Product organisations have never had more access to customer insight. So why are better-informed teams finding decisions harder to make? Nitika Wahi explores one of the biggest shifts reshaping Product and UX Research today.
A few months ago, a VP of Product at a fast-growing SaaS company told me something that stuck with me. Her team had more customer data than ever: continuous interviews, in-product analytics, AI-generated synthesis of every support ticket and community post. And yet, she said, her roadmap decisions felt harder to defend than they had five years earlier, when her team had a fraction of the evidence. “We’re not short on insight,” she said. “We’re short on confidence.”
That conversation wasn’t unusual. For years, the challenge facing Product organisations was relatively clear: how do we get closer to our customers? Research was constrained by time, budget and access. Teams competed for research capacity. Customer interviews were carefully prioritised. Usability studies happened at key milestones rather than continuously. When evidence was scarce, the answer was to gather more of it.
Today, that picture has changed beyond recognition. Product Managers conduct interviews as part of continuous discovery. Designers regularly lead usability testing. Analytics platforms capture behavioural data at extraordinary scale, while AI can summarise interviews, cluster themes and synthesise qualitative research in minutes. Customer feedback flows continuously from support channels, community forums, app stores and in-product analytics.
On paper, this should be a golden age for evidence-based decision-making. Yet the conversations I’ve been having with Product and UX leaders tell a different story. Despite unprecedented access to customer insight, many describe decision-making as becoming harder, not easier. More signals to interpret. More perspectives to reconcile. More pressure to move quickly while staying confident they’re solving the right problem for the right customers.
It was while researching our recent Product Perspective Report, When Insight Moves Faster Than Confidence, that this paradox first became clear. Across conversations with senior Product leaders, one theme kept resurfacing: the bottleneck was rarely access to research. It was confidence. As I reflected on those conversations afterwards, another question kept returning to me: why are better-informed organisations finding decisions harder to make?
I think it reflects a deeper shift in how Product organisations create understanding, which is beginning to blur the traditional boundaries between Product, Design and UX Research.
The confidence paradox isn’t emerging in isolation. It’s the product of several shifts happening at once.
The first is impossible to ignore: AI has changed the economics of research. Tasks that once took hours or days can now take minutes: interviews can be transcribed and summarised instantly, hundreds of survey responses can be synthesised into coherent themes, and platforms can tag and cluster qualitative data at a scale that once required significant manual effort. AI is far from replacing human interpretation, but it’s dramatically lowering the barrier to generating and organising evidence. And that means research activities that were once reserved for specialist teams are now accessible to anyone curious enough to ask questions.
At the same time, Product organisations have been changing from within. Continuous discovery has pulled Product teams much closer to their customers. Rather than treating research as a discrete phase before development begins, many organisations now hold customer conversations throughout the product lifecycle. Product Managers interview users to validate assumptions. Designers run usability testing as they iterate. Engineers are increasingly exposed to customer feedback firsthand. Understanding users has become part of everyday practice, and that’s a positive evolution, because the closer decision-makers are to the people they serve, the more grounded their decisions tend to be.
The role of the UX Researcher, meanwhile, is evolving in a different but equally important direction. As more teams become capable of day-to-day research, the value of dedicated researchers increasingly lies elsewhere: helping organisations ask better questions, design stronger research programmes, and connect evidence to strategic decisions. Their influence is extending beyond interfaces and usability into organisational alignment, systems thinking and long-term product direction. Microsoft’s own UX research team has mapped a similar shift, describing UX researchers moving from ‘late-stage advisors’ to co-owners of product outcomes as traditional boundaries between UXR, Design, PM and Engineering break down.
In other words, research activities are becoming more widely shared while research expertise is becoming more strategic. Product Managers are interviewing customers. Designers are synthesising qualitative insight. AI is accelerating analysis. Researchers are shaping strategy rather than simply running studies.
It’s no surprise, then, that a different question has begun surfacing in conference talks and leadership meetings: How are the boundaries between Product and UX Research roles blurring?
Debating whether Product Managers are becoming Researchers risks distracting us from a much bigger shift: what happens when research itself becomes democratised.
For most of the past two decades, research was a specialist capability. Access to customers was carefully managed, methodologies required expertise, and synthesising evidence was slow. Research happened inside a dedicated function before its findings were shared with the wider organisation. That model no longer reflects reality. Customer understanding is now built through hundreds of small interactions rather than a handful of formal studies – Product Managers interviewing during discovery, Designers observing usability sessions, Customer Success surfacing recurring pain points, Sales bringing market feedback into roadmap discussions, AI helping organise it all.
This is, in many ways, exactly the direction the industry has been working towards and it’s arguably a sign of maturity, not something to fear. But democratising research is not the same as democratising understanding. Research is the process of generating evidence: asking questions, gathering observations, identifying patterns, documenting what customers say and do.
