Your UX research isn't the problem. What happens next is.

Consider an online retailer trying to increase sales.

The product team might redesign its checkout button, simplify navigation or introduce a promotional banner. All reasonable ideas.

But what if customers aren't buying because they don't understand the sizing information? Or because discount eligibility is confusing?

No amount of button optimization will solve those problems.

Chris advocates a "problem-first" approach that begins with understanding customer obstacles before developing solutions.

The distinction matters financially.

In one client analysis, Chris and his team found that experiments originating from research-backed customer problem statements had a more than 50% higher win rate than experiments without that foundation.

That's the kind of evidence that can change an executive's perception of UX research.

Rather than positioning research as a necessary step in product development, organizations can begin treating it as a measurable contributor to business performance.

Better research produces better ideas, even with AI

To demonstrate the difference, Chris developed an experiment that divided participants into two groups.

Both received the same challenge: improve a digital experience.

One group received only a broad business objective. The other received a customer problem statement supported by qualitative research and behavioral data.

The results revealed a familiar pattern.

Without customer context, participants gravitated toward generic solutions, such as making buttons sticky or adding social proof.

With research-backed problem statements, their ideas became more specific, thoughtful and relevant to actual customer frustrations.

Chris repeated the exercise using AI and observed a similar improvement when the model received meaningful research context.

The implication is significant.

AI can generate dozens of product ideas in seconds. But speed doesn't guarantee relevance. Without reliable customer evidence, AI risks becoming a faster way to produce solutions nobody needs.

The competitive advantage isn't simply having better AI tools. It's having better information to give them.

AI can rescue research from the forgotten report

There's another opportunity for AI that may be even more valuable than generating ideas.

Making existing research accessible.

Organizations often accumulate years of interviews, usability studies and customer feedback. Yet those insights remain scattered across presentations, repositories and documents that product teams rarely revisit.

Chris sees AI helping close that gap by allowing teams to retrieve relevant customer evidence when they're making decisions.

Imagine a product manager asking what customers struggle with during checkout and immediately receiving findings from previous research.

Instead of commissioning another study or relying on assumptions, the team can build on what the organization already knows.

AI can also challenge weak hypotheses by asking whether a proposed feature addresses an established customer problem.

But there's an important distinction between retrieving evidence and inventing explanations.

AI-generated assumptions about customer behavior aren't substitutes for actual customer research.

As Chris cautioned, "It has to be good quality research going into these repositories in the first place."

Researchers remain responsible for the integrity of that evidence. AI simply helps their work travel further.

Speak the language of business outcomes

Even excellent research can struggle to influence decisions when researchers and executives speak different languages.

Researchers discuss methodologies, sample sizes and usability findings. Business leaders want to understand conversion, retention, revenue and cost.

Both perspectives matter, but the conversation needs a common starting point.

Chris recommends beginning with the organization's business objectives.

If leadership wants to improve customer retention, researchers should explain how their work will uncover the obstacles preventing customers from returning.

If the priority is increasing conversions, research should identify where and why customers abandon the journey.

The methodology still matters. It just shouldn't dominate the conversation.

Amrit reinforced the importance of connecting research to commercial priorities, particularly when teams struggle to identify the metrics their stakeholders care about.

That means getting closer to product leaders, understanding their objectives and demonstrating how customer evidence can improve the decisions they're already making.

The goal isn't to turn UX researchers into financial analysts. It's to make the business value of their expertise impossible to ignore.

Give product teams a roadmap of problems, not features

Perhaps the biggest opportunity is organizational.

Chris recommends running continuous opportunity discovery alongside product development rather than treating research as something that happens only after a feature has been proposed.

Researchers identify and prioritize customer problems. Product teams develop potential solutions. Experimentation measures what actually works.

This creates a roadmap organized around problems worth solving instead of features waiting to be shipped.

It also gives designers greater freedom to experiment.

Rather than defending one preferred solution, teams can test several approaches against the same customer problem, learning which delivers the strongest results.

That's particularly important as AI makes building and testing new experiences faster.

The danger isn't that organizations will run out of ideas.

It's that they'll produce more ideas than they can meaningfully evaluate.

For UX researchers, the opportunity is to become the people who help organizations decide where to focus their increasingly abundant development capacity.

That requires stronger connections between research, experimentation and business strategy. It also means taking ownership of the customer problems that deserve attention.

Chris offered a memorable challenge to researchers looking to elevate their influence, "Make a hero of those triangulated customer problem statements.”

Additional resources

These four resources complement the article's central themes: connecting UX research to business outcomes, using AI to make insights more accessible, and helping product teams solve the right customer problems.

• Webinar: From insights to impact: making research stick—Learn how to communicate research findings more effectively, use storytelling to engage stakeholders, and turn customer insights into business decisions.
• Guide: Human insight for the AI-driven product development process—Explore why continuous customer discovery, early validation, and human judgment become more important as AI accelerates product development.
• Podcast: From doing research to leading it: how UX research creates real business impact—Hear how UX researchers can move beyond delivering findings to influencing strategy, earning stakeholder trust, and demonstrating measurable business value.
• Blog post: Future-proofing your UX research career—Discover why translating customer insights into business priorities is becoming an essential skill for UX researchers seeking greater strategic influence.