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

Author: Helen Taylor

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?

Finding Our Place in the Age of AI

Posted on by Helen Taylor

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. 

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. 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: 

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. 

How the pharma industry built a common framework for medicinal product data

Posted on by Helen Taylor

This is an article from the Pistoia Alliance IDMP Ontology community. 

The ISO IDMP standards specify what medicinal product data should include, but they cannot eliminate differences in regional regulatory requirements or organizational perspectives. As organizations began implementing the standards, there was a growing need for a common semantic framework that could connect these perspectives and support more consistent data exchange. 

The obvious fix was for the whole industry to build one shared model together with regulators instead of a dozen diverging ones. The hard part was doing that between competitors, in the open, without anyone owning it. This is the story of how that happened, from an informal conversation outside a conference in 2021 to an open, industry-owned ontology in production today. 

What this article is about

A short history of the Pistoia Alliance IDMP Ontology (IDMP-O): where the idea came from, how it became a pre-competitive Pistoia Alliance initiative, and how it grew from a first proof of concept into a shared industry asset for pharma. It’s the origin story behind a piece of shared infrastructure many teams now rely on. 

Who should read this article

  • Regulatory, data standards and semantic technology teams in pharma weighing up ISO IDMP implementation.
  • Data and IT leaders who want to understand where the IDMP Ontology came from before adopting it.
  • Anyone curious about how pre-competitive collaboration works; how competitors build shared infrastructure once, and together, rather than repeatedly in isolation.

If you’ve ever wondered why the industry chose to solve the IDMP data problem collectively, this is for you. 

A shared foundation

Every so often, a shared foundation emerges not because one company decides to build it, but because an industry recognizes a common challenge and comes together to solve it. The IDMP Ontology is one such example. Here is how it came together. 

The problem IDMP-O set out to solve

As global regulatory authorities, including the European Medicines Agency (EMA), progressed their adoption of the ISO standards for the Identification of Medicinal Products (IDMP), it became increasingly evident that organizations were implementing these standards differently according to varying requirements and perspectives. While the standards define the medicinal product information that needs to be documented, they do not provide a common semantic framework to support consistent understanding across organizations, systems, and jurisdictions. Consequently, differing implementations driven by regional requirements and organization perspectives can create challenges for data integration, interoperability, and information exchange throughout the pharmaceutical value chain. 

Today, the IDMP Ontology is an industry-led initiative under the Pistoia Alliance that solves those pressing challenges and enables product data interoperability. 

From early experiments to a formal initiative

The origins of the IDMP Ontology can be traced back to 2021. Representing a group of data and semantics experts from Roche, Bayer, GSK, ACCURIDS, Novartis, and Merck KGaA, Sheila Elz, Heiner Oberkampf, and Gerhard Noelken proposed to the Pistoia Alliance the creation of a shared, vendor-neutral semantic foundation for medicinal product data. 

Following a collaborative workshop, the project was formally launched as a pre-competitive initiative. Its mission was to develop an open-source, machine-readable semantic model that would complement existing IDMP standards, reduce implementation ambiguity, and enable FAIR (Findable, Accessible, Interoperable, and Reusable) data across regulatory, clinical, manufacturing, supply chain, and pharmacovigilance domains. 

Proof, then momentum

Phase 1 of the project commenced in 2022 with sponsorship from seven leading pharmaceutical companies. Within the same year, the team successfully delivered its first Minimum Viable Product (MVP), focused on substance-related use cases. The ontology was validated using real-world industry data and demonstrated the value of semantic interoperability for medicinal product information management. 

Building on this success, the project rapidly expanded to include additional pharmaceutical companies, technology providers, standards organizations, and regulatory stakeholders. Participants and sponsors have included Bayer, Novartis, GSK, Roche, Merck KGaA, Boehringer Ingelheim, Johnson & Johnson, AstraZeneca, Amgen, AbbVie, MSD, and Pfizer, alongside strategic partners such as the EDM Council (now EDM Association), CrownPoint Technologies, and ACCURIDS. 

In parallel with the growth of the ontology, the project helped catalyze broader industry standardization efforts. In 2023, use cases and lessons learned in the IDMP Ontology project were included in the ISO Technical Specification (TS) 21405. This guidance for the representation, governance, and implementation of IDMP standards using IDMP ontology was published in March 2026. 

A shared industry asset

A major milestone was achieved in January 2024 with the release of Version 1.0 of the ontology under an open-source MIT license. Covering key concepts across all five ISO IDMP standards, Version 1.0 marked the transition from pilot development to production implementation and established the IDMP Ontology as a shared industry asset supporting regulatory compliance, data governance, interoperability, and digital transformation initiatives. 

The impact of the project was further recognized in 2024 when the IDMP Ontology received the Bio-IT World Innovative Practice Award, acknowledging its successful application and demonstrated value across multiple organizations. 

Since then, the IDMP Ontology has continued to evolve through regular community-driven releases, expanding support for regulatory reporting, jurisdiction-specific vocabularies, manufacturing and supply chain data, batch traceability, and enterprise product master data management. 

Today, the IDMP Ontology, together with HL7 FHIR messages, serves as a foundational implementation layer for ISO IDMP. As an open-source, vendor-neutral semantic framework, it enables consistent medicinal product data representation, cross-system interoperability, regulatory compliance, and AI-ready data ecosystems. Guided by an active Steering Committee and supported by a growing global community, the IDMP Ontology initiative continues to advance toward the vision of a shared digital language for medicinal product information across the healthcare and life sciences industries. 

What can you do with this?

If you’re a practitioner in regulatory data, semantic technologies, ontology engineering, or IT, the IDMP Ontology gives you an open, machine-readable starting point for ISO IDMP implementation instead of a blank page. It is a shared industry reference model you can use to resolve internal definitional inconsistencies, map and integrate your data, and build a semantic foundation. It’s free to browse, download, and evaluate. 

If you’re a leader in data strategy, R&D IT, or Regulatory Operations, the story here is not just about IDMP, but about building for the future. Adopting a shared, vendor-neutral model allows organizations to benefit from collective industry expertise while avoiding the cost of repeatedly solving the same integration challenges. Beyond supporting compliance and interoperability today, it creates the high-quality, standardized data foundation needed for tomorrow’s AI-enabled capabilities. 

Join the IDMP-O community

The IDMP Ontology is built and maintained by its community. Whether you’re already implementing ISO IDMP or just starting to explore it, there’s a place for you: browse and download the ontology, join a Community of Interest webinar, or get involved with the group of people actively implementing the ontology and learning from each other’s experiences. 

Consistent medicinal product data isn’t a competitive advantage to hoard. It’s a shared infrastructure that benefits the whole industry, built once, built together. 

Where to learn more

About the authors

This history reflects the collective work of the members of the IDMP Ontology group at the Pistoia Alliance and the Steering Committee guiding the evolution of the IDMP Ontology today. 

Written by Aditya Tyagi (Pistoia Alliance), Heiner Oberkampf (ACCURIDS), Sheila Elz (Boehringer Ingelheim) and Christian Baber (Pistoia Alliance) 

With special thanks to the participants, founders, and colleagues from Bayer, GSK, Merck KGaA, MSD, Novartis, Roche, Johnson & Johnson, Boehringer Ingelheim, Pfizer, AstraZeneca, AbbVie, Amgen, ACCURIDS, and EDMA. 

Join the conversation

A question we’d like your take on: 

If your organization has implemented ISO IDMP standards, what was the hardest part and would a shared semantic model have changed it? 

Connect with the IDMP Ontology team and share your thoughts. 

What does the FAIR data ecosystem look like in 2036?

Posted on by Helen Taylor

This is an article from the Pistoia Alliance FAIR for Pharma Community 

Ten years of FAIR. The picture is mixed. 

The original paper is among the most cited in scientific history. FAIR has entered EU policy. Pharma organizations have started their FAIR journeys and some are beginning to show real returns. 

And yet: FAIR silos, FAIR fatigue, Fake FAIR. Median organizational maturity sits at level 0–1 out of 4. Interoperability across organizations? Still largely undemonstrated. 

So what happens if we get the next ten years right? Imagine a life science ecosystem where your data shows up in a search, and someone else’s data shows up in yours. Where machine-actionable, interoperable data across organizations actually exists. Where FAIR is operational fabric, not a journey. 

In April 2026, at the Royal Society of Medicine in London, the Pistoia Alliance brought 36 experts from 23 organizations together to map what needs to change from here to there. Four groups, each representing the full diversity of the pharma ecosystem, independently mapped the actors, constraints, and enabling assets shaping FAIR adoption. Then they placed them on a shared board. 

