GPT Makes Ideas Cheap. Wrong Innovation Bets Remain Expensive.
Why the best innovation prompt starts with what would have to be true.
“Give me ten ideas.”
It is probably one of the most natural ways to use GPT in innovation work. Add a market, a customer problem or a technology, ask for possible solutions and a few seconds later the screen fills with options.
Some are predictable. Others are surprisingly useful. Almost all of them are expressed clearly enough to create the impression that progress has been made.
I use GPT for this as well. There is nothing wrong with it. Generative AI is an excellent instrument for expanding a search space, combining familiar concepts and overcoming the uncomfortable emptiness at the beginning of a creative task.
The problem begins when the availability of ideas is confused with the reduction of uncertainty.
An innovation team does not become more likely to succeed simply because it can produce more plausible possibilities. It succeeds by discovering which possibilities deserve further commitment before the cost of being wrong becomes difficult to recover.
GPT has made the first part dramatically cheaper.
The second remains expensive.
The most obvious use is not necessarily the most valuable
The largest analysis so far of how consumers use ChatGPT examined 1.5 million conversations. It found that writing was the most common work-related use, while almost half of all messages involved asking the system for information, guidance or advice.[1]
This reflects the basic attraction of the technology. It can produce something immediately. A draft appears. A summary becomes available. A plan takes shape. An empty page is replaced by visible output.
In innovation work, the equivalent is ideation. A team can ask for customer problems, product concepts, business models, campaign ideas, experiment designs or alternative value propositions without organizing another workshop or waiting for inspiration.
Research supports the usefulness of this. Across five experiments, participants using ChatGPT produced ideas that evaluators rated as more creative than ideas generated without technological assistance or with conventional web search. The effect was particularly strong for ideas that were incrementally rather than radically new.[2]
Other studies have found a more complicated pattern. AI assistance can improve the average quality of individual ideas while making the collective pool of ideas more similar. In one experiment, generative AI improved individual creative output but reduced diversity across the resulting stories.[3] A later reanalysis of brainstorming experiments reached a similar conclusion: ChatGPT raised average creativity while reducing the variety of ideas available to the group.[4]
A large comparison published in 2026 examined more than 9,000 humans and over 215,000 observations from language models. Humans were only slightly more creative on average, but they showed greater variation and produced more of the exceptional ideas found at the top of the distribution.[5]
This does not make GPT a poor ideation partner. It makes it a particular kind of partner.
It is very good at rapidly producing coherent possibilities around a frame. It can improve an average response, combine established patterns and help a person search more broadly than they might alone. With careful human guidance, it can also produce solutions that compare favorably with human crowds in strategic viability and overall quality.[6]
What it does not automatically do is determine whether the frame is correct.
GPT answers the question inside the question
Suppose a team asks:
Give us ten ideas for an AI assistant that helps account managers prepare customer meetings.
The model can produce useful features: automated company research, stakeholder profiles, opportunity summaries, suggested questions, risk alerts, talking points and follow-up recommendations.
The result may be excellent.
It may also rest entirely on assumptions that nobody has examined:
Account managers are insufficiently prepared.
Poor preparation materially affects customer outcomes.
The problem is caused by a lack of information rather than a lack of time, motivation or commercial judgement.
Account managers will trust AI-generated preparation.
They will change their current workflow.
Customer and commercial data can be accessed legally and reliably.
Better preparation will improve conversion, retention or account growth.
The economic value will exceed the cost of integration, governance and maintenance.
GPT can generate the solution without testing any of these conditions.
That is not a defect. The model was asked to produce ideas, so it did.
The danger lies in how easily articulate output can make an untested premise feel more developed than it is. The idea now has features, a value proposition, perhaps even a name and a rollout plan. Each additional layer creates cognitive and organizational momentum.
The team begins discussing how the assistant should work before it has established whether the assistant should exist.
More plausible output can increase the cost of weak reasoning
Generative AI does not perform uniformly across every task. In a field experiment with consultants, AI improved speed and quality on tasks that fell within the model’s capabilities. On a task outside that frontier, people using AI were less likely to reach the correct answer.[7]
The important point was not merely that the model sometimes failed. Its capabilities were uneven in ways that users found difficult to recognize. A person could experience several impressive results, build trust in the system and then rely on it precisely where its performance became weaker.
