A practical look at how life sciences leaders can build a defensible AI roadmap grounded in data, governance, human trust, measurable value, and evolving approaches to discoverability.
Natalie Sevcik | Director of Communications
September 11, 2026
AI adoption in life sciences has reached an inflection point. The question is no longer whether organizations should explore AI, but how to execute in a way that is practical, measurable, and defensible.
That was the focus of The AIgency Era: Building a Defensible AI Roadmap for Life Sciences, featuring Justin Molavi of Genentech, Brooke Fleming of Travere Therapeutics, and MERGE’s Keir Bradshaw and Pat McGloin.
The conversation went beyond the technology itself, exploring what it takes to make AI actionable—and ultimately, a valuable team member rather than a bolt-on tool. Four themes emerged for life sciences leaders building their AI roadmap.
It’s tempting to begin an AI strategy by asking: What tool should we buy? What model should we use? What can we automate?
But a more important question may be: What if data governance didn’t block innovation, but actually unlocked it?
For life sciences leaders, governance is a catalyst for commercial readiness.
Organizations have traditionally operated in a “buying data” model. The next evolution requires treating data as a strategic product—curated, governed, and designed to help teams, patients, and HCPs make better decisions.
Governance isn’t simply a compliance exercise. In a highly regulated industry, it forms the foundation for trust. If employees don’t trust an insight, they won’t use it.
The goal is to create enough structure for people to experiment with confidence.
Even the best AI strategy can stall if the people expected to use it don’t trust it.
When commercial field teams ask, What data produced this recommendation? Why should I believe it?—trust can break down quickly.
The opportunity is to turn raw AI outputs into trusted, human-first recommendations by combining human insight and creativity with the scale and power of AI.
That makes change management and human behavior central to AI adoption, not an afterthought.
One idea raised during the conversation was the role of “activation champions”—people who bridge the gap between technology teams and the teams using AI in the field. They help translate not only how a capability works, but why it matters and how it can be used with confidence.
This points to a broader shift in the role of humans.
As AI becomes better at generating answers, people become more important—not less—for applying context, judgment, and empathy.
In high-stakes environments like health and life sciences, AI should be viewed as a co-architect, working alongside human expertise rather than replacing it.
Organizations don’t need to jump straight from experimentation to autonomous agents. A more defensible path is crawl, walk, run.
That progression matters because AI maturity isn’t simply about deploying increasingly sophisticated technology. It’s about developing the organizational capabilities to use it responsibly.
This also changes how leaders should think about ROI.
Instead of measuring whether an AI tool was launched or how many people logged in, ask: Did it change a decision? Did it improve an outcome? Did it help a team work differently?
Those are the signals that turn AI from an innovation story into a business case.
Search is shifting. With Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), patients and HCPs increasingly find information through AI-powered experiences rather than traditional web links.
For life sciences, that means rethinking content itself.
Brands need to stop viewing content as static web pages and start treating it as a dynamic data asset: modular, authoritative, factual, and structured. Content built this way can be understood and surfaced by AI systems, helping commercial teams deliver more relevant experiences at scale.
It’s a shift from simply being visible in search to becoming a trusted source within the answers people receive.
The AIgency Era isn’t about adopting AI for its own sake. It’s about building the organizational foundations—data, governance, products, people, and content—that allow AI to create meaningful value without creating unnecessary risk.
Insight meets instinct.
Technology meets humanity.
And even when we talk about AI, we’re ultimately talking about people: how they make decisions, build trust, and create better outcomes.
For life sciences leaders, the most defensible AI roadmap may not be the one with the most advanced technology.
It’s the one that creates the strongest foundation for using that technology well.
Watch the full recording of The AIgency Era: Building a Defensible AI Roadmap for Life Sciences to explore what a practical, defensible AI roadmap could look like for your organization.