The result was not a plan, not a roadmap. It was something more useful for independent but interconnected actors: a shared landscape. A map. And this article shares what emerged. 

Who should read this 

If you lead data strategy, R&D informatics, data governance, or digital innovation in pharma or life sciences, or if you advise those who do, this is a useful signal from the front lines of the FAIR ecosystem. 

It is also for anyone still wondering whether FAIR is a niche technical agenda. It is not. 

What this article is about 

Three things: first, why FAIR success at scale requires ecosystem-level action, not just organizational effort; second, where pre-competitive collaboration has the highest leverage right now; and third, what Bedrock Change actually looks like — and why the lever you should be pulling today is standards, not regulators. 

The landscape: three zones, one shared picture 

The workshop produced a shared effort-versus-time map. Four independent groups contributed 28 clusters across 11 innovation domains, falling into three zones. 

Zone 1: Act now. Low effort, short horizon. AI-driven FAIRification, FAIR training, and foundational literacy. Every group independently placed these early. The resources exist today. The bottleneck is not invention: it is awareness and adoption. 

Zone 2: Build together. The zone where coordinated community action delivers what no single organization can. Standards and shared vocabularies, skills and capability, funding and business case, procurement. This is where pre-competitive collaboration has the highest leverage. 

Zone 3: Bedrock Change. The deep systemic shifts that take longest but underpin everything else. Regulatory alignment, legislative reform, healthcare system integration, and the long-horizon challenge of making AI outputs themselves FAIR-compliant. No single actor moves this zone alone. But the work done in Zones 1 and 2 is what makes it possible. 

1. Zone 1 is not low-hanging fruit. It is putting yourself in a position to succeed. But you need to act now. 

If Zone 1 were truly easy, organizations would already be FAIR. Often they are not. 

Here is something that surprises some people: FAIR has 15 principles, not 4. Ask colleagues how many there are and you will almost certainly hear “four.” But the four letters cover 15 sub-principles, and the gap between the label and the substance is exactly where implementation fails. “My data is in a lake, it is findable” — a real statement, heard more than once. Or: “Our platform has a semantic layer, but no machines can act on it.” Both are signals of shallow understanding, and both are very common. 

As Anuj Uppal, VP Life Sciences Transformation at Campana Schott, put it: “The biggest obstacle to FAIR today is a shallow understanding of the principles. You can’t automate your way out of a definition problem.” 

Three things every organization can act on now, without waiting for anyone else. First: invest in learning. Build a shared understanding of what FAIR actually means, across your team, your leadership, and your vendor landscape. Hint: an AI mandate exists in almost every organization right now. That is a FAIR mandate in disguise, and the language of AI buyin can carry FAIR along with it. Second: know the recipe. Use the resources that already exist: the FAIR Maturity Matrix, the FAIR Toolkit, the Pistoia training program for example. Third: let machines help. AI tools for metadata extraction, maturity scoring, and FAIRification are available today and reduce the cost of getting started significantly. But they require a solid understanding of FAIR to use well. Measure where you are, then close the gap. 

2. Zone 2 is where community investment pays off, and the tools already exist. 

Standards clusters landed consistently in the collaborative, medium-effort zone: shared vocabularies, ontologies, common data models, FAIR procurement language. These are things the community can act on together, now. 

The Pistoia Alliance already has infrastructure in this zone. The FAIR Maturity Matrix lets organizations benchmark and track progress across seven dimensions. The FAIR Business Value Frame, currently in pilot with Pistoia members, quantifies the business case. The Pharma General Ontology builds the shared semantic layer that FAIR interoperability requires. The IDMP and CMC ontologies extend this to regulatory and manufacturing data. 

Not one of these tools was built by a single company. Every one is stronger because it is shared. As Ranu Sharma, Senior Data Scientist at AbbVie, put it: “These are innovations no single organization can reach alone, which is exactly why they belong to the community.” 

Regulatory clusters landed in Zone 3: not because they are unimportant, but because they require policy engagement and legislative timelines that move on their own. The strategic implication: invest now in what the community controls. Standards are the infrastructure for regulation. When requirements arrive, organizations that built on shared standards will be ready. Those who waited will retrofit under pressure. 

One more thing worth saying plainly: free riders do not get a seat at the table when rules are being defined. If standards are being built now, this is the moment to contribute. 

3. Zone 3: you cannot move the bedrock alone. But you can build what moves it. 

Zone 3 is where the really hard things live: regulatory alignment, legislative reform, healthcare system integration, legacy data at scale. These are not technical problems with technical solutions. They require engagement with regulators, health authorities, and legislators who move on their own timelines and respond to evidence, policy dialogue, and sustained community pressure — not product roadmaps. 

Every group independently placed these clusters here. Not because they are unimportant, but because no single organization controls them. The question is not whether to engage with Zone 3. It is how. 

The answer is simple, and it is the through-line of this entire landscape: we can’t move regulators. We can move standards. Pick the lever you actually control. 

Standards built in Zone 2 become the evidence base for Zone 3. When a regulator asks for FAIR-compliant submissions, organizations that have already adopted shared vocabularies, common data models, and interoperability standards will be ready to respond. Those who waited will scramble to comply under pressure. As Avinash Dixit put it: “Defining the standards today is going to lower the cost of compliance tomorrow.” 

Healthcare systems deserve a specific mention. They are complex, vary by country, and sit firmly in Zone 3. But smaller countries with more aligned national systems are already further ahead on the FAIR journey. They are proof that it is possible, and a source of lessons for the rest. When healthcare systems become FAIR-ready, real-world data flows back into clinical development, closing the loop and compounding the value of every FAIR investment made upstream. 

One more Zone 3 item worth naming explicitly: FAIR AI. Not AI as a tool to accelerate FAIRification — that belongs in Zone 1. This is something different: AI systems that themselves produce FAIR-compliant artefacts, and AI models that are themselves findable, accessible, interoperable, and reusable. Whether AI-generated data products can be trusted, traced, and reused with the same confidence as human-curated data is not yet resolved — technically, legally, or culturally. That resolution is genuine Bedrock Change, and building the governance frameworks, provenance standards, and regulatory dialogue to get there is work the community needs to start now. 

If you see a gap in this zone, the right response is not to wait for someone else to fix it. Speak up, identify collaborators, and start building from where you stand. No single person or company breaks into massive operational systems alone. But communities do. 

What you can do 

The most important message from this workshop: the choice is yours, but it is not cost-free. In the words of Avinash Dixit, VP Life Science Practice at Datavid: “FAIR now, or pay later. The cost of acting now is always lower than the cost of catching up later.” 

Three actions, one per zone: 

  1. Zone 1, Act now: Run the FAIR Maturity Matrix assessment on your organization this quarter. Not someday. This quarter. Start asking your vendors whether FAIR is on their product roadmap. Use AI to machine-check your data and establish a baseline. 
  1. Zone 2, Build together: Use the FAIR Business Value Frame to quantify your business case in language leadership understands. Embed FAIR language in your next procurement RFP. Join the Pistoia FAIR Community of Experts and contribute to shared infrastructure that no single R&D budget can build alone. 
  1. Zone 3, Think long: Connect your FAIR and AI governance agendas explicitly. The standards you invest in today lower your compliance burden when regulators catch up. Start the internal conversation now about which standards investments will matter most. We can’t move regulators. We can move standards. Pick the lever you can actually control. 

Where to learn more 

Explore the interactive FAIR Forward landscape map and see where your organization fits: 

👉 pistoiaalliance.org/resource-library/fair-forward-2026-ecosystem-landscape-map/ 

About the authors 

This article reflects the collective expertise of the FAIR Forward workshop presenters and the broader FAIR Community of Experts within the Pistoia Alliance. 

Authors: Ranu Sharma (AbbVie) · Anuj Uppal (Campana Schott) · Avinash Dixit (Datavid) · Birgit Meldal (Pfizer) · Giovanni Nisato (Pistoia Alliance) 

With thanks to the 36 participants of the FAIR Forward workshop and to the FAIR for Pharma Steering Group for their contributions, energy, and collective brain. 

Join the conversation

Where does your organization sit on the FAIR Forward landscape: picking up Zone 1 quick wins, building Zone 2 shared infrastructure, or wrestling with bedrock change challenges? 

The global landscape and regulations continue to evolve. Are you ready?

Posted on by Helen Taylor

This is an article from the Pistoia Alliance Controlled Substance Compliance & Shipping Community  

Today’s global landscape has made controlled substance compliance harder. In many organizations, only a few specialists, sometimes one person, must ensure that every controlled substance the company orders, makes, or ships complies with the laws of every country it touches. When they get it right, little happens. When they get it wrong, a shipment, site, or development program can stop for weeks. 

