The second of a 3-part article series on how AI is redefining user experience in life sciences from the UXLS Community of Experts at the Pistoia Alliance.
Aspen Snow, Senior User Experience Researcher, Merck Sharp & Dohme LLC, Rahway, NJ, USA
This article is written in a personal capacity. The thinking here grew out of conversations in the UXLS Community of Experts and reflects the author’s own view rather than a formal position of their employer or the Pistoia Alliance.
A year ago, I worried that AI would slowly chip away at the parts of the research I loved most. If AI could write discussion guides, summarize interviews, build personas, generate journey maps, and even draft reports, where would that leave us researchers?
Like many practitioners, I had spent years developing the skills and methods that defined my craft. Then suddenly, tools were appearing that could perform many of those tasks in seconds. After a mild existential crisis and some experimentation with AI tools, I came to realize that AI is brightening – rather than diminishing – the sparkle of UX research.
Rather than replacing researchers, AI is creating space for us to operate at a more strategic level. The future UX researcher won’t just conduct a study. They’ll be the “expert in the loop”– an essential bridge between business and user needs – helping teams interpret what the data is really saying, especially when the answer is messy, inconvenient, or not what anyone expected.
The shift from researcher to orchestrator
Historically, UX researchers owned the research process end to end. We planned the studies, recruited participants, conducted interviews, analyzed findings, and delivered recommendations. Research was a sequence of activities that we all agreed upon, and our value was closely tied to executing them.
Today, AI can perform nearly every step in that process, all before you finish your morning coffee.
The question is no longer whether AI can generate insights.
The question is: When anyone can use AI to generate insights, who helps determine what they mean?
That’s where I believe the future UX researcher shines and becomes the orchestrator.
As AI tools lower the barrier to executing research tasks and producing artifacts, the value of UX research shifts from simply conducting studies to orchestrating understanding. In practice, this means connecting fragmented data, user behaviors, business context, and team assumptions to enable better decisions. It means choosing the right methods, designing research that is thoughtful and unbiased, and bringing empathy, human judgement, and accountability into the process, so teams can move from scattered insights to understanding with intention.
Researchers are organizational memory
For years, one of the biggest challenges organizations faced was collecting information. Research was often constrained by time, resources, and access. Today, information is abundant. AI can summarize interviews, cluster themes, create personas, generate journey maps, and even propose design concepts. The challenge is no longer generating knowledge. It’s understanding our data in context and discerning what matters most, when it matters most.
Researchers are uniquely positioned to serve as organizational memory by connecting dots between:
- Previous studies
- Historical decisions
- Business context
- User behavior patterns
- Emerging trends
While AI can help us summarize what was said, researchers will have the context of why it was said and whether it still applies. Researchers are the ones who recognize when a pain point echoes across another workflow or remember when another team tried a similar solution and struggled. We flag when an old insight means more now than it did a year ago, because the business context has changed.
Organizations are efficient at storing knowledge, but the value of the future researcher is not being the person with the most data. The value comes from serving as the organization’s living memory, and ability to recognize when something from the past should shape today’s decisions. Without this organizational memory, teams risk rediscovering the same pain points, rebuilding the same flawed workflows, and treating every user issue as isolated events.
From user advocate to human steward
As AI becomes embedded in the products, platforms, and workflows that shape our work, researchers may find themselves taking on a broader responsibility.
UX researchers have always served as advocates and voice for users. In the future, we may become stewards of the human experience itself.
This will include helping organizations navigate questions of:
- Trust
- Transparency
- Explainability
- Fairness
- Human agency
These elements are showing up more in my daily work. One day I’m talking with scientists. The next I’m speaking with engineers, product managers, designers, or leadership teams. Everyone views the problem through a slightly different lens.
More and more, my job isn’t finding answers; it’s helping everyone agree on the question we’re trying to solve.
As technology becomes more capable, ensuring it remains human-centered becomes increasingly important. The future UXR is not only responsible for understanding people. We are also responsible for ensuring that the systems being built continue to serve people in meaningful, ethical, and trustworthy ways.
