report still leads with bookings.
Some extra effort may be sensible launch investment. Better documentation, product improvements and experience could reduce it. But if these customers consistently need more assistance to obtain value, that is part of the economics of serving them.
The business has established that it can sell the offer. It is still learning whether it can deliver the promised value repeatedly, at a cost the price can support.
You’ve asked people to back this expansion. The board has approved the investment, teams have changed their priorities and the first wins have been celebrated. Now you may need to explain why the plan requires another look.
It is tempting to give the familiar explanation more time. Perhaps the team just needs to get through the early difficulties.
Perhaps it does. But that needs testing before the next commitment makes changing course harder.
A long career still gives us a particular view
Twenty years can teach you a great deal. It cannot expose you to every combination of customer needs, product capability, market conditions and commercial economics.
Someone may have reviewed thousands of customer decisions, but many happened within the same business model, using similar information and under similar incentives. That repetition can build considerable expertise. How far it transfers is another matter.
Consultants, investors and fractional leaders may see many businesses without following every recommendation through to its consequences. An operator may understand fewer businesses much more deeply. Neither perspective is complete.
Then there is the quality of the feedback.
A campaign attracted customers, but did we see what they cost to support eighteen months later? The business grew after we changed its pricing, but what else changed? We left after a successful market entry. Did the economics hold?
Two psychologists who approached expertise from different directions offer a useful way to think about this. Daniel Kahneman studied how human judgement goes wrong; Gary Klein studied how experienced professionals make effective decisions under pressure.
They agreed that reliable intuition depends on having something consistent to learn and enough useful feedback to learn it. Repetition alone isn’t sufficient. If consequences arrive much later, or we never discover why an outcome occurred, years of experience can leave important assumptions untested. [1]
In GTM, feedback can arrive in another department, or after several changes have made the original decision difficult to evaluate. We can come away believing an approach worked because we saw the revenue arrive, but never saw the full cost of delivering it.
The familiar answer can direct the search
In experiments with expert chess players, Merim Bilalić and colleagues examined what happened when a familiar solution was available alongside a better one.
Players said they were searching for a better solution. Their eye movements showed that attention remained focused on features associated with the familiar answer. [2]
This is called the Einstellung effect: an established approach can obstruct a better one. The experiments don’t establish how often executives misdiagnose a business situation. They do show why sincerely looking for alternatives may not be enough.
Our software business could commission extensive research into market size, competitor pricing and acquisition opportunities while barely examining whether these customers can obtain value at a cost that makes serving them worthwhile.
We also learn from a selective set of examples. Jerker Denrell’s research shows how under-observing failures can produce misleading beliefs about effective management. Practices that look attractive among surviving businesses may look different when unsuccessful businesses are included. [3]
Which attempts failed? What made the successes possible? How much of that survives in the account we remember?
I wouldn’t ask leaders to set their experience aside. I would ask them to give the present business a fair chance to contradict it.
The trap is allowing a familiar explanation to decide what we investigate, then treating the evidence we find as confirmation. Here are five ways to test whether past experience fits the GTM decision in front of you.
1. Write down what happened before explaining why
Start with a description someone else can check.
“Onboarding is inefficient” has already named the problem.
“Customers in the new segment take longer to reach first value and need more implementation support” gives us something to investigate.
Establish three things:
Who: which customers or part of the business?
What: what changed, and compared with what?
When: over which period?
Ask the relevant teams for observations before circulating your preferred explanation. Otherwise, your question may decide what evidence they bring.
In our example, compare customer readiness, implementation effort and progress towards first value. Include stalled customers as well as those who completed the process.
A short shared account may reveal that people are discussing different customers, periods or definitions of success. Find that out before commissioning the fix.
2. Ask what made it work last time
“It worked at my previous company” is useful context. To act on it here, we need to understand what made it effective there.
Make the comparison explicit:
What is genuinely similar?
What is different enough to affect the result?
What haven’t we checked?
Two customer groups can look similar on a market-sizing slide and require very different businesses to serve them.
Perhaps the previous market had the same operational problem, but customers employed specialists who could implement the product. The new customers expect the software, and your team, to fill that gap.
Strategy researchers Giovanni Gavetti, Daniel Levinthal and Jan Rivkin examined how managers use experience from one setting to make decisions in another. Their simulation suggested that an analogy is useful when managers recognise which similarities affect the outcome. Applying the old approach too rigidly can prevent them from finding a better fit for the new situation. [4]
In our example, customer size and industry may be less important to the economics than whether customers have the people and skills to implement the product. That is the comparison the investment decision needs.
Give the experienced person room to explain the reasoning behind their judgement. Colleagues can then test the comparison without having to challenge someone’s entire career.
3. Give your preferred explanation a credible rival
“Keep an open mind” is easy to agree with. Challenging the explanation is harder when it comes from your CEO or the investor backing the plan.
Make examining alternatives part of the decision process, whoever proposes the idea.
Suppose leadership believes the new segment needs better onboarding. Write down another plausible explanation, then identify evidence that would help distinguish between them.