That responsibility is further challenged as regulators respond to rapidly evolving substances, technologies, global supply chains, and new routes to illicit markets. Broader controls are necessary to address emerging threats, but the ensuing complexity and ambiguity in the enforcement of regulations designed to curb and disrupt creative illicit-market channels also create barriers to legitimate pharmaceutical research and delay the delivery of medicines that patients need. 

In 2026, professionals handling controlled substance compliance and shipping face fast-moving, high-consequence decisions with limited resources to provide guidance to the business. We ask: How can we work effectively now to prepare for what comes next? The answer requires stronger connections among compliance, R&D, trade, logistics, and security learning from each other to prepare for now and the future. 

A widening rulebook. A shifting supply chain. And some of the smallest teams in the industry holding the line. 

These challenges will be at the center of the Pistoia Alliance’s 2nd European Controlled Substance Compliance & Shipping Conference, taking place on 29–30 September 2026 in Cork, Ireland. 

Three pressures are converging

Controlled substance compliance has always been demanding. Over the last two years, however, three pressures have intensified at the same time: broader and more complex rules, disrupted physical supply chains, and a persistent shortage of specialist resources. 

1. The rulebook keeps widening. With good reason.

Criminal networks continually develop synthetic compounds intended to evade existing controls. Regulators and law enforcement respond by using broader definitions that cover not only known substances but also related chemical families. The market then adapts, and the cycle repeats. 

As testament to this cycle, the European Drug Report 2026 recorded 50 new psychoactive substances reported for the first time in 2025. It also noted continued market adaptation, including new synthetic and semi-synthetic cannabinoids and opioids. For legitimate industry, the same broad controls intended to address these threats can extend into the chemical space used by pharmaceutical R&D. 

And the boundary can be narrow. A research compound may differ from a controlled analogue by a single atom. Misclassification can stop work, trigger destruction of material, or require a lengthy registration process. The central risk is therefore not paperwork alone, but timely and defensible interpretation. 

2. Compliance and supply-chain security are converging 

Geopolitical tension, trade-policy uncertainty, and rerouted logistics are changing where goods move and how long journeys take. These shifts require companies to reassess depots, carriers, routes, security controls, and the handoffs between internal teams and logistics partners. 

Criminal methods are changing as well. Cargo-theft intelligence describes increasingly strategic schemes involving impersonation, fraudulent documentation, fictitious pickups, and double brokering. Pharmaceuticals remain exposed, particularly where oversight is fragmented across compliance, shipping, security, and third-party providers. 

For controlled substances, these are not separate conversations. A compliant transaction can still fail if the carrier, route, documentation, or physical controls are weak. Organizations therefore need joint ownership of regulatory and logistics risk, with clear escalation points across compliance, trade, security, and 3PL/4PL partners. 

3. Small teams carry disproportionate consequences

A benchmark of Pistoia Alliance member companies reported approximately one trade compliance professional for every 700 to 1,000 employees, improving to about one per 300 in specialized global supply chain settings. Controlled substances may represent only part of a portfolio, but the specialists responsible for them still face expanding rules, cross-border complexity, and changing logistics risks. 

When an error occurs, be it misclassification, failed check, or stopped shipment, the initial disruption may last days, while investigation, corrective action, and regulatory follow-up can take weeks. The function may be small by headcount, but its consequences span the enterprise. 

The answer is prevention, not more documentation 

When pressure rises, the instinct is often to create more documentation. Yet most organizations already have substantial information in systems, spreadsheets, and shared drives. The opportunity is to use that information to direct attention: automate routine data flows, identify where risk is concentrating, and bring human judgment to exceptions.

Technology can multiply a small team’s capacity, but it cannot replace subject matter expertise or resolve ambiguous law on its own. The aim is to prevent the next disruption and remain inspection-ready across the lifecycle, from sourcing and research through shipping and disposal. 

Shared intelligence is the practical advantage 

These challenges span jurisdictions, business functions, data systems, and scientific disciplines. No organization can solve them efficiently in isolation, and specialists often have few peers with whom they can test an interpretation or compare practices. 

Peer exchange makes it possible to benchmark staffing and governance, compare approaches to automation, strengthen handoffs with logistics partners, and anticipate regulator expectations. The objective is not uniformity; it is faster learning and better prevention. 

What organizations can do now 

  • Treat classification as an interpretation risk, especially for novel compounds near a control boundary. 
  • Map handoffs among compliance, shipping, security, R&D, and logistics partners, then define ownership and escalation points. 
  • Use existing data to identify risk earlier rather than document problems after they occur. 
  • Resource controlled substance compliance according to consequence, not only portfolio size or transaction volume. 
  • Benchmark practices with peers before an inspection or incident exposes a gap. 

Join the conversation in Cork 

The 2nd European Controlled Substance Compliance & Shipping Conference will bring together professionals from compliance, trade, logistics, security, R&D, supply chain, and partner organizations on 29–30 September 2026 in Cork, Ireland. The program will focus on practical benchmarking, inspection readiness, import/export, R&D compliance, supply-chain intelligence, and the responsible use of technology and automation to manage risks. 

If your work touches a molecule regulated by any government, this is your conversation. Register today 

Where to learn more 

About the authors 

This article reflects the expertise of the Pistoia Alliance Controlled Substance Compliance & Shipping Community and its Steering Committee. The Pistoia Alliance is a global, not-for-profit organization that lowers barriers to innovation in life-science R&D through pre-competitive collaboration. 

Join the conversation 

Share your experiences in the comments! We’d be interested in knowing where the widening of controls cause you the most difficulty. Is it classification, partner management, or something else? 

The Blind Spots of Explainable AI 

Posted on by Helen Taylor

The Next Challenge Isn’t Explaining AI Decisions. It’s Designing AI’s Role in the Experience. 

The first 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.  

Stijn Janssen, Strategic Design Leader, Novo Nordisk 

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 is increasingly invisible. It “hides in plain sight,” appearing as a feature on SharePoint sites, in workspaces, and in the corners of digital applications, framed by organizations as a coworker or assistant. It is there, waiting to collaborate and help us with our Jobs To Be Done (JTBD). But do we really understand why it is there, or whether it is actually helping us become more productive and efficient? 

It’s one of the blind spots we have when it comes to Enterprise AI implementation and adoption. We embrace new technology by allowing everyone to build and use tools that make our lives easier. At the same time, we are adding more complexity to our digital ecosystem, infrastructure, and user journeys as they become increasingly tailored and personalized. A general way of explaining the purpose, behavior and intended use becomes a big challenge because there is no transparency or consistency. Two roles can use the same agent for different purposes and therefore need different explanations. 

Contradictions create blind spots 

As we democratize AI across the organization, everyone has the power to create their own assistant to support their way of working. At the same time we are mining processes to understand how they are followed as described in the standards and identify deviations and bottlenecks so we can decide what AI solutions we can build to help us work more efficiently. 

We now have grassroots-built agents that are used by individuals (e.g., a CRA builds a solution to help generate monitoring reports, a medical writer uses a tool for Q&A documents) and centrally developed solutions we want (user) groups to work with without understanding if there is overlap and if the solution is understood the same way or explained in different ways. The result is a fragmented landscape of people using their own AI solutions, centrally offered solutions or none of the above. That does not eliminate friction; it merely relocates it to the interaction between human expectations and AI autonomy. 

This illustration shows how key roles in life sciences are using multiple agents  serving multiple roles/personas with different skills, workflows, and purposes. 

Explaining is designing 

There is a fundamental shift we need to make to create consistent experiences as we are moving to a world where agents are not only positioned as our coworkers but act as our colleagues because they participate autonomously in experiences, much like humans do. It means we need to focus not only on how we instruct AI, but also on how we define and design the behaviors, expectations, roles, and responsibilities it should have. 

We need to answer the question: Which jobs belong to humans, and which belong to AI? As AI owns part of the journey, what are its goals and boundaries? Designers have always been good at uncovering these questions by mapping out journeys or service blueprints yet these maps rarely include AI as an actor, so designers should claim their position in designing for this explainability by defining the behavior, principles and guidelines that set the standards for working with AI. 

The future of experience design is not just human-centered; it’s about orchestrating understanding. Aspen Snow takes up this theme later in this series in Finding Our Place in the Age of AI: The Future UX Researcher, calling for participation and collaboration between humans and AI in ways that are consistent, trustworthy, ethical, and intentional. Explaining UX and AX (Agent Experience: orchestrating the technical setup and infrastructure) separately is not going to cut it. We should instead think about a new discipline: Actor Experience, the practice of designing how human and AI actors collaborate, interact, and share responsibility to achieve consistent outcomes. 

