Beyond the Hype: What Agentic AI Actually Means for Life Sciences

But as we explored with senior leaders from pharma, medtech, and digital health at our recent executive event at The Westin Hotel Hamburg Elbphilharmonie, co-hosted with Acquia and Conductor, the real conversation isn’t about what AI can do. It’s about what your organization is ready to operationalize. 

For Life Sciences companies, the context is unique. This is an industry where compliance isn’t optional, where content goes through Medical, Legal, and Regulatory review before it reaches a single audience, and where “moving fast and breaking things” can have consequences that go far beyond a product launch. And yet: the shift we’re seeing right now is fundamentally different from the chatbot wave of three years ago.

We’re moving from tools that suggest to agents that execute.

The Real Bottleneck Isn’t Technology — It’s the Operating Model

Dr. Michael Kurr, former Head of Digital Transformation at Boehringer Ingelheim and Novartis, put it plainly during our event: without a clear operating model, defined ownership, and proper governance, AI stays trapped in pilot mode — no matter how powerful the technology.

This resonated deeply with the room. Every leader we spoke with recognized the pattern: promising AI initiatives that stall not because of technical limitations, but because no one has answered the fundamental questions. Who owns the AI workflow? Where does it sit — in IT or in the business unit? And who is accountable when an AI agent makes a decision that touches compliance?

These aren’t technical questions. They’re organizational ones. And they’re the reason most AI pilots in pharma never scale.

From Production Commodity to Strategic Orchestration

Our Managing Partner Volkan Jacobsen framed the broader shift: a significant portion of digital budgets has historically gone toward the manual labor of coding, content assembly, and asset production. That era is ending.

When everyone has access to AI that can draft, code, and assemble in seconds, speed stops being a differentiator. It becomes the baseline. What matters next is how you orchestrate these agents across complex workflows — particularly in an environment where every piece of content carries regulatory weight.

For a pharma marketing team, this means the question is no longer “How fast can we produce a campaign?” It’s “How do we ensure that AI-generated content passes MLR review, aligns with local market requirements, and reaches the right HCP segment — without adding three weeks of manual back-and-forth?”

The Elephant in the Room: MLR

Every pharma marketing leader knows this scenario. A campaign is ready. Creative is approved. And then it enters the MLR cycle — Medical, Legal, Regulatory review — where it can sit for weeks while reviewers check claims against source documents, verify fair balance, flag off-label references, and ensure local market compliance.

This is where the promise of agentic AI meets the reality of regulated industries head-on. And it’s where honest conversations need to happen.

The potential is obvious: AI that pre-screens content against approved claims before it reaches a reviewer’s desk. Automated cross-referencing between marketing copy and clinical source documents. Audit trails that are generated as part of the workflow, not bolted on after the fact.

But the gap between potential and production-ready is significant. MLR workflows aren’t just about speed — they carry legal liability, they vary by market, and they require a level of traceability that most AI implementations haven’t been stress-tested for. The question isn’t whether AI will transform MLR processes. It’s who builds the governance framework that makes it possible — and who validates that framework against the regulatory standards your organization is held to.

This is exactly the kind of challenge that came up repeatedly during our event: technically feasible, organizationally complex, and impossible to solve without clear ownership.

From Lab to Launch: AI in Scientific Workflows

Dr. Marco Polidori from Eppendorf brought a perspective that extended beyond marketing into laboratory operations. His presentation showed how AI is already changing day-to-day lab workflows — but the success factors are the same: data quality, regulatory alignment, and interdisciplinary collaboration between IT, science, and operations.

The takeaway for the room was clear: whether you’re applying AI to content workflows or laboratory processes, the pattern is identical. The technology works. The challenge is building the organizational muscle to deploy it responsibly and at scale.

The 2026 Horizon: From Pilot to Scale

We’re entering what many call the “Year of the Pilot.” But for Life Sciences companies, a pilot without a path to scale is just a science project.

To bridge that gap, we’ve developed the AI Impact Framework — a structured approach to move from idea to operational AI:

1. Grounding. Identifying the high-value business cases that actually impact outcomes. Not “Where can we use AI?” but “Where does AI solve a problem that’s costing us time, money, or compliance risk right now?”

2. Minimum Viable AI. Building a working prototype with the guardrails your industry demands — not a demo, but a system that’s ready to be stress-tested in a real workflow.

3. Scale & Govern. Introducing the governance model, change management, and operational processes that allow a successful pilot to become a standard operating procedure.

A Note on Timing

It’s tempting to wait. To let others go first, learn from their mistakes, and adopt later with less risk. In many industries, that’s a reasonable strategy.

In Life Sciences, the calculus is different. The companies that build AI governance frameworks now — that define their operating models, train their teams, and establish compliance-ready workflows today — will set the standards that others have to follow. The cost of waiting isn’t just falling behind competitors. It’s having to adopt someone else’s framework instead of shaping your own.

AI in Life Sciences: What’s Next

The technology is ready. The question is whether your internal processes, governance structures, and team capabilities are ready to lead it.

If you want to explore what that looks like for your organization, there are two ways to start:

Go deep: Join our AI Impact Workshop — a structured session where we apply the framework to your specific use cases, workflows, and organizational reality. Reach out to us to find out more!

Start a conversation: Book a 30-minute call with our team. No pitch deck. No pressure. Just an honest assessment of where AI can create real value in your context — and what it takes to get there.

This article reflects insights from our executive event “Agentic AI in Life Sciences,” held in Hamburg in partnership with Acquia and Conductor. Speakers included Dr. Marco Polidori (Eppendorf), Dr. Michael Kurr (former Boehringer Ingelheim / Novartis), and Volkan Jacobsen (Factorial).