AI Doesn’t Mean the Future Is Less Creative
One of my biggest concerns about AI was that it would make research less creative. Researchers invest significant time creating artifacts: discussion guides, surveys, personas, journey maps, reports, presentations, workshop activities and countless sticky notes.
When AI started generating many of those outputs in seconds, I worried that some of the creativity I’d always loved about the profession might disappear.
What I’ve experienced has been almost the opposite.
I’m spending less time formatting slides and more time thinking. Less time moving sticky notes around and more time asking whether we’re solving the right problem in the first place.
In many ways, AI has given me the room to be more creative, not less.
The artifact becomes a commodity. The insight becomes the value.
The future researcher isn’t necessarily the person who creates the most polished journey map. It’s the person who uncovers the insight that changes the direction of a product, program, or organization.
Creativity hasn’t disappeared. It’s simply moved upstream.
I now find it in framing problems, asking better questions, challenging assumptions, identifying tensions, and imagining better futures.
Looking to the Future
UX researchers have been valued for the studies we conducted and the artifacts we produced. As AI democratizes creation, I don’t think the future UX researcher will spend their days competing with AI to create artifacts faster.
Instead, I believe our value comes from something much harder to automate: our ability to ask better questions, connect seemingly unrelated ideas across teams, facilitate and build shared understanding, influence decisions, and imagine better futures.
AI can generate endless information.
But researchers create its meaning. And honestly, that’s the part of the job I never wanted to automate anyway.
Continue the conversation in October
Aspen is speaking at the 2026 UX for Life Sciences Conference, 20–21 October, hosted by GSK in Stevenage, UK. Her session, “Practical AI for UX Research in Life Sciences: Tools, Trials, and Takeaways,” is the practical counterpart to this article: if this piece is about why the researcher’s role is shifting, her talk is about the tools she has actually tried, what worked, and what didn’t.
Two days, four themed sessions and four hands-on workshops, with practitioners from Roche, Elsevier, GSK, AstraZeneca, EMBL-EBI, Harvard Medical School, Novo Nordisk, Novartis, MSD and others. This is a working conference, not a lecture series.
Want a first dive into the topic?
This article grew out of a UXLS panel discussion in June titled How AI Is Redefining User Experience in Life Sciences. Aspen was joined by UX leads from Novartis, Novo Nordisk and AstraZeneca. The full recording is free to watch here.
And if you want something you can put to work this week, the UXLS Community has put out free, practical resources:
- Setting up a UXLS Community of Practice: What makes a CoP succeed or fail? How to start small and build momentum? A proposal template to help articulate value internally.
- Quick-start Learning Resource for AI: A curated selection of materials to help UX professionals explore the intersection of user experience and artificial intelligence.
- The UXLS Maturity Model: A tabular guide to assess the integration and impact of your UX strategy.
About the UXLS Community
User Experience for Life Sciences (UXLS) is a community of experts at the Pistoia Alliance. We bring together UX designers, researchers and leaders from pharma, technology vendors and academia to raise the standard of user experience across life-science R&D through best-practice guides, toolkits, and open discussion of what works.
We meet regularly and because the Pistoia Alliance provides a pre-competitive framework for these conversations, members can compare notes across organizations without the usual hurdles.
If you work on the tools scientists use, you are welcome. Find out more on the UXLS Community webpage.
Your turn
What is the one part of your research practice you would never hand to AI, and what changed your mind about something you thought you’d never hand over?
Be specific if you can. Including the tools that disappointed you. This is the kind of input from which this community works and may shape the workshop discussions in October.
The rest of the series
This is Part 2 of three articles from the UXLS Community on how AI is redefining user experience in life sciences. Part 1, by by Stijn Janssen (Novo Nordisk), examines the blind spots of explainable AI. Part 3, Peter Hummel (Novartis Biomedical Research), argues that AI opportunities are found by mapping workflow friction, not by looking for places to apply AI and will drop next week.