Industry Outlook 2026: How AI and Payer Pressure Impact Biopharma R&D (Part 1)

*Full transcript available below

Janice MacLennan, a senior strategy consultant to Big Pharma and the founder and CEO of Nmblr, a UK-based collaborative strategy platform, outlines three structural shifts reshaping the biopharmaceutical landscape: AI adoption, intensifying payer pressure, and the redesign of work and talent systems.

In an interview with BioPharm International®, McLennan emphasizes that AI has moved beyond pilot mode and is now deeply embedded across the value chain. As she notes, "AI moved from experimentation to actually starting to be woven into the fabric of discovery, development, and, actually, commercial execution. Those are three areas where AI has been widely adopted."

In R&D, development processes are being fundamentally redesigned around AI-driven analytics, changing how uncertainty, timelines, and portfolio risk are managed, MacLennan explains. On the commercial side, AI is enabling sophisticated omnichannel engagement and micro-segmentation, allowing teams to target narrower patient and prescriber cohorts with far greater precision and speed, she adds.

How is AI reshaping biopharma discovery, development, and market access?

McLennan links these advances to mounting economic and payer scrutiny, particularly in oncology, inflammation, and rare diseases. Historically protected, these therapeutic areas in 2025 faced tighter coverage criteria, step edits, and growing expectations for demonstrated value. "You have to be able to demonstrate the value of what you bring to the market over and above just the science…" she states.

This shift, MacLennan observes, is forcing biopharma organizations to embed payer readiness as early as Phase II development, integrating evidence-based strategy and access planning alongside clinical development.

Looking ahead, McLennan argues that companies must redesign work before workforce, building talent systems that prioritize human capabilities, such as judgment, creativity, relationship building, and accountability, while positioning technology as an enabler rather than a threat. In this model, she explains, AI augments expert teams, supporting faster, better-quality decisions in an increasingly constrained and competitive market.

Transcript

Editor's note: This transcript is a direct, unedited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

Speaker 1

My name is Janice MacLennan. I'm a Life Sciences strategy consultant, and have been doing this for over 35 years, working with pharma and biopharma teams, really to help them navigate uncertainty and make better choices. Also, I'm the founder and CEO of Nmblr, a strategy and collaboration platform which has been built to help teams collaborate more effectively and align around decisions faster.

I'm going to focus on probably three trends. One is around AI and and the point that AI moved from experimentation to actually starting to be woven into the fabric of discovery, development and actually commercial execution, those are three areas where AI has been widely adopted. How has that impacted the industry, or R and D have redesigned their development processes around AI, so what they're doing looks very different. And from the commercial perspective, we've got things like omni channel, which really it's it's been done very differently because of AI. We've got things like micro segmentation, which are now possible, so being able to target different populations. So all of that is, is actually how it's impacted the industry, and why it's impacted, why AI has has had such an impact, I think, is because of the speed, the cost and getting us to see what good could look like, which is quite different from the past. So that's one trend. A second trend, I would say, actually, I'm going to zoom in on to certain therapeutic areas, like oncology, inflammation, rare disease, which historically have been quite protected, one could argue, whereas in the last year, in 2025 they definitely came under a lot more economic and payer pressure than they've ever experienced before. And how that's impacted them is the industry has and they still are. Probably a lot of companies who believe that great science will sell itself. And I'm going to say that is absolutely no longer true. You have to be able to demonstrate the value of what you bring to the market over and above. Just the science and why this happened in these particular areas is, I think, the amount of competition you know everybody because they were high value areas, lots of companies have focused on developing development in those areas. So the competition is super intense, and because of the payer pressure, the margin for error is even greater. So, so that's that a third area is what really became a big battlefield was, is market access. So in 2025 that was a big change. The payers started developing much tighter criteria. Generally. They started looking at different ways of, you know, step edits, ways of of delaying access to the most in an innovative therapies and cross functional teams have had to start building in their thinking around the payer ready as early as phase two. So historically, they used to leave it till we've got approval, or we confidently going to get her approval. And let's then start worrying about the payer, whereas this has now got to be built into the equation a whole lot earlier.

In my view, the most successful strategies are going to be those that are more about having what we would refer to possibly as a talent system, rather than ad hoc incentives, bonuses. That sort of approach. So what do I mean by that, really redesigning your work before you actually redesign the workforce? And when I say redesign the work, actually think about where humans create value, and think about designing work around where humans create value. For example, judgment, creativity, relationship, building, accountability, all these things are things that humans can do. I'm less confident that AI can do or technology can do. If we do that, what we can then do is see everybody will see technology as an enabler rather than as a threat. Because at the moment, a lot of people are perceiving AI to be a threat, I would suggest.