For most product teams, getting actionable insights about what your users need you to build requires spending weeks conducting user research, analyzing data, collating insights, and defining your product development roadmap.

Let's instead imagine if we could go from start to finish in half the usual time all while making informed decisions based on direct user feedback. 👀

That’s the advantage of using AI for product discovery.

AI tools shorten the time-to-insight and offer greater scalability without compromising on the quality of output, as shown in our latest benchmarking study.

In this post, we’ll break down the most effective ways to use AI in your product discovery process with examples and tips for every use case. But first, let’s get the basics out of the way.

What is product discovery (and why you need it)

Product discovery is the process of closely understanding and validating user problems to determine the best solutions you can build into your product. It’s an evidence-informed approach to learning what product you should build (and how to build it) with input from your actual users.

The focus is on understanding the value you can create for customers with new capabilities before starting the actual development efforts, and then continually calibrating your product development based on what you understand about your customers.

A good product discovery process sets you up for long-term success. Here’s how:

Clear understanding of customer needs: You get first-hand insights into what users really expect in your product. Use this information to guide your PRD and build capabilities customers will actually love.

Minimize risk of product-market misfit: Product discovery validates your hypotheses about user needs and confirms whether there’s enough customer interest for a specific feature. It essentially prevents you from going against the market demand.

Reduce wasted effort and resources: A good discovery process is critical for small teams where the same people are involved in the discovery and delivery process. They can strategically dedicate their resources and limited bandwidth to developing capabilities that your users value.

Fast-track development: The discovery process also offers more clarity and a good direction for your development efforts. When you know exactly what to build, you can work faster to roll out new capabilities without getting bogged down with more questions.

Is a continuous process: Discovery doesn’t just happen once, it’s ideally a continuous process as you evolve your product. According to author and product coach Teresa Torres, continuous product discovery involves “at a minimum, weekly touchpoints with customers by the team building the product, where they're conducting small research activities in pursuit of a desired product outcome.”

The bottom line: without a strong product discovery process, you’re shooting in the dark and hoping something strikes. You can change that by leveraging AI tools to increase productivity and speed up the discovery process. Let’s see how.

Using AI in product discovery: A 5-step playbook

We’ll break down the core steps of the Double Diamond Discovery framework and share our best tips + tools to integrate AI in each step of the process.

1. Understand underlying user needs

The product discovery process starts with identifying a broader set of problems users struggle with.

Before you start outlining your product roadmap, you have to determine exactly what problems you can solve for your customers—and whether these problems are pressing enough for people to use your solution.

This is where a research study can eliminate guesswork and will help you understand your users up close, create journey maps, and discover their actual pain points.

But, here’s the catch: conventional research methods, like user interviews or focus groups, are heavy on time and effort. You have to spend time scheduling, moderating, transcribing, and analyzing each interview.

AI tools can fill this gap and significantly speed up your product discovery from start to finish.

For example, with AI-led user interviews, you can conduct interviews and extract valuable insights at a 16 times faster pace than a human-moderated study. Purpose-built for scalability, Wondering’s AI interview studies can intelligently moderate the conversation with the participant in over 30 supported languages.

Plus, you can combine both qualitative and quantitative questions to get a more holistic understanding of customer pain points.

Besides user research, you can also leverage AI to perform market research and analyze your competition. You don’t have to waste time collecting and sifting through heaps of data on your competitions. AI tools can source and summarize all competitor benchmarking data to identify opportunities for your product.

2. Define and validate the problems

Once you’ve identified a set of user pain points, it’s time to validate these problems and find the most critical ones to solve.

This step involves rigorous data analysis. The more data you analyze, the more patterns you’ll discover in user responses to narrow down the most pressing problems.

AI tools can analyze data faster and derive actionable insights from your user studies. You can use tools like Wondering to instantly transcribe interviews and create a meaningful summary of all responses. This summary documents:

The most common themes across responses

Key quotes for each theme with exact audio notes

An overview of the respondents’ sentiments in the study

Analyzing responses will reveal the biggest problems your users are experiencing. Then, you have to determine how big the problem is and the value you can create by solving it. This deeper analysis will make it easier to prioritize what you want to solve first and how.

3. Ideate possible solutions

With a list of problems to work toward, you’re all set to start brainstorming the most effective ways to solve user problems. This step will rely mainly on cross-functional collaboration, using activities like mind mapping and design sprints to collect input from the entire team.

AI tools can streamline the entire process and keep the whole team on track. For example, you can use tools like Miro Assist to generate a mind map in seconds and collectively discuss different ideas.

But here’s the kicker: things can quickly go south if your ideation process is entirely based on intuition instead of user expectations. That’s why it’s important to consistently validate your assumptions and assess a concept with quick design testing.

You can create a design test within minutes using Wondering—add a design or concept image and include one or a sequence of questions. Users can see the image and respond to your questions while Wondering transcribes and analyzes their responses in real-time.

By collecting and sharing insights in just a few hours, these design tests make it easy to validate every idea before putting it into action.

4. Prototype your ideas

The big heavy lift comes in the prototyping stage when you’re ready to translate your ideas into a mockup and collect user feedback. To get started exploring design concepts, creating mockups or concepts is another time-consuming task that you can speed up with AI tools like Galileo, Visily, and Prototypr.

These tools can turn something as simple as a sketch or a one-line prompt into a initial design concepts that you can then expand on. You can customize this output to make the design more aligned with your goals.

You also have the option to choose a template instead of starting from a blank canvas and conveniently whip up a mockup in a few hours.

5. Test and iterate

After rolling out the prototype, all that’s left to do is collecting user feedback and iterating your designs.

AI-powered user interview tools can make this process much quicker and frictionless. You have the advantage of moving with speed and precision while iterating the prototype based on users’ expectations.

A good AI tool lets you evaluate your designs from multiple angles, such as:

A/B tests

Design tests

User interviews

Feedback surveys

You can circle back to step 1 and create similar research studies to learn what users think about your prototype. With Wondering, you can go from creating a study to collating insights in a few hours. That means it’s easier to make product discovery a continuous habit without feeling overwhelmed or lost in the process.

Mitigating the risks of using AI in product discovery

We know what you’re thinking: Don’t large language models product hallucinations? How can I trust it to support my product discovery?

A common concern with adopting AI into mission critical workflows is hallucinations. Hallucinations occur when an AI system generates false or inaccurate information, typically due to limitations in the AI's training data.

Fortunately, by keeping these limitations in mind, AI tools like Wondering can implement checks and controls in their AI systems that mitigate these limitations. If you’re curious to learn more about how tools like Wondering do this, check out this article.

Make product discovery a breeze with AI

Product discovery is the legwork required to ensure you don’t end up building a tool or feature that nobody wants. Sadly, too often product teams end up shooting in the dark instead, not really knowing how to make discovery a continuous habit. You don’t have to anymore.

AI tools can speed up your product discovery process, taking you from discovering problems to testing the prototype in a matter of hours rather than weeks.

Want to fast-track your discovery and development cycle? We’d love to show you how. Check out a free trial of Wondering today.

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Author

Axel Thomson

Founder & CEO

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