5 Patterns We’re Seeing in Life Sciences AI Adoption

ZS Associates reports that 93% of life sciences tech executives plan to increase AI spending this year. Only 22% have successfully scaled it, per Deloitte. That gap tells you almost everything you need to know about where the industry actually stands.

I spend most of my time working with life sciences leaders who are trying to move AI from ambition to operating reality — in an industry where the stakes are ultimately about getting better medicines to patients, faster. Not the enterprise strategy conversation, but the functional one. The VP figuring out what AI means for how her regulatory team works, the head of medical affairs who's been told to "integrate AI" but hasn't been given a plan or a budget to do it with.

Here are five patterns I keep seeing across that work.

1. Enterprise AI strategies aren't built for functional leaders

Most AI strategies in life sciences are designed top-down. They start at the enterprise level with broad vision, platform selection, and governance frameworks, then assume the benefits will cascade to individual functions. That logic makes sense at the enterprise level. It breaks down in practice.

Enterprise AI strategies are built for C-suite sponsors with $2M+ budgets and 12-month timelines. They answer questions about platform architecture, vendor selection, and organizational governance. What they don't answer is how a specific function should operate differently next quarter — which workflows change, what new capabilities the team needs, and how to get there within the regulatory and operational constraints that function actually faces.

These are fundamentally different questions, and they require a different kind of work: shorter in timeline, deeper in the function, more focused on how work changes than which platform to buy. That layer of planning is missing from most organizations' AI approach, and it's the root of nearly every other pattern on this list.

2. Functional leaders are carrying the mandate but don't have a plan

This is the pattern I find most striking. In every life sciences company I talk to, there are VP and SVP-level leaders who've been told — explicitly or implicitly — that AI is part of their mandate now. Their CEO has committed to it publicly. Their board is asking about it. Their enterprise strategy includes it as a priority.

But when you sit down with those same leaders, they don't have a concrete plan for how their department operates differently with AI in it. They have awareness, ambition, and often anxiety — but not a future state operating model that gives them a clear picture of which workflows change, which decisions get faster, what capabilities their team needs that they don't have today, and how to get there without disrupting the work that matters most.

That's not a failure of leadership. It's a direct consequence of the pattern above. When enterprise strategies don't reach the functional level, functional leaders are left to figure it out on their own — without a framework, a methodology, or in many cases a budget designed for the kind of work they actually need.

3. Teams lack clarity about what AI actually changes

BCG found that 70% of digital transformations fail because of poor change management, not technical issues. In life sciences, I'd argue the number is higher.

Here's why: life sciences functions don't operate like commercial tech teams. You can't move fast and iterate freely when you're running a pharmacovigilance operation or managing a regulatory submission timeline. The people in these roles are rigorous by training and by necessity. They're not resistant to AI — they're resistant to ambiguity about how AI changes what they're accountable for.

When functional leaders don't have a plan, that ambiguity cascades to everyone on their team. People don't know which decisions will shift, which workflows will look different, or what will stay the same. Without that clarity, even the most well-intentioned AI initiative meets friction — not because people don't see the potential, but because nobody has told them concretely what this means for their work.

We worked with a Fortune 100 biopharma recently where the leadership team had all the right ingredients: executive sponsorship, tools deployed, and a clear strategic vision. What they didn't have was a plan for how AI would change the operating model of the specific function we were working with. Once we built that — mapping workflows, clarifying decision rights, and defining what changes and what doesn't — adoption went from theoretical to actionable in weeks. Nothing changed about the technology. What changed was that the team knew exactly what was expected of them and how their roles would operate going forward.

4. Pilots succeed in isolation and fail to scale

This is where all of the above becomes visible. A team runs a pilot — automated literature review, AI-assisted signal detection, predictive analytics for site selection — and it works. The technology performs. The proof of concept succeeds. And then nothing happens.

Not because the pilot failed. Because it was never designed to connect to the actual decision-making process. The insights don't flow into existing workflows. Nobody changed how the team operates to absorb the new input. There's no operating model that accounts for how the pilot's output fits into the broader function. So, the pilot succeeds on its own terms and fails to scale — and the organization adds it to a growing list of AI experiments that looked promising but didn't stick.

This is the symptom that most organizations recognize first. But it's not the root problem. It's the downstream consequence of everything above: enterprise strategies that don't reach the functional level, functional leaders without a plan, and teams without clarity about what changes. Pilots launched in that environment are set up to stay in a vacuum, no matter how well the technology works.

5. The best roadmaps are built around early wins, not around technology

The natural instinct, once an organization recognizes these patterns, is to go big. Catalog every AI tool. Evaluate every vendor. Build a comprehensive 12-month implementation timeline. In theory, this is rigorous. In practice, it stalls — because it answers the wrong question first.

The life sciences teams I've seen make real progress build their roadmap differently. They start by identifying two or three workflows where AI can create visible, immediate relief — reducing manual effort, accelerating a decision cycle, eliminating a bottleneck that everyone knows about, but nobody has fixed. Those early wins become the foundation of the roadmap. And crucially, the plan isn't tied to any specific platform or vendor — it's built around how the work changes, which means it stays relevant regardless of which tools the organization ultimately adopts.

This matters because in a highly regulated, risk-aware industry, people need to see AI working in their world before they'll trust it with the work that matters most. A roadmap that starts with quick wins builds organizational confidence. A roadmap that starts with technology builds a presentation. 

What ties these patterns together

There's a thread running through all five: the missing piece isn't technology, strategy, or ambition. It's a clear picture of how the function will operate differently once AI is integrated.

That's what a future state operating model does. It answers the questions that functional leaders are actually asking: Which workflows change? Which decisions get faster? What stays the same? What capabilities does my team need that they don't have today? How do we get there without disrupting the work that matters most?

When that picture exists, everything else follows. Enterprise strategy connects to functional execution. Leaders have a plan they can stand behind. Teams have clarity about what changes and what doesn't. Pilots connect to real workflows instead of living in isolation. And quick wins aren't random experiments — they're deliberate first steps toward a defined future state.

Without it, you get what most organizations have right now: a lot of AI activity, very little AI adoption, and functional leaders carrying a mandate they can't act on. With it, you have something defensible — a plan that leadership can support, that regulatory and quality teams can endorse, and that the people doing the work can actually follow.

These patterns aren't going away. If anything, they're intensifying as AI investment accelerates. The organizations that will lead in life sciences AI adoption won't be the ones with the biggest technology budgets. They'll be the ones that do the harder work of defining how their functions will actually operate differently — and building the plan to get there.

If your function is navigating the gap between AI ambition and execution, our AI Integration Roadmap is designed for exactly this moment. In 6–8 weeks, we help VP/SVP leaders build a concrete integration plan — grounded in how your department actually works, and designed to be adopted, not just presented. Learn more at nepf.co/ai-lifesciences.