If the issue is… | Look for… |
|---|---|
The onboarding process | Where otherwise ready customers stall, and whether a changed process helps them progress. |
Customer readiness | Missing skills, resources or ownership that prevent progress even with competent onboarding. |
Different groups within the segment | Consistent differences in value achieved and service effort between identifiable customer groups. |
Several causes may coexist. The purpose is to stop collecting evidence that only confirms your first answer.
Ask someone who sees the consequences from another position what your explanation fails to account for. Make clear that you want help establishing the conditions under which the investment will work.
Someone raising a delivery problem should not have to choose between supporting growth and reporting what they see.
4. Follow the same customers through the system
Test the whole commercial proposition:
Can we win them? Examine what attracts these customers, what they buy and the cost of acquiring them.
Can we deliver enough value for them to stay? Follow implementation, adoption, ongoing support and the outcomes customers actually receive.
Can we do that repeatedly at economics worth expanding? Include service effort, product work and capacity diverted from other customers. Distinguish launch costs from work each new account will require.
Marketing may be right about strong demand while delivery is right about the cost of serving it. Both belong in the investment decision.
The findings could strengthen the case. Higher support costs may be worthwhile if customers stay longer and expand. Or one part of the segment may perform well while another absorbs the margin.
Use the same customers and clear time periods. Where retention outcomes haven’t had time to emerge, say so.
You don’t need elaborate costing to start noticing hidden effort. Record the extra management attention, the specialist repeatedly pulled into implementations and the work postponed elsewhere.
This is what treating GTM as a system means in practice. Early wins tell us something useful. Customer value and the economics of delivering it determine whether there is something worth scaling.
5. Decide what to test, who owns it and when to review
You still have to decide. The team needs direction, the budget cannot remain provisional and waiting has a cost too.
For the new segment, the next step could be a limited test of revised customer-readiness requirements and onboarding support.
Agree:
The commitment: what changes, for whom, and how much resource it can use.
The owner: who makes the decision and who contributes.
The evidence: what would justify continuing, adapting or stopping.
The review point: when enough should be known to decide the next step.
The executive leading the expansion owns the commercial decision. Product and delivery establish what customers need to achieve value; Marketing tests whether those customers can be reached; Finance assesses the economics.
Check whether delivery effort falls as the approach improves and whether existing customers continue receiving the service they need. New-account success achieved by neglecting the established base is an incomplete result.
Keep the test interpretable. Changing the audience, price, product and support model together may leave you unable to explain the outcome. Early progress can justify another limited step, but it cannot prove a retention outcome that hasn’t happened yet.
Focusing on evidence that could change the next decision helps improve decision velocity. Leadership can reach a better-supported judgement sooner, without waiting for every uncertainty to disappear.
At the review, make the choice explicit: proceed, change the approach or stop.
AI makes the starting question more consequential
“AI is useful, but it doesn’t have my pattern recognition” deserves the same scrutiny as any other claim about expertise.
Which patterns? Learned under what conditions? With what evidence that they apply here?
Equally, asking AI to build a market-entry plan around a successful playbook does not test whether the playbook transfers. We have already framed the task around using it.
For our software business, a more useful starting point might be to ask AI to compare the two customer groups, identify assumptions in the expansion case and suggest evidence that could challenge them. Its output still needs checking against the business.
We shouldn’t assume the combination automatically produces better judgement. A 2024 review of 106 experiments found that human–AI combinations outperformed humans alone on average, but fell short of whichever performed better alone, the human or the AI. The studies covered 2020 to mid-2023, so they are not a benchmark for current models. [5]
The practical test is whether AI helps uncover something consequential, evaluate evidence or reach a sound decision sooner. If it helps us produce a more persuasive version of what we already believed, we should recognise that too.
Put Acuity to work on the decision in front of you
When you’re deciding whether to back a growth plan, change it or call a halt, a confident opinion only takes you so far. You need to understand what the business can support, which assumptions remain untested and what deserves attention before more is committed.
I built Acuity to help leadership teams see that connected GTM system.
Across 21 capabilities, Acuity examines reported operating practices, differences in participants’ experiences and where confidence runs ahead of or behind those practices. Its priorities and guidance help leaders identify what evidence to inspect and what operating changes to consider.
Acuity is not an independent audit and does not prove what caused a growth problem. It gives leadership a structured basis for examining the business beyond the explanation that first felt familiar.
If you are entering a market, building on strong performance or trying to recover growth, book a demo. See how Acuity can help you understand what your GTM system can support and where to focus next.
Sources
1. Kahneman, D., and Klein, G. (2009). Conditions for Intuitive Expertise: A Failure to Disagree. American Psychologist, 64(6), 515–526.
2. Bilalić, M., McLeod, P., and Gobet, F. (2008). Why good thoughts block better ones: The mechanism of the pernicious Einstellung (set) effect. Cognition, 108(3), 652–661.
3. Denrell, J. (2003). Vicarious Learning, Undersampling of Failure, and the Myths of Management. Organization Science, 14(3), 227–243.
4. Gavetti, G., Levinthal, D. A., and Rivkin, J. W. (2005). Strategy Making in Novel and Complex Worlds: The Power of Analogy. Strategic Management Journal, 26(8), 691–712.
5. Vaccaro, M., Almaatouq, A., and Malone, T. W. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8, 2293–2303.