Why Validating AI Product Ideas Matters
Understanding why validation is especially important for building successful AI products and AI features
Currently, there’s a massive interest in building AI products. Many companies are rushing to develop new “intelligent” features or even entirely new AI products. This technology promises to help product teams tackle unsolved problems and redefine user experiences. However, in many cases, the decision to use AI is rooted in an interest in creating business value rather than customer value. The underlying intention is often to capitalize on AI somehow (to increase efficiency, reduce costs, etc.), not to create genuinely better solutions to customer problems. As a result, many products now have AI features that are just not good or even useful.
While bad product ideas have always existed, as AI accelerates product development, there’s a real risk of teams pursuing more bad ideas, just because they can. Well-intentioned teams can also end up building more solutions in search of problems because they are too optimistic about what AI can actually accomplish. While it certainly has a lot of potential, unrealistic expectations only lead to disappointment. Product teams still need to validate product ideas. AI may not be the right choice for every situation. It certainly should not be the default solution for every problem.
What Is Validation
Validation is testing assumptions and gathering evidence to de-risk product decisions.
Validation is the process teams use to verify that they are solving the right problems and building the right solutions. In every organization, different people have different ideas about what problems to solve and what solutions to build. However, every product idea involves some assumptions, and untested assumptions increase the risk of failure. Many products and features have failed because teams built something they believed to be useful without sufficient validation. Therefore, teams must define their risky assumptions and test them to verify that they are making the right decisions and working on the right things.
What Do Teams Need To Validate
The purpose of validation is to help product teams verify the following:
They have a real, painful, and significant customer problem.
There is a strong business case for solving the problem.
They have a useful, usable, and desirable solution.
When Do Teams Need To Validate
Validation is part of the product discovery process, which aims to identify a painful customer problem and define a viable solution. Discovery starts with initially exploring what’s possible (diverging) before narrowing down (converging) on specific ideas. As teams explore the problem and solution space, they discover many opportunities to pursue. They need to validate if they have identified problems and solutions that are valuable to the customers and the business. While AI is opening up new opportunities, many AI features fail to produce the expected results because they lack strong validation. Recent research indicates that 95% of companies are not seeing a return on their substantial AI investments. This highlights the need to validate AI features to avoid focusing time, effort, and resources on the wrong targets.
Why Is Validation Important
Validation helps us ensure we are solving the right problems and building the right solutions.
Validation helps teams build better products.
When product teams build something that fails to produce the desired outcomes, many organizations are quick to attribute this to poor execution. While execution can be to blame, often the team’s efforts can be derailed from the beginning if they don’t prioritize validation. When teams pursue unvalidated (or poorly validated) product ideas, they inevitably set themselves up for failure. Their products fail to resonate with users because they solved the wrong problems and/or built the wrong solutions. The problems and solutions teams focus on strongly influence their thinking and decision-making. It shapes their choices across the product development process, affecting what goals they set, what they build, and how they build it. Therefore, validating product ideas is critical for maximizing the odds of building a successful product.
Validation helps teams at every stage of development.
Validation does not stop after the team has chosen a problem to solve and a solution to build. Many critical product decisions need to be made between committing to an idea and delivering the finished solution. Each one of these must be validated to produce a useful, usable, and desirable solution. At each stage, the goal is to test assumptions, gather evidence, and reduce uncertainty before investing more resources. The circumstances can change mid-development, so teams need to verify that their current approach remains relevant. Even after release, product teams must validate whether their solution works (and continues to work) as expected.
Validation helps teams de-risk product ideas.
Teams have finite resources, and they need to use them wisely. They cannot afford to build features based on gut feeling or intuition alone. While conviction is important, so is evidence. They should not be committing to building something unless they can prove that there is an opportunity there. The discovery process helps product teams reduce the risk associated with product decisions. It ensures that the team is not just chasing interesting ideas but investing in solutions that solve real customer problems. Validation gives the team proof to support their decisions, ensuring they build products that are actually valuable to users. It reduces risk by giving us data points to navigate uncertainty scenarios. While this does not necessarily eliminate failure, it does increase the likelihood of success.
Why Validating AI Product Ideas Is Especially Important
Validation helps teams discover painful customer problems.
Every product idea starts from an insight into a meaningful need that is not being met. Success often hinges on the team’s ability to identify, understand, and solve the underlying problem. We must articulate the customer’s specific needs, and then pressure test those needs to determine how painful the problem is: Is a solution a must-have or a nice-to-have (a need or a want)? Painful problems have an outsized impact on a customer’s experience. There are multiple dimensions of a compelling problem. So, we must assess how painful a customer problem is to effectively evaluate if it’s worth solving.
The need to validate customer problems remains the same, regardless of whether solutions use AI or not. However, validating AI features is especially important because, when talking to customers, teams sometimes project their own enthusiasm about AI. They interpret lukewarm customer interest as positive signals because they are looking for problems AI can solve, instead of painful problems. In reality, anything short of “I want this!” is really a “No thanks.” Customers will react strongly when a problem matters to them. It will be evident in their responses, reactions, and behaviors (in interviews, experiments, prototype tests, etc.).