As Peter Hummel will argue in the article that closes this series, Start with the Workflow, Not the AI: “Scientists need to understand what the AI is doing, where its suggestions come from, when to rely on it, and when to challenge it.” It is no longer sufficient to explain how AI works. We need to explain what role AI plays within the experience. 

The digital evolution 

When I explain my role in digital transformation, I always start by saying it is not a transformation; it is an evolution and therefore, we need to look ahead to keep up with AI developments and prepare for the next shifts in focus:  

  • focus on capabilities 
  • then on workflows 
  • and the next focus will be on relationships. 

Not simply the relationship between people and technology, but the relationship between people and AI actors within the same ecosystem. 

Explainability will always matter (as described in this NN/g article) because every participant in the journey needs a shared understanding of what the AI is there to do, what decisions it owns, where its authority ends and how it contributes to the outcome. 

But perhaps the future isn’t about better explanations; it’s about giving AI a clear role in the experience. 

Continue the conversation in October 

Stijn is speaking at the 2026 UX for Life Sciences Conference, 20–21 October, hosted by GSK in Stevenage, UK. His session, “Implementing a Digital Operating Model in R&D: Connecting Strategy, Portfolio, and Evidence Across the Lifecycle,” is where this article goes operational: designing how human and AI actors share responsibility in an experience, builds on the operating model that makes that coordination real across R&D. 

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. 

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. Stijn was joined by UX leads from Novartis, Merck/MSD 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 published 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 

In your organization, when someone spins up their own AI agent, who decides what that agent is allowed to own, and does anyone else know it exists? 

Be specific if you can. Do you have real examples of overlapping or invisible agents? 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 1 of three articles from the UXLS Community on how AI is redefining user experience in life sciences. Two more follow. In Part 2 next week, Aspen Snow (Merck Sharp & Dohme LLC), explores how AI has shifted the value of UX researchers from producing understanding to orchestrating it. In two weeks, Peter Hummel (Novartis Biomedical Research) shares in Part 3 how AI opportunities are found by mapping workflow friction, not by looking for places to apply AI. 

UX in Life Sciences: What’s Actually Different — and Why It Matters

Posted on by Helen Taylor

This is part 1 of a 3-part series on UX in Life Sciences from the Pistoia Alliance UXLS community.  

If you’re coming from traditional UX into life sciences, you may think you already understand the rules of the game. But here’s the reality: in this industry, the software is not the product — a life-changing therapy is.  

That shift changes everything.  

From longer timelines and regulatory complexity to constrained research access and fragmented organizational structures, UX in life sciences operates in a fundamentally different environment. Yet as digital transformation and AI adoption accelerate, the role of UX is becoming more strategic than ever. 

What is the article about? 

This is the first in a 3-part series from the Pistoia Alliance UX in Life Sciences (UXLS) community exploring how UX operates in one of the most complex and regulated industries.  

In this article, we focus on what makes UX in life sciences fundamentally different from other sectors and why those differences matter. We explore how regulatory environments, scientific workflows, and long development cycles reshape UX practice, from research and design to measurement and impact. We also highlight the structural challenges practitioners face, including small teams, organizational change, and industry-wide isolation. 

Who should read this?  

This series is for UX practitioners, designers, researchers, and digital leaders working in or considering moving into life sciences. It is especially relevant for those navigating complex, regulated environments, as well as leaders responsible for digital transformation, R&D systems, or AI adoption.  

If you’re interested in how UX operates when the stakes are higher, timelines are longer, and impact reaches patients, this series is for you.  

UX in Life Sciences: What’s Actually Different — and Why It Matters  

If you move from general software UX into life sciences, your first surprise might be this: “Product” doesn’t mean the software. In life sciences, the drug is the product. The software is the enablement tool that helps create it.  

That shift sounds simple, but it changes almost everything about how UX operates.  

In most technology environments, UX shapes the core product experience. Roadmaps are driven by growth and engagement. Iteration cycles are short. Feedback loops are tight. In life sciences, timelines stretch across years. Regulatory frameworks are layered and unavoidable. Decisions involve scientists, clinicians, regulatory specialists, IT, legal, and leadership long before a screen is ever designed.  

UX here is less about shipping features quickly and more about shaping complex systems responsibly. That means reducing cognitive load in specialized scientific workflows, designing for auditability and traceability, and connecting digital metrics to pipeline progress or patient outcomes rather than clicks and completion rates.  

When the true product is a therapy that may take a decade to reach patients, digital misalignment is not just inconvenient. It is expensive, risky, and slow to correct.  

The cost of building solutions before understanding problems  

That cost becomes most visible when UX is treated as a downstream activity rather than a strategic one. In life sciences, where lifecycles are long and regulatory stakes amplify early misalignment, the difference between a project mindset and a product mindset is not academic. It is a matter of risk management.  

Learn more about that mindset shift in part 2 of this series.

Research under constraint  

In many industries, recruiting users for research is relatively straightforward. In life sciences, engaging patients, healthcare professionals, or even internal scientific users can involve compliance reviews, layered approvals, and legal oversight. The domains are highly specialized, the tools often carry years of technical debt, and knowledge sharing across companies is constrained by NDAs.  

For practitioners entering the field, this can feel restrictive. A researcher accustomed to scheduling ten user interviews in a week may spend that same week navigating a single approval chain.  

Another adjustment is the depth of the domain itself.  Existing UX skills remain essential, but life sciences has its own language, processes, and scientific context.  Learning the vernacular and understanding the workflows takes time, and newcomers often need a longer ramp-up period before they can fully operate with confidence.  

But that friction raises the bar. It demands sharper problem framing, knowing exactly which questions matter most before you get limited access to users. It demands clearer articulation of value, because every study must justify its overhead. And it produces more durable outcomes. The friction is real. So is the opportunity to build credibility through rigor. 

Small teams, shifting structures  

UX teams in life sciences are frequently small relative to the enterprises they support. Some are centralized, some embedded, many in hybrid models that evolve during restructures. And restructures are common. Reporting lines shift. Priorities change. Budgets tighten.  

Practitioners learn to operate through influence rather than authority, adapting to changing contexts while maintaining continuity in user-centered practice. Without shared standards and shared language, UX maturity can reset every time the org chart changes, with years of institutional knowledge and hard-won credibility evaporating in a single reorganization.  

The hidden cost of isolation  

That fragility is amplified by isolation. Across organizations, practitioners describe remarkably similar experiences: reinventing templates independently, struggling to benchmark maturity, rebuilding momentum after every restructuring, all behind separate walls. Without a sustained community connecting practitioners across organizations, the lessons learned in one team rarely reach another.  

Life sciences has a long tradition of pre-competitive collaboration to solve exactly these kinds of shared structural challenges. Communities of practice are how that tradition is extending into UX.  

Part 3 of this series explores how communities of practice are taking shape across the industry.

A field worth entering, and a community ready for you  

The work can feel slower, more bureaucratic, and demanding of patience and political navigation. Yet it places you at the intersection of science, data, and human experience in ways few industries do. The problems are systemic. The consequences are real. And at the end of the chain, the workflows you improve help bring therapies to patients faster and more reliably. Not many UX roles let you draw a line from your work to someone’s health.  

The UX in Life Sciences community within the Pistoia Alliance exists to support this evolution, connecting practitioners across member organizations to exchange frameworks, share lessons, and strengthen the strategic role of UX.  

If your organization is already part of the Pistoia Alliance and you are not yet connected to the UXLS community, this is an invitation to join. If your organization is not yet part of Pistoia, that conversation is worth starting. The Alliance supports challenges across data, interoperability, AI, and scientific collaboration well beyond design.  

Joining the Pistoia Alliance is a good place to begin.

If you are outside the industry and curious, the field needs practitioners who understand complexity, not just craft.  

UX in life sciences is demanding. It is also becoming more strategic with every digital transformation initiative and every AI investment that depends on human trust and adoption. The practitioners shaping this field do not have to do so in isolation.  

What can you do with this?  

If you work in UX within life sciences, take a step back and assess where your organization sits: is UX being applied late in delivery, or is it shaping strategy early? The earlier UX is embedded, the greater its impact on reducing risk, aligning stakeholders, and accelerating outcomes.  

If you are new to the industry, recognize that success here depends not just on craft, but on navigating complexity, building domain understanding, and influencing across disciplines.  

What’s next in this series  

In Part 2, we explore how the differences we’ve discussed fundamentally change the way UX teams need to operate, and why a mindset shift is critical in life sciences.

