How to use AI to validate your app idea before you start

AI can feel magical. You type a question, and instantly it gives you structured information, comparisons, or suggested improvements. But it’s important to understand its limitations.

Used well, AI can help you step outside your own thinking. It can highlight potential challenges you may not have considered, reflect back the assumptions you’re making about users, and surface patterns from similar types of products in the market. This can be incredibly useful early on, when you’re still shaping the problem you want to solve.

What it can’t do is confirm whether something is technically feasible, estimate real-world costs or timelines, or design a product that genuinely works for users. Those things depend on context, constraints, and experience. AI doesn’t understand your business, your customers, or your long-term goals in the way a product team does.

The most productive way to think about AI is as a way to pressure-test your thinking, not to replace it.

Before you even open up an AI tool, it’s worth spending some time getting clear on your own thinking. The better you understand your idea, the more useful AI becomes.

Start by grounding yourself in the problem. Who is this for? What’s frustrating or inefficient about the current way they solve this problem? Why does this problem matter enough for someone to change their behaviour and use a new app?

It’s also useful to separate what feels essential from what feels optional. Many early app ideas become bloated because everything feels important. Being honest about what absolutely needs to exist for the idea to be tested helps keep you grounded.

Finally, try to articulate what success actually looks like. Is success people using the app daily? Completing a specific action? Saving time? Improving an outcome? Even rough thinking here will help frame more meaningful conversations later on.

At this stage, AI works best when you already have a point of view. It’s there to challenge and refine it, not to create it from scratch.

Once your idea is loosely defined, AI becomes useful as a way of poking holes in it. The aim here isn’t to get reassurance, it’s to expose weak spots early, when they’re still cheap to fix.

For example, you might prompt:

• Prompt: “Here’s my app idea: a mobile app to help remote workers manage their mental wellbeing through daily check-ins and small habits. What challenges might users face with this type of product?”
• Example output (summarised): It might highlight that users could struggle with habit fatigue, privacy concerns around mental health data, or difficulty seeing long-term value from short daily interactions.

Even if you don’t agree with everything, this kind of response forces you to think: how would we make daily check-ins genuinely valuable? How do we reassure users about data privacy? What makes this feel meaningfully different from existing wellbeing apps?

Another useful angle is external scrutiny:

• Prompt: “If you were an investor or partner reviewing this app idea, what questions would you ask to test whether it’s viable?”
• Example output (summarised) : Questions around user acquisition, long-term engagement, differentiation in a crowded market, and how impact would be measured.

This can help you sense-check whether your idea stands up to basic commercial and product scrutiny, even at an early stage.

AI can also help you sanity-check your positioning:

• Prompt: “Compare this idea to existing apps in this space and highlight potential gaps or opportunities.”
• Example output (summarised): It might point out that many wellbeing apps focus on content, whereas there could be an opportunity around lightweight tools embedded into a user’s daily workflow.

Again, the value isn’t in the AI being “right”, but in the way it helps you interrogate your own assumptions and sharpen your thinking.

Once you’ve had a few rounds of validation, the goal is to bring everything back into something tangible. This is where a lot of early-stage ideas fall down: people gather loads of input but don’t synthesise it into something usable.

A strong early brief doesn’t need to be perfect, but it should be clear. You should be able to describe your idea in one or two sentences without caveats. You should be able to articulate the core problem and why it’s worth solving. You should have a short list of features that feel essential to testing the idea, rather than a long wishlist of everything the app could become one day.

It’s also healthy to be explicit about what you don’t yet know. These unknowns are often the most valuable starting points for working with an experienced product team. They shape the right questions, research, and discovery work early in the process.

By the time you reach this point, AI has done its job. It’s helped you think more clearly, not given you a ready-made solution.

A couple of small behaviours can dramatically improve how useful AI is at this stage:

• Treat its answers as hypotheses, not facts.
• Ask follow-up questions that challenge the first response.
• Rephrase the same question from different angles to see what changes.
• Stay focused on validating your idea, rather than drifting into solution or implementation detail too early.

The people who get the most value from AI are usually the ones who already think critically. AI simply gives them a faster way to explore their thinking.

AI can help you arrive at the starting line with more clarity, better questions, and fewer blind spots. That alone can save months of wasted effort later on. But turning a validated idea into a product that people actually want to use still requires experience, design thinking, and technical expertise.

If you’ve used AI to pressure-test your idea and shape a clearer brief, you’ll be in a much stronger position when you start conversations with a product team. From there, the real work begins: turning insight into something that works in the hands of real users.

The next step is turning that thinking into a product that works in the real world. We’ll help you challenge assumptions, define what really matters, and shape an app that delivers genuine value for your users and your business.