Log In/Sign Up Become a Member Submit an Idea
Search

Start with the Workflow, Not the AI

Posted on by Helen Taylor

A user-centered way to find AI opportunities scientists will actually use

Part 3 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.

Peter Hummel, User Research Lead, Novartis Biomedical Research

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.

AI will not create value in scientific research simply because it is powerful. It creates value when it removes real friction from scientific work.

Across life sciences, many teams are exploring generative AI, automation, copilots, and decision-support tools. The important question is not what AI can do, but where it can help people work better, faster, and with greater confidence.

The strongest AI opportunities often start with a simple question: Where does the current workflow create avoidable effort, delay, uncertainty, or rework?

User research helps make this friction visible. Interviews, contextual inquiries, journey mapping, and workflow analysis can reveal where people search across multiple systems, repeat manual tasks, rebuild context, or make decisions with incomplete information.

Only after gaining these insights should we ask whether AI is the right solution. Often it is. AI can help retrieve information, summarize evidence, reduce documentation effort, connect fragmented workflows, and support decision-making. In other cases, the better solution may be simpler: streamlined processes, improved usability, higher-quality data, stronger system integration, or a combination of these improvements.

Adding AI to a broken workflow can accelerate frustration rather than creating value. The goal should be not to revolutionize EVERY workflow with AI, but to apply it where it delivers meaningful benefits for both scientists and the business. The most effective solution could be a hybrid approach, where people, processes, technology, and AI each play the role they are best suited for.

The foundation is to first understand and improve the workflow, then introduce AI where it adds value – whether by transforming parts of the workflow, automating repetitive tasks, reducing user effort, providing relevant insights at the right moment, improving clarity, or supporting better decisions.

A practical way to identify high-value AI opportunities is to:

  1. Understand the workflow and the people involved.
  2. Identify where time, quality, confidence, or momentum is lost.
  3. Determine the root causes of the friction.
  4. Compare AI-based solutions with simpler alternatives.
  5. Prioritize opportunities based on user value, business value, feasibility, and likelihood of adoption.

For me, the key principle is simple: do not start by asking where AI can be applied. Better start by asking where people lose time, where workflows create friction, and where better support could improve speed, confidence, and decision quality.

Ultimately, the best AI opportunities are those that people trust, adopt, and integrate into their daily work because they make meaningful work easier, faster, and more effective.

Continue the conversation in October

Peter is speaking at the 2026 UX for Life Sciences Conference, 20–21 October, hosted by GSK in Stevenage, UK. His session, “From Interfaces to Intelligence: Future Skills for UX Teams in AI-Driven Workflows,” picks up where this article stops: if the question here is which AI opportunities are worth pursuing, his talk is about the skills UX teams need to deliver them.

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. It is a working conference, not a lecture series.

Register today

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. Peter was joined on the panel by other UX leads in pharma. The full recording is free to watch here.

And if you want something you can put to work this week, the UXLS Community has made available several free, practical resources:

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 on the UXLS Community webpage.

Your turn

Where does your workflow lose the most time, and would AI genuinely fix it, or just make the mess move faster?

Be specific if you can. Concrete examples of workflow friction are exactly what this community works from and can shape the workshop discussions in October.

The rest of the series

This is the final article in a three-part series from the UXLS Community on how AI is redefining user experience in life sciences. The other two articles were:

Where Stijn asked what role AI should play and Aspen asked what becomes of the UX researcher, this closing piece asks the practical question: where is AI actually worth applying?