Join the UX in Life Sciences (UXLS) community  

The UX in Life Sciences (UXLS) community at the Pistoia Alliance brings together practitioners across organizations to share frameworks, exchange lessons learned, and strengthen the role of UX in a complex and evolving industry.  

If your organization is already a member of the Pistoia Alliance, we invite you to get involved in UXLS. If not, this is an opportunity to explore how the Alliance supports collaboration across data, AI, interoperability, and digital transformation in life sciences. 

Where to learn more  

The UXLS Community of Experts

Joining the Pistoia Alliance

About the authors  

This article reflects the collective expertise of members of the UX in Life Sciences (UXLS) community within the Pistoia Alliance.  

The main contributors were Jing Zhang, PhD Jing Z. · Brian Mila · Nicola Marcon · Peter Hummel · Sven Neumeyer · Voula Gkatzidou, PhD · Breac Baker Breac B. · Peter Horvath · Sudha Y. · Farah Egby  

With thanks to our Steering Committee — Sven Neumeyer (Novartis) · Simon Fortenbacher (GSK)· David Pace (Merck) — and to all members of the UXLS Best Practices workstream for their contributions, ideas, and energy throughout the year. 

From Projects to Products: Why UX in Life Sciences Needs a Mindset Shift

Posted on by Helen Taylor

This is part 2 of a 3-part series on UX in Life Sciences from the Pistoia Alliance UXLS community.

If you’ve spent time in life sciences UX, you’ve likely experienced it: a project lands, you’re brought in late, you do what you can, it ships. And then the team moves on. The tool you helped design may stay in use for years, but nobody’s watching how it performs, and nobody’s asked to improve it. That’s the project mindset in action.  

The shift to a product mindset changes all of that.  

In an industry where digital tools touch scientific workflows, data integrity, regulatory compliance, and ultimately patient outcomes, whether UX is treated as a project deliverable or a product capability is not a semantic difference. It determines how early you’re involved, how success is defined, and whether the value of UX compounds over time or resets with every reorganization.  

What is the article about?  

This is the second in a 3-part series from the Pistoia Alliance UX in Life Sciences (UXLS) community exploring how UX operates in one of the most complex and regulated industries.  

In part 1, we discussed how regulatory environments, scientific workflows, and long development cycles reshape UX practice in the Life Sciences and highlight the structural challenges practitioners face.

In this article, we examine the shift from project-driven to product-driven UX and why it matters in life sciences. We explore what the traditional project model looks like in practice, how the life sciences environment amplifies the need for change, and what a product mindset concretely enables: earlier strategic involvement, continuous measurement, and sustained UX presence across the product lifecycle. We also offer a practical set of checkpoints for practitioners navigating this shift in their own organizations.  

Who should read this?  

This series is for UX practitioners, designers, researchers, and digital leaders working in or considering moving into life sciences. It is especially relevant for those advocating for earlier UX involvement, clearer outcome measurement, or stronger UX presence within product teams.  

If you are a digital or R&D leader responsible for transformation programs or AI adoption, this article offers a clear articulation of why the conditions under which UX operates directly affect the quality and sustainability of your digital systems. 

From Projects to Products: Why UX in Life Sciences Needs a Mindset Shift  

Across pharmaceutical and life sciences organizations, a significant shift is underway. Teams that once operated through short, linear projects are increasingly moving toward long‑lived, continuously evolving products. For User Experience (UX) practitioners, this shift is far more than operational vocabulary. It fundamentally changes when we are involved, how we work, and how user value is defined, measured, and sustained.  

And in life sciences where digital tools influence scientific workflows, data quality, compliance, and ultimately patient outcomes, the difference between a project mindset and a product mindset is magnified. 

The Project Model Most of Us Know  

Traditionally, life sciences UX has grown up in a project-driven environment:  

  • Projects have a fixed start and end, often constrained to weeks or months, and are milestone driven.  
  • UX is brought in late, forcing the work to become reactive.  
  • UX is minimized to User Interface (UI) work or “Making things look nice”.  
  • Measurement often stops at go‑live, leaving no visibility into whether user behavior changes or whether the solution delivers meaningful value. 

In this model, teams unintentionally build feature factories, where success is judged by output volume rather than outcome achieved.  

In fast consumer tech, this inefficiency is annoying. In life sciences, it can be costly, risky, and long‑lasting.  

Why Life Sciences Amplifies the Need for Change  

Life sciences digital systems rarely stand alone. They operate within complex scientific and operational ecosystems, embedded across drug discovery and experimental workflows, supporting lab operations and data capture, enabling clinical development, underpinning quality, safety, and regulatory environments, extending into global manufacturing and supply systems, and ultimately engaging with patients and healthcare professionals.  

These processes are interconnected, data‑dependent, and heavily governed. A misalignment introduced early can ripple across years of scientific and operational activity.  

That is why organizations are increasingly adopting a product mindset, because scientific, digital, and data systems cannot be improved meaningfully through short-term, isolated bursts of delivery.  

What a Product Mindset Enables  

A product mindset reshapes three foundational aspects of UX in life sciences.  

1. UX Starts Earlier  

With a product approach, UX is brought into the conversation before a solution is predefined. Teams take time to frame the problem:  

  • What scientific workflow are we trying to improve?  
  • What pain points or cognitive burdens do users experience?  
  • What scientific or business impact are we targeting?  
  • How will we know if we achieved it?  

This early strategic framing allows organizations to avoid misalignment that is costly to fix later. 

2. Success Is Measured Continuously  

Products are evaluated not just at launch but by how they perform and deliver value over time, with teams continuously monitoring usability, adoption, satisfaction, and workflow impact through telemetry and usage analytics, standardized measures like the User Experience Questionnaire (UEQ), ongoing qualitative feedback, and trend analysis gathered over months and years.  

Instead of a single point-in-time snapshot, UX becomes a continuous, evidence-based practice.  

3. UX Stays Embedded  

Product teams keep UX involved for the long haul, which allows practitioners to build deep domain understanding, track evolving workflows, support iterative improvements, maintain consistency across releases, and reduce long‑term design and technical debt.  

The Defining Difference Between Projects and Products  

Projects optimize delivery. Products optimize long-term value.  

A project mindset keeps UX in a reactive posture. A product mindset elevates UX into a strategic role. 

This shift is not theoretical. It improves the quality, safety, and sustainability of digital systems used across life sciences. As a result of that, UX contributes to long-term product health, critical in large organizations where structures and priorities frequently shift.  

For UX Practitioners Navigating the Shift  

Moving from projects to products isn’t like turning on a switch. Adopting product terminology doesn’t mean true product thinking is in place. The real question is whether the conditions for product‑driven UX actually exist. Use this as a practical checkpoint to guide your work and influence your teams:  

  • Are you involved before solutions are defined, helping frame the problem rather than reacting to it?  
  • Do you stay engaged across the product lifecycle, shaping direction and what matters, not just polishing the UI?  
  • Is user experience evaluated continuously through ongoing feedback loops, not only just at a point in time?  
  • Does the team optimize value, not delivery, focusing on solving the right problems rather than shipping the most features?  
  • Are success metrics tied to real user, scientific, and business outcomes, not just delivery milestones?  

If the answer to several of these is “no,” then the organization is still operating in project logic, and that’s your signal to advocate for earlier involvement, clearer outcomes, and stronger UX presence throughout the lifecycle.  

The organizational reality  

Life sciences organizations are large, complex, and constantly evolving. With every restructuring and organizational change, UX maturity can easily slip backward if teams lack shared standards and a common language. This is where communities of practice (CoPs) become essential. They preserve institutional knowledge, promote consistent methods, strengthen collective understanding of UX’s value, and connect practitioners who might otherwise work in isolation. Most importantly, they provide the continuity needed to sustain product thinking through ongoing organizational change.  

In the third article of this series, we explore how UX CoPs help break down silos, accelerate learning, and strengthen UX’s strategic role across R&D, digital, data, and IT in Life Sciences.

What can you do with this?  

If you work in UX within life sciences, use the practitioner checklist in this article as a starting point for an honest conversation with your team or stakeholders. Not all five conditions will be in place. But that’s not a failure. It’s a map. Identify where the gaps are, name them clearly, and begin advocating for the specific conditions that would shift your practice from reactive delivery to strategic contribution.  

If you lead a digital, R&D, or data function, ask yourself whether the UX embedded in your organization operates with the right mandate, timing, and continuity. The sustainability of your digital systems depends on it. 

What’s coming next in this series  

In Part 3, we explore how communities of practice are forming across life sciences organizations to sustain UX maturity, share knowledge, and reduce the isolation that so many practitioners experience, even as structures shift around them.