The same problem appears in decision support. Experimental research has found that people can over-rely on AI advice even when it conflicts with contextual information and works against their own interests.[8]
Innovation creates particularly favorable conditions for this error because the correct answer is rarely available in advance as in any scientific field and approach. There is no answer key for a new market, an untested business model or a customer behavior that does not yet exist.
A confident recommendation can therefore survive for a long time without being proven wrong.
When GPT is asked to develop an idea, it helps the idea become more coherent. When it is asked to defend the idea, it can provide convincing arguments. When it is asked to create a business case, it can fill the familiar sections.
None of that is equivalent to evidence.
The model may have improved the presentation while leaving the decision exactly as uncertain as it was before.
Innovation is not suffering from an idea shortage
Many organizations still design innovation work around the assumption that ideas are scarce.
They run challenges, workshops, hackathons and campaigns to generate more of them. Employees are encouraged to think differently, submit possibilities and explore what new technology might enable.
The resulting portfolios rarely fail because every idea was bad. They fail because too many ideas remain alive without earning their continued existence.
An idea gains a sponsor, a budget and a team. The team then becomes responsible for demonstrating progress. Evidence is collected inside a structure that already assumes continuation. Weak signals are interpreted optimistically because too much has become attached to the initiative.
GPT can accelerate this pattern. It makes it inexpensive to generate concepts, prototypes, narratives and business cases, but it does not make the later commitments reversible.
A prototype may now cost €5,000 instead of €50,000. The organization may still invest €2 million in scaling the wrong opportunity.
The economically important question is therefore not:
How cheaply can we create the next version?
It is:
What do we need to learn before the next commitment becomes justified?
This is where the best innovation prompt starts somewhere else.
Ask what would have to be true
Before asking GPT for ideas, features or strategies, ask:
What would have to be true for this opportunity to work?
The question changes the role of the model.
Instead of extending a preferred answer, GPT is asked to expose the conditions on which that answer depends. The output becomes an initial map of assumptions rather than a polished version of the idea.
The distinction matters because innovation decisions are rarely based on one large uncertainty. They rest on a system of beliefs about customers, behavior, technology, economics, distribution, regulation and organizational capability.
Some assumptions will be well supported. Others will be plausible but untested. A few may be carrying almost the entire risk of the opportunity.
The value of the question lies in making that structure visible.
Research on entrepreneurial decision-making supports the broader logic. In a randomized controlled trial, entrepreneurs trained to articulate predictions and test hypotheses more systematically made more precise decisions and were more willing to pivot when evidence challenged their original ideas.[9]
A larger replication involving 759 firms found that a scientific approach increased the termination of ideas and encouraged a more selective pattern of strategic change. The researchers argued that hypothesis-driven thinking increased “methodic doubt”: entrepreneurs became more aware that alternatives to their preferred explanation might exist.[10]
That is exactly the doubt that fluent AI output can otherwise remove too quickly.
The prompt is not the method
“What would have to be true?” is useful, but it is not a magic sentence.
GPT cannot tell you which assumptions are factually correct unless reliable evidence is available and supplied. It cannot interview customers, observe behavior or accept responsibility for an investment decision. It may overlook assumptions, invent evidence or rank risks according to a generic pattern that does not fit your situation.
Its role is to help make the reasoning inspectable.
The team still needs to decide:
which assumptions are relevant;
what evidence already exists;
where the model is merely guessing;
which uncertainty carries the greatest exposure;
what could be tested;
what result would change the decision.
GPT can support that work because it is patient, fast and capable of examining a proposition from several angles. It can ask uncomfortable questions without organizational status, sponsorship or sunk costs.
But it must be instructed to do so.
When asked to develop an idea, it tends to help develop the idea.
When asked to challenge the conditions beneath it, it can help the team think more scientifically.
The most valuable innovation prompt does not begin with the solution.
It begins with what the solution assumes.