Validation helps teams objectively evaluate AI use cases.
AI is reshaping how product teams operate. It’s now possible to solve previously unsolvable problems and build previously infeasible solutions. As a result, we are now seeing AI features pop up in many products. While AI can enhance many interactions, an AI feature is not always valuable just because it’s viable. The users may not want these features. They want to address their specific needs. An AI feature may spark their interest in the short term. However, if there is no meaningful improvement in their experience, they won’t continue to use the feature, no matter how ”intelligent” it is. There are also limits to AI’s intelligence (until someone figures out AGI at least).
AI will certainly be useful in the development process, but it may not be useful in every feature. Teams must still validate the desirability, feasibility, and viability of AI product ideas: How and why would this AI feature benefit customers? What aspects of the problem is AI uniquely equipped to solve? How difficult would it be to develop this feature? Etc. They should thoroughly investigate whether an AI feature can actually create better experiences. Their decisions must be rooted in solid evidence, logic, and reasoning.
Validations help teams verify if certain AI solutions are feasible.
Many unrealistic expectations about AI stem from a lack of understanding of what it is, how it works, and, most importantly, what it can do. AI represents a broad spectrum of tools and technologies. There are many ways to implement AI systems. Each configuration can lead to different results. For example, the AI model used in an AI feature can greatly impact the quality of the outputs because certain models are better at certain tasks. Even if the right model is chosen, it may not perform as expected without the right fine-tuning. Similarly, there are many other variables to consider when using AI (such as prompt engineering, AI evals, etc.).
Many teams will not fully understand the non-intuitive things that make successfully implementing AI hard. They may also not currently possess the necessary skills, knowledge, and expertise to use AI effectively. So, they need to validate whether they can even build an AI feature that can consistently produce reliable or useful outputs. Their goal is to enhance the user experience, not diminish it. Therefore, they must verify what their AI feature can realistically accomplish, based on the team and the AI system’s current limitations. This helps them narrow the scope of feasible solutions and focus on building features that are more likely to be meaningful to users.
Validation helps teams determine the right ways to implement AI systems.
There are many ways to design, build, and operate AI solutions. So, the team must thoroughly evaluate how they plan to implement AI: Does the solution require some prompt engineering? If yes, then what would the optimal prompt be? Does it require an AI workflow or an AI agent? If it needs an agent, then what type of agent would work best? Does it require an AI eval? If yes, then what type of eval would ensure the best results? Every decision influences the output in different ways. However, the team can reduce the likelihood of failure by validating their decision-making with strong supporting evidence.
Validation is also critical for ensuring AI features remain effective. AI systems can be black boxes where inputs mysteriously turn into outputs. Therefore, we can’t always guarantee that they will produce the expected output. So, we need to verify their behavior through testing and place guardrails to mitigate potential issues. Even if everything currently works fine, AI features could still fail to work as expected once deployed. They could encounter scenarios that teams could not account for and respond erratically. Therefore, product teams need robust monitoring and evaluation systems to validate that their solution keeps working as it should.
Validation helps teams avoid AI applications that are just too hard to build.
The potential of AI-powered solutions can make previously unsolvable problems seem a lot more solvable. However, many teams fundamentally misunderstand AI’s capabilities and what it takes to build a successful AI system. They fail to realize just how hard solving certain problems will be. AI makes product teams especially vulnerable to something called tarpit ideas. These are product ideas that seem great, but are really not viable or feasible. These are enduring problems that people keep trying to solve.
Unlike genuinely hard problems, a potential solution to these problems feels within reach. What makes tarpit ideas so tricky is that they get good initial validation. Customers like the idea, and the team generally gets a lot of positive feedback and interest. When you can’t get any positive feedback, it’s obvious that your idea is bad or unappealing. With tarpit ideas, it can be less obvious that something is not worth building. The substantial challenge of building a good solution only becomes evident later on, after resources have already been sunk.
Validation helps teams use AI to move fast and move in the right direction.
Some people believe that they can now build without validating, since development is now faster and easier with AI coding assistance. They can rapidly develop new iterations or just start over if a feature is unsuccessful. Eventually, they may figure out a version of their initial idea that resonates with users. However, the time, effort, and resources wasted in pursuing the bad ideas cannot be recovered. Good teams understand that speed without direction is not useful. The team needs a well-defined target to aim for, so they can concentrate their efforts. Validation helps them choose the right targets. It also ensures that the right product, design, and engineering decisions will be made.
Conclusion
Validation helps us ensure that AI product ideas are valuable to customers and the business.
Product teams must be relentlessly focused on finding the right targets. That’s how they make real progress and build sustained momentum. The teams that consistently favor evidence-based decision-making will likely perform better in the long term than those who prioritize speed at all costs. While pursuing new, untested ideas can pay off sometimes, people often grossly overestimate their odds of success. Disciplined experimentation can be a great tool for deeply exploring problems and solutions. Rigorous validation can drastically improve the quality of the products built. Investing time into validating ideas is almost always more beneficial than blindly building and iterating. As AI systems become more capable, the best products will come from teams that don’t fall for the hype and stay committed to solving the right problems and building the right solutions.