Join the UX in Life Sciences (UXLS) community  

The UX in Life Sciences (UXLS) community at the Pistoia Alliance brings together practitioners across organizations to share frameworks, exchange lessons learned, and strengthen the role of UX in a complex and evolving industry.  

If your organization is already a member of the Pistoia Alliance, we invite you to get involved in UXLS. If not, this is an opportunity to explore how the Pistoia Alliance supports collaboration across data, AI, interoperability, and digital transformation in life sciences. 

Where to learn more  

The UXLS Community of Experts

Joining the Pistoia Alliance

About the authors  

This article reflects the collective expertise of members of the UX in Life Sciences (UXLS) community within the Pistoia Alliance.  

The main contributors were Jing Zhang, PhD Jing Z. · Sven Neumeyer · Peter Hummel · Brian Mila · Nicola Marcon · Voula Gkatzidou, PhD · Breac Baker Breac B. · Sudha Yerramilli Sudha Y. · Peter Horvath · Farah Egby  

A special thanks goes to our Steering Committee, Sven Neumeyer (Novartis) · Simon Fortenbacher (GSK) · David Pace (Merck), and to all members of the UXLS Best Practices workstream for their contributions, ideas, and energy throughout the year. 

Beyond Silos: The Power of Communities of Practice in Pharma

Posted on by Helen Taylor

This is part 3 of a 3-part series on UX in Life Sciences from the Pistoia Alliance UXLS community.  

If you work in UX in pharma or life sciences, you might be familiar with the feeling of working in silos. User Experience specialists operate across diverse domains and business functions, navigating competing priorities that fragment teams and separate functions. As a result, efforts are duplicated, insights are scattered, and leadership doesn’t see the collective value that UX brings.  

Invariably, fragmented pharma organizations leave UX potential untapped, and their practitioners isolated without the power to deliver real strategic impact.  

But fragmentation isn’t inevitable. Communities of Practice offer a different path.  

What is the article about  

This is the third and final article in a 3-part series from the Pistoia Alliance UX in Life Sciences (UXLS) community exploring how UX operates in one of the most complex and regulated industries.  

In this article, we explore how Communities of Practice (CoPs) help UX practitioners break down silos, accelerate learning, and strengthen UX’s strategic role across R&D, data, and IT in life sciences. Drawing on workshops, interviews, and surveys, we examine what makes CoPs thrive and what derails them, trace their journey from informal gatherings to a sustained community, and outline the tangible value CoPs create for practitioners and organizations alike.  

Who should read this  

This series is for UX practitioners, designers, researchers, and digital leaders working in or considering moving into life sciences. This final article is especially relevant for those who experience isolation in their practice, those interested in building or sustaining a community, and leaders who want to strengthen UX maturity across their organization. 

If you are navigating organizational change, looking to reduce fragmentation, or wondering how to preserve UX momentum through restructuring, this article is for you.  

If you haven’t read the earlier articles in this series yet, you can find them here:  

Part 1 – UX in Life Sciences: What’s Actually Different — and Why It Matters

Part 2 – From Projects to Products: Why UX in Life Sciences Needs a Mindset Shift  

Beyond Silos: The Power of Communities of Practice in Pharma

Communities of Practice as the Bridge  

A Community of Practice does something simple but powerful: it brings UX practitioners together across teams and silos. They share knowledge, support each other, and collectively elevate the visibility, maturity, and strategic impact of UX across the organization.  

Through workshops, interviews, and surveys across the UX for Life Science network, we examined how organizations design, govern, and sustain CoPs, uncovering practical guidance grounded in real successes and failures.  

What Makes CoPs Thrive and What Derails Them  

So, what separates thriving CoPs from those that stall? Looking across organizations, we see patterns emerge — both in what enables communities to flourish and what tends to derail them. 

Success isn’t measured by headcount or calendar invites. Rather, thriving CoPs share five core ingredients:  

  • Engagement quality: depth and richness of interactions over passive attendance  
  • Diverse representation: UX practitioners from multiple business areas, roles, and experience levels  
  • Community focus: bringing UXers together with shared purpose  
  • Knowledge generation: the ability to create and disseminate standards and best practices  
  • Value realization with sustainability: tangible member benefits paired with governance structures that weather organizational change  

Yet even well-intentioned communities stumble. Our research uncovered five common pitfalls: 

  • Lack of ownership and resources: CoPs drift without clear champions and structural support  
  • Absence of formal recognition: informal operations lack the credibility to survive restructures 
  • Engagement fatigue: unclear value, repetitive formats, or weak incentives drain participation  
  • Shifting priorities: organizational changes pull focus away  
  • Wrong metrics or too much overhead: chasing vanity metrics and bureaucratic governance instead of impact and fun  

Getting started requires deliberate action:  

  • Start small and focused with a core group sharing clear common goals  
  • Secure buy-in early by articulating the broader mission and building a business case for leadership  
  • Create structure and space through dedicated channels, regular cadence, and consistent accountability  
  • Lower barriers to participation by sharing tools, examples, and success stories  
  • Scale intentionally, reinforcing common ground as you grow 

Figure 1: A framework to define the success, failure, and initiation of a Community of Practice. For more a more detailed description, consult our Guide to setting up a Community of Practice. 

The Journey: From Spark to a Sustainable Community  

Building a UX Community of Practice (CoP) is a gradual evolution from informal gatherings to an embedded cultural force. It begins with a few passionate UXers sharing ideas beyond projects, then grows as participation expands across departments. With structure and governance, engagement diversifies through discussions, polls, and collaborative projects. Over time, sustaining momentum becomes crucial, roles formalize, and impact becomes measurable. As success scales, focus must sharpen to avoid overload. Ultimately, a mature CoP becomes part of organizational infrastructure, shaping how UX is practiced, shared, and valued as a lasting platform connecting people, improving processes, and advancing UX maturity across the enterprise. 

Why It Matters: The Untapped Impact of CoPs  

CoPs create value across different roles and organizational levels: from peer learning for junior practitioners to strategic platforms for leaders, and silos breaking down across organizations. This manifests in four key ways: 

  • Fosters belonging; CoPs act as creative and professional associations within the organization, providing encouragement, feedback, and a sense of belonging, identity and shared culture for UX practitioners.  
  • Breaks down silos; by connecting practitioners across teams and functions, CoPs transform isolated efforts into a visible, collaborative network that elevates UX’s strategic influence and visibility. 
  • Accelerates growth; members benefit from shared tools, frameworks, and peer learnings that can be directly applied to their work and career development. 
  • Drives efficiency; CoPs help organizations avoid reinventing the wheel by sharing methodologies and lessons learned, leading to greater efficiency and innovation.  

The common thread? CoPs reduce fragmentation and enable UX practitioners at every level to connect, learn, and collectively amplify their impact.  

Join the Conversation  

Whether you’re building a CoP from scratch, sustaining one through a difficult transition, or scaling one that’s found its rhythm, the patterns are clear. Connection matters. Community matters.  

Ready to dive deeper? The Pistoia Alliance UXLS Best Practices workstream has developed practical resources to support this journey. Explore the full resources and detailed frameworks here.  

We’d love to hear your story, what’s working in your CoP, or what challenge are you facing? Share your experience in the comments or reach out to us; the collective wisdom of UX for Life Science practitioners strengthens us all.  

What can you do with this?  

If you are a UX practitioner working in life sciences, reflect on whether your organization has an active community of practice. If not, consider whether you might be the person to start one. Even a small group of practitioners sharing challenges and approaches can begin to shift the dynamic from isolated to connected.  

If you lead a digital, R&D, data, or IT function, ask yourself how your UX practitioners are currently connected across your organization. Communities of practice are a low-cost, high-return investment in the consistency and sustainability of your digital systems.  

Closing the series  

This is the final article in our 3-part series on UX in Life Sciences. Together, these three pieces trace the full arc from understanding what makes life sciences UX different, to the mindset shift required to operate effectively within it, and lastly, the communities that sustain that practice over time.  

If you missed the earlier articles, you can read them here:  

Part 1 – UX in Life Sciences: What’s Actually Different — and Why It Matters

Part 2 – From Projects to Products: Why UX in Life Sciences Needs a Mindset Shift

Join the UX in Life Sciences (UXLS) community  

The UX in Life Sciences (UXLS) community at the Pistoia Alliance brings together practitioners across organizations to share frameworks, exchange lessons learned and strengthen the role of UX in a complex and evolving industry.  

If your organization is already a member of the Pistoia Alliance, we invite you to get involved in UXLS. If not, this is an opportunity to explore how the Pistoia Alliance supports collaboration across data, AI, interoperability, and digital transformation in life sciences.  