Understanding is different. It’s knowing which evidence matters most when different sources point in different directions. It’s recognising what hasn’t yet been asked, not just analysing what has. It’s weighing customer needs against technical constraints, commercial priorities and strategic ambition, and having the judgement to decide what should and shouldn’t influence a decision. Perhaps most importantly, understanding is collective: organisations don’t act because a single study exists, but because enough people share a common interpretation of the evidence and have the confidence to act on it.
That, I think, is where the conversation becomes most interesting. The real shift isn’t that Product Managers are conducting interviews, or that Researchers are becoming more strategic; those are symptoms. The real shift is that the production of research is becoming distributed across organisations, while the responsibility for creating understanding remains as challenging, and as valuable, as ever.
Is it true that research is becoming democratised while understanding isn’t?
If it is true, there are important implications for how Product organisations build competitive advantage. For years, the differentiator was access to customers, markets, specialist research capability. As the cost of generating information keeps falling, that advantage is eroding. AI can cluster themes, detect sentiment and surface anomalies with impressive speed, but analysis is not judgement. Judgement is deciding which customer segment matters most strategically, which problem is worth solving first, and how commercial priorities should be balanced against user needs. These are questions no synthesis tool can answer on its own. Confidence doesn’t come from accumulating more evidence indefinitely; at some point, leadership teams have to decide they know enough to move.
It could be fair to say that AI is democratising research, and also that it is not democratising judgement.
That isn’t a criticism of AI; we use AI extensively (and appropiately) in our work. But as the mechanics of research get easier, the quality of organisational judgement matters more, not less. The organisations that thrive won’t be the ones with the most information. They’ll be the ones best equipped to interpret it, challenge it, and act on it with confidence.
These challenges become significantly more complex when products are designed for global audiences.
Much of the current conversation about AI and research assumes a fairly simple relationship: a team conducts research, synthesises findings, and uses them to inform decisions. Very few global organisations operate that simply. When you’re building for twenty, fifty or a hundred markets, an insight gathered in one cannot be assumed to hold in another. Customer expectations are shaped by language, regulation, infrastructure, cultural norms and digital maturity, even where the underlying need is shared. The way it manifests, and the solution that feels intuitive, can be very different.
Research can tell us what customers in Brazil think about a payment flow, how users in Japan approach trust, or how privacy expectations differ between Germany and the United States. AI can process those findings faster than ever. What it can’t do is determine how those local truths should shape a global product strategy. Should an insight that holds consistently in one market influence the roadmap for every market? How should teams respond when evidence from two countries points in opposite directions? At what point does local optimisation start undermining the consistency of the global experience?
These are questions of judgement; distinguishing what’s locally specific from what’s universally relevant, separating meaningful differences from statistical noise, and recognising those insights that should shape global strategy aside from those that should steer local adaptations.
As AI makes it easier to gather and synthesise research at scale, the complexity of interpreting it increases. More markets generate more perspectives; more perspectives create more competing evidence; more evidence demands stronger organisational judgement.
For organisations building across cultures, languages and continents, context isn’t just something that enriches research. It’s the mechanism through which research acquires meaning. In global organisations, the challenge is no longer simply distributing research. It’s creating shared understanding from evidence that is often locally true but globally inconsistent.
I don’t think we’re witnessing the end of UX Research, or the emergence of a single hybrid role that replaces Product Managers, Designers and Researchers alike. I think we’re seeing something more interesting: the activities that once defined research are becoming distributed across Product organisations, while the responsibility for creating shared understanding – connecting evidence to strategy, navigating uncertainty, building confidence around hard decisions – is becoming more valuable than ever. That capability won’t belong exclusively to Product Managers or Researchers. It’s one the strongest organisations will build collectively.
This article is one perspective on a conversation we’re continuing to explore at intO. It builds on themes first identified in our recent Product Perspective Report, When Insight Moves Faster Than Confidence, where senior Product leaders consistently described confidence (not information) as the increasingly scarce resource in decision-making. If you’d like to explore those findings in more depth, download the report.
We’re now taking that conversation a step further. Over the coming months, We’ll be speaking with senior Product, UX and Research leaders from global organisations to understand how these shifts are playing out in practice. How are the boundaries between Product and UX Research roles evolving? Where does specialist expertise create the greatest value? And what capabilities will distinguish the strongest Product organisations over the next five years?
Those conversations will inform an intimate Product Leadership Roundtable in September, in the Bay Area (register your interest in attendance here), bringing together practitioners to challenge, refine and build on these ideas.
Perhaps the strongest Product organisations of the next decade won’t be those that generate the most research, but those that become exceptionally good at turning distributed evidence into shared understanding?
If that question resonates with your own experience, I’d love to hear from you. Download the report, join the conversation, or get in touch if you’d be interested in contributing your perspective.
Nitika Wahi
Commercial Director, intO
Get in touch with nitika@studio-into-com | Connect with me on LinkedIn
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