AI in Life Sciences: From Hype to What Actually Works

Contents:

A recap of the LinkedIn Live Webinar, Hype vs. Reality: AI Investments Pharma Leaders Should Make in 2026

Bryan Ennis
Co-Founder & Chief Quality Officer
Sware

Jeff Brittain
Former Head of IT, North America, Bayer

CEO, UMRF Ventures

Executive in Residence, FedEx Inst. of Tech.
President, Verix Partners

In a LinkedIn Live conversation with Jeff Brittain, we spent 30 minutes looking back at a year of AI progress in Life Sciences—what worked, what didn't, and where pharma leaders should focus their investments in 2026.

If you missed the conversation, here are the ideas I keep coming back to.

Investment Is Rising, But Scale Remains Limited

The numbers frame the moment clearly: 92% of Life Sciences companies are increasing AI spend, yet only 2% have reached true enterprise scale. Why is scaling so difficult? Life Sciences combines the complexity of any enterprise with manufacturing, research, and GxP environments where outcomes need to be predictable, measurable, and defensible. Rigor isn't a bug in this industry; it's the point.

The teams making progress tend to focus on repeatable, data-rich use cases, particularly in areas like manufacturing. Rising AI budgets don't equal enterprise maturity. Scaling AI requires the right use cases, data, infrastructure, and governance.

Regulation Is Raising the Stakes

Jeff and I spent a lot of time on this. As AI moves deeper into regulated environments, responsible use and appropriate validation become leadership responsibilities—not simply IT tasks.

For global organizations, navigating different regulatory expectations across the EU, US, and China adds another layer of complexity. The landscape is still evolving, but the takeaway is straightforward: build governance and validation into your AI strategy from the start. Retrofitting controls after deployment is far harder than designing them in from day one.

Start with the Problem, Not the Technology

We see two ways organizations get AI wrong, and they sit at opposite extremes.

• Doing nothing. Treating AI as hype leaves you exposed as competitors operationalize it and pull ahead.

• Going all in on one platform. Every major model provider and hyperscaler is engineered to lock you into its ecosystem.

Both approaches can leave you with technology without a clear path to value. The better approach starts with a different question: What business problem are we trying to solve?

That shift from model-first to process-first is critical. Start with the outcome, map the current process, and identify where AI can create measurable value. But don't stop at optimization. AI can make existing processes faster—and create entirely new ways of working. Start with the process, redesign where needed, and then build the technology around it. Stay flexible as the technology evolves, and don't let a platform decision substitute for defining the problem.

Domain-Specific Models and Flexible Architecture

Jeff made a point I keep coming back to: AI is following a familiar technology cycle. Like ERP, we're moving from point solutions toward best-of-breed tools and more flexible architectures.

I don't expect one system to do everything. A more practical model pairs foundational models for general reasoning with smaller, domain-specific models focused on areas like regulatory, validation, or medical affairs. The key is staying flexible—keeping your architecture loosely coupled so you can use the right model for the right problem.

Agentic AI: The New Delivery Model

When Jeff reframed agentic AI as an approach to executing end-to-end processes rather than a new technology category, something clicked for me. In Life Sciences, the goal isn't AI for its own sake. It's better processes and outcomes.

Agentic AI could eventually become simply another way work gets done. But getting there requires a foundation: your data, processes, and people need to work together, with visibility into outcomes and the ability to detect drift. Digital twins may become increasingly useful here, giving organizations a way to monitor how complex processes behave over time.

As we move toward more autonomous operations, that evidence becomes critical. You can't assume autonomy is working—you have to demonstrate it.

Where Leaders Should Invest in AI

We see two areas where AI investment can create meaningful value.

First, data-rich, repeatable operations. Manufacturing, analytics, scheduling, logistics, and fill-rate optimization offer strong opportunities because they generate large volumes of structured data. Enterprise productivity remains another practical area, with knowledge retrieval, employee assistants, and RAG delivering frequent, measurable value.

Second, regulated use cases. We're now seeing stronger approaches to implementing, validating, and governing AI in GxP workflows that once felt too risky. Image-based applications, scientific analysis, and protein modeling are among the areas moving quickly.

The opportunity is to build on proven productivity gains while expanding into higher-value, regulated applications with the right controls in place.

Governance and Data Must Scale With AI

Moving from productivity tools into GxP requires a different level of discipline. Governance has to mature alongside deployment: define ownership, assess risk before go-live, establish appropriate controls, and build compliance in from the beginning. Standards such as ISO 42001 will increasingly shape that conversation. Quality and Regulatory also become strategic partners in AI transformation, not simply gatekeepers, with clear accountability for technology, processes, and outcomes.

Underneath all of this is data. AI has made data quality, ownership, lineage, and traceability impossible to ignore. Poor data leads to poor AI performance and poor decisions, making data both the foundation of AI and a growing source of risk.

The next phase will be less about proving that AI works and more about improving the processes around it—finding inefficiencies, redesigning workflows, and moving from pilots to production with the evidence to support every decision.

Hype vs. Reality: AI Investments Pharma Leaders Should Make in 2026

During this webinar, Bryan and Jeff share expert insights and real-world perspectives on which AI capabilities are delivering measurable impact in regulated environments today, which investments are falling short, and how to evaluate emerging technologies through a compliance-first lens.

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