Where to learn more  

The UXLS Community of Experts

Joining the Pistoia Alliance

About the authors  

This article reflects the collective expertise of members of the UX in Life Sciences (UXLS) community within the Pistoia Alliance. 

The main contributors were Jing Zhang, PhD Jing Z. · Sven Neumeyer · Peter Hummel · Brian Mila · Nicola Marcon · Voula Gkatzidou, PhD · Breac Baker Breac B. · Sudha Yerramilli Sudha Y. · Peter Horvath · Farah Egby  

A special thanks goes to our Steering Committee — Sven Neumeyer (Novartis) · Simon Fortenbacher (GSK) · David Pace (Merck) — and to all members of the UXLS Best Practices workstream for their contributions, ideas, and energy throughout the year. 

Pistoia Alliance Releases Version 1.7 of the IDMP Ontology 

Posted on by Helen Taylor

The Pistoia Alliance today announced the release of Version 1.7 of the IDMP Ontology, the open, machine-readable framework that turns the ISO IDMP standards into a shared digital language for describing medicinal products consistently across systems, companies and jurisdictions. 

The headline of this release is structured packaging. Version 1.7 introduces pack size representation as the total number of units in a package, expressed per unit of presentation, following EMA and ISO guidance. Composite packs are captured one component at a time, and combination products such as powder-and-solvent packs that require reconstitution are recognized automatically from their pharmaceutical dose form. Built-in validation rules derive pack sizes from a product’s constituents and confirm that the values hold together, so packaging data is not just recorded but checked. 

The release also adds the units of presentation vial, capsule and syringe mapped to the corresponding EMA SPOR reference data, so that product descriptions line up with the reference terminology regulators and industry already use. These additions make it easier to represent real-world packaging accurately and to compare it across sources. 

Every change in Version 1.7 traces back to a specific request from the group of experts who review and inform the ontology. Therefore, each release reflects real implementation needs. Version 1.7 also completes a technical migration of the ontology’s source files to the Turtle format, introduces a single merged release file for easier consumption, and ships quality reports alongside the ontology. 

“Every release of the IDMP Ontology is shaped by organizations who use it. Version 1.7 focuses on packaging because that is what our community of experts told us they needed next,” explains Aditya Tyagi, Project Manager of the Pistoia Alliance IDMP Ontology. “That is the pattern: the industry brings real problems, and the ontology evolves to solve them. If you work with medicinal product data, now is the time to be a part of what we build next.” 

Version 1.7 continues the steady, quarterly evolution of the IDMP Ontology, with each release adding the capabilities the industry has asked for. The improvements introduced are relevant to everyone working with medicinal product data. 

Pharma and life sciences organizations interested in staying abreast of developments of the IDMP Ontology can join our Community of Interest (CoI) to follow releases, learn from peers, and inform industry requests.  

Watch the recording of our latest CoI meeting and find out more on the IDMP Ontology webpage.  

Alternatively, contact idmpo@pistoiaalliance.org to engage directly in the ontology’s evolution and maintenance.

The IDMP Ontology Explained

Posted on by Helen Taylor

Despite the ISO IDMP standards, the same medicine is often described differently. This article explains the IDMP Ontology, how it aligns product data across systems and organizations, and how to get access. 

This article is based on the IDMP Ontology Community of Interest webinar hosted by the Pistoia Alliance on 1 July 2026. 

Regulators are asking the pharmaceutical industry to describe its products in one common, standardized way. The ISO IDMP standards define what information to capture. However, not everyone interprets these standards the same way, so two companies can follow the same rules and still describe the same medicine differently. Closing that gap is what the IDMP Ontology was built to do.  

Here’s what it is, how it works, and how to get it. 

What this article is about 

This is an introduction to the IDMP Ontology from the IDMP Ontology team at the Pistoia Alliance, written for anyone who has come across the term but never had it explained plainly. 

We cover what an ontology actually is, what the IDMP Ontology is and how it works, using the alignment of company data with the EMA’s Product Management Service (PMS) as a worked example. We explain why it was built collectively rather than by any single enterprise, where the project is heading, and how you can get access to it. 

Who should read this

This article is for anyone who works with medicinal product data: regulatory affairs and regulatory information management professionals, master data and data governance owners, IT and data architects, standards and ontology specialists, and regulators themselves. 

If you have heard of ISO IDMP, EMA PMS, or the IDMP Ontology and want to understand what they actually mean in practice and what to do about them, this article is for you. 

The IDMP Ontology Explained: One Shared Language for Medicinal Product Data  

A standard everyone reads differently 

the ISO IDMP standards (ISO 11238, 11239, 11240, 11615 and 11616) are a set of five international standards that set out the information needed to identify a medicine precisely: its substances, dose forms, units of measurement, packaging and the product itself. As authorities like the European Medicines Agency (EMA) roll these out, companies must submit and maintain their product data in this standardized structure. The catch: the standards specify what information to exchange and how that information is interconnected, but there is room for interpretation. Left to fill in the details themselves, organisations implement the standards differently. As a result, data ends up in silos: hard to join up inside a company, and harder still to reconcile with regulatory agencies. 

What is the IDMP Ontology? 

Let’s start with the word. An ontology is simply an agreed set of terms and the relationships between them written down so that people and computers can use them. A plain dictionary tells you what a word means on its own; an ontology also captures how things connect. Things like, which substance sits inside which product, which pack size comes in which packaging. So, the meaning emerges from the whole rather than single terms.  

The IDMP Ontology applies that idea to medicinal product data. It is an open, vendor-neutral, machine-readable model of the ISO IDMP standards. Every concept in the ontology carries one precise, ISO-referenced definition, plus the relationships that link them. Alongside HL7 FHIR, the widely used standard for exchanging health data between computer systems, it forms the implementation layer for ISO IDMP standards. It is a practical bridge between a standard written on paper and real data moving between real systems. And because the data structure is machine-readable, software, and increasingly AI agents, inherit meaning and context directly from the framework. 

One example of how it works: aligning your data with the EMA  

Take the EMA Product Management System (PMS) as a use case. Your product data lives in two places: you have data in your internal system and in the EMA’s PMS. Keeping those aligned as they drift apart over time is time and cost consuming. But if you tag both data sets to the same IDMP concepts as defined by the IDMP standards, the records can be lined up product record to product record, property entry to property entry. Then, differences that were previously invisible surface immediately. You can realign them and keep the two data sites in sync. That tagging is based on the shared understanding and digital language of the IDMP Ontology, allowing you to describe medicinal products consistently across systems, organizations, and borders. 

And the EMA’s implementation of the PMS is only the start. Other regulators are moving in the same direction. Map once to a shared language like the IDMP Ontology, and each new authority becomes a configuration on that framework rather than a new project.  

The value also doesn’t stop at compliance 

The same backbone makes medicinal product data interoperable and reusable across regulatory, manufacturing, pharmacovigilance, supply chain and all other functions within an enterprise, turning a regulatory obligation into an AI-ready enterprise data asset. 

A collective solution. An aligned industry. 

The IDMP Ontology isn’t any one company’s tool. It grew out of pioneering ontology work in 2020 and launched as a pre-competitive Pistoia Alliance initiative after a workshop in late 2021. The team delivered its first working model in 2022, and Version 1.0 of the ontology followed in January 2024 under an open-source MIT licence, covering key concepts across all five ISO IDMP standards.  

The ontology has been funded and further developed by a broad group of pharmaceutical companies, including Bayer, Novartis, GSK, Roche, Merck KGaA, Boehringer Ingelheim, Johnson & Johnson, AstraZeneca, Amgen, AbbVie, MSD and Pfizer, in collaboration with partners like ACCURIDS and the EDM Council. In 2024, it was recognized with the Bio-IT World Innovative Practice Award.  

That collective authorship is the whole point. The consistent, trusted medicinal product data enabled by the IDMP Ontology can only be a benefit to one, if it built and shared by all. 

The road ahead  

The IDMP Ontology has now entered a long-term maintenance phase, with a multi-year commitment to keep the ontology current with the ISO standards, resolve issues, and make it simpler to adopt. A quarterly Community of Interest and regular user-group meetings keep the community learning from each other’s real-world implementations and the ontology aligned with industry demands. 

How to get access and involved  

The IDMP Ontology is open source. The public version is freely available to download from the ontology webpage. Updated versions are examined by Pistoia Alliance members (ontology funders and wider community) prior to public release.  

If you’d like access to the IDMP Ontology, have questions about how to start, or want to see how others are using it, we’re glad to help. Reach out to idmpo(at)pistoiaalliance.org. 

We also welcome everyone to join our Community of Interest. 

If your organization is already a member of the Pistoia Alliance, we invite you to get involved. If not, this is an opportunity to explore how the Alliance supports pre-competitive collaboration across data, AI, interoperability, and standards. 

We began this work with a simple end goal: better, more trustworthy medicinal product data — and, ultimately, patient safety. That’s not something any single company can achieve alone. Which is exactly why we encourage you to join us. 

What can you do with this? 

If you work with medicinal product data day to day, start by looking at where your product information requires manual effort to enter, update, reconcile. Think of ways that would optimize your data workflows, bearing in mind interoperability and automation.  

If you work with regulatory data, consider the IDMP Ontology as shared infrastructure rather than a compliance cost. Mapping once to a common, ISO-referenced language positions your organization to align medicinal product data and build AI applications. If you would like to talk through where to start, we are glad to help. 

Where to learn more 

Browse the IDMP Ontology webpage for updates, resources, and events. 

Join the Pistoia Alliance. Learn about benefits to members here. 

About the authors 

This article reflects the collective expertise of the IDMP Ontology team and community within the Pistoia Alliance. Final editorial work by Aditya Tyagi. 

Many thanks to the speakers at our July 2026 Community of Interest webinar, Sheila Elz (Boehringer Ingelheim), Heiner Oberkampf (ACCURIDS) and Raphael Sergent (ACCURIDS), and to our Steering Committee and funding members, for their contributions, ideas, and continued support. 

Join the conversation 

  • How far apart are your internal product data and the data your regulators hold — and what does it cost you to keep them aligned? 
  • What has held your organization back from adopting a shared standard like the IDMP Ontology? 
  • Share your perspective in the comments and connect with others working to make medicinal product data consistent, trusted, and reusable! 

#IDMP #IDMPOntology #PharmaData #RegulatoryAffairs #Interoperability #FAIRData #Ontology #DataStandards #AIReadyData #PreCompetitiveCollaboration #PistoiaAlliance 

FAIR4Clin: Is Your Clinical Data Working Hard Enough? 

Posted on by Helen Taylor

This is an article from the Pistoia Alliance FAIR for Pharma community. For more information about this cross-industry initiative, visit our community webpage. 

Clinical trial data locked in silos, PDFs and slide decks? FAIR4Clin is a practical guide to map the complexity of FAIR implementation in the clinical space. 

You ran a clinical trial. The data exists. Somewhere. 

When a colleague needs a cross-trial comparison, or when a regulatory question surfaces three years after database lock, the hunt begins. Spreadsheets, CRO hand-offs, metadata buried in a slide deck from 2019, a key variable defined slightly differently in every study. Weeks of detective work before any actual science, or an answer to a regulatory question, can happen. 

This is not a technology problem. It is an organisational design problem. FAIR data would be the answer. And FAIR4Clin is the guide to navigate the complexity of FAIR in the clinical space. 

 

What this article is about 

FAIR4Clin, the free guide from the Pistoia Alliance, tackles three things this article will preview: why clinical and real-world data needs FAIR specifically, what a FAIR-by-design approach looks like across the full study lifecycle, and what leading pharma companies are already doing to make this real. 

 

Who should read this 

If you work in clinical data management, biostatistics, regulatory affairs, RWE, data architecture, or digital and AI strategy in pharma or life sciences, and you have felt the pain of data that is technically “there” but practically unusable, this is for you. 

It is also for those building the next generation of data platforms and asking: how do we avoid recreating the same silos in a modern wrapper? 

Is Your Clinical Data Working Hard Enough? 

1. The clinical data problem has a name — and a specific answer 

Clinical data is not “just data.” It is multi-source, heavily regulated, generated under conditions designed for regulatory submission, but not for future reuse, cross-study integration, or AI. 

The result is predictable: fragmented datasets across CROs, sites and internal systems; inconsistent or missing metadata; consent terms never designed for secondary use; and standards (CDISC, OMOP, FHIR, SNOMED…) that are each valuable but often applied in isolation, without the semantic glue to make them talk to each other. 

FAIR (Findable, Accessible, Interoperable, Reusable) gives a common language to diagnose and address these gaps. But generic FAIR guidance rarely speaks the language of clinical trials. FAIR4Clin does. 

It is written specifically for study-level data: clinical trials, registries, and real-world evidence. It helps you identify where the gaps are in your own processes and understand what “FAIR” means in practice; not as an abstract framework, but as concrete choices made at each phase of the study lifecycle. 

 

2. FAIR is a design principle, not a last step 

One of the most useful contributions of FAIR4Clin is this: FAIR is not something you do after a study is complete. It is something you design in from the start. 

The guide walks through the clinical study process through a FAIR lens, from protocol and data management plan through data collection, curation, analysis, and ultimately secondary use and sharing. At each step it asks: what metadata is needed? Which standards should apply? How are consent and permitted uses encoded so they remain machine-readable downstream? 

It also clarifies how the major clinical data standards relate to each other and to FAIR: 

  • CDISC (CDASH, SDTM, ADaM): the regulatory foundation for structured clinical trial data. 
  • OHDSI / OMOP: common data model for large-scale observational and real-world data analytics. 
  • FHIR®: modern API-driven health information exchange across EHRs and registries. 
  • Semantic models (BRIDG, NCIT, others): the conceptual glue that aligns meaning across systems. 

The key point FAIR4Clin makes explicit: using CDISC or OMOP alone does not make data FAIR. The missing pieces are machine-actionable metadata and semantic clarity, including persistent identifiers, provenance, licensing, and shared vocabularies that let datasets find each other and be trusted when reused. 

This is what distinguishes FAIR4Clin from a standards compliance checklist. It is a roadmap for designing clinical data that can be discovered, trusted, and reused for follow-on analyses, regulatory queries, AI, and cross-organisation collaboration. 

 

3. Leading companies are already doing this 

FAIR4Clin is not theoretical. It documents how major pharma companies are operationalizing these principles today: 

  • Roche: prospective FAIRification at the point of entry. Microservices harmonise clinical and non-clinical data, embedding standards and quality checks from the outset rather than retrofitting them years later. Read more 
  • AstraZeneca: set up very early one enterprise URI policy across business domains, including clinical, ensuring data can be found, linked and reused consistently regardless of source system. Read more 

These are not pilot projects. They are infrastructure decisions being made at enterprise scale. The message is clear: FAIR is becoming core clinical data infrastructure. Organizations that design for it now will avoid costly retroactive work later. 

 

What can you do with this? 

You do not need to redesign your organisation to start. Three concrete steps for the next 90 days: 

  1. Run a quick FAIR baseline. Pick 2–3 representative studies. Ask: can we easily discover them? Are identifiers stable across systems? Is metadata structured and machine-readable? 
  1. Update your protocol and DMP templates. Add a requirement for standard identifiers, named vocabularies (CDISC, SNOMED, LOINC), and a high-level secondary-use plan. This costs almost nothing and prevents enormous friction later. 
  1. Pilot on one concrete use case. For example: “make our oncology portfolio FAIR at the study level for cross-trial analysis.” Measure time saved. Build your internal case from evidence, not advocacy. 

And if you lead a data, digital, or R&D function: treat FAIR as clinical data infrastructure, not a compliance checkbox. The organisations named above are making it an enterprise decision. The sooner it is designed in, the less there is to retrofit later. 

 

Where to learn more 

The full FAIR4Clin guide is free, open and available here. 

We also welcome your feedback as we develop the next version! 

Also consider joining the FAIR for Pharma community at the Pistoia Alliance.  

This cross-industry community has been creating practical tools, frameworks, and thought leadership for implementing FAIR data since 2019. Consistent, reusable clinical data builds on a shared infrastructure for the whole industry, built once by the industry. Learn more 

If your organisation is not yet a member of the Pistoia Alliance, this is an opportunity to explore how we support collaboration across data, AI, interoperability, and digital transformation in life sciences. 

 

About the authors 

This article reflects the collective expertise of members of the FAIR for Pharma Community within the Pistoia Alliance. 

Our main contributor was Tara Kumar Gajula. The final article was edited by the community facilitator, Giovanni Nisato.  

With thanks to the FAIR for Pharma Steering Group and the Best Practices and Business Value working groups for their contributions, ideas, and energy throughout the year. 

 

Join the conversation 

  • Where does your organisation sit on FAIR for clinical and real-world data — designed in from the protocol, or bolted on after database lock? 
  • What’s the single biggest barrier to making your study data findable and reusable? 
  • Share your perspective in the comments and connect with others working to make clinical data FAIR! 

#FAIRData #ClinicalTrials #Pharma #LifeSciences #Interoperability #FAIR4Clin #PistoiaAlliance #DataStewardship #DigitalHealth #ClinicalResearch