Apr 6, 2026
We recently brought together a group of biotech and pharma executives in Boston for a closed-door conversation about AI. Nobody was there to sell anything, and people talked about what was actually happening inside their companies instead of the version they'd give on a panel. Attendees represented executives, AI leaders, and functional leads across early stage biotechs, mid-size pharma, and global life sciences companies.
Here's what we heard.
1. The maturity gap is wider than most people admit. In the same room, we had companies describing themselves as "just starting" and others as "leaning in," and there is no single playbook right now.
2. Trust is the real bottleneck. Not technology. Not budget. Every leader in the room knew that one bad AI output, even a user error, can set back adoption by months. The pressure not to over-promise is as intense as the pressure to move fast.
3. Finding a common language is one of the most underrated challenges. Organizations are suddenly bridging functional experts (medical, regulatory, commercial) with technical teams. Without shared language and shared problem definition, AI gets applied to the wrong things and adoption stalls.
4. Human-in-the-loop isn't going away. It's becoming more important. Even as the FDA actively pushes AI forward, executives were clear: AI-assisted doesn't mean AI-decided. Drafts still go to human review. The goal is efficiency, not replacing judgment.
5. AI is a CEO and board-level priority, but accountability is still fuzzy. Everyone agrees it matters. What's less clear is who owns it across the business. Is it the CDO? CMO? P&L leaders? Defining accountability across functions, not just at the top, is one of the most urgent open questions.
6. The "AI Officer" is emerging as a key unlock. Not just a title but a function. Someone who brings organizational focus, bridges subject matter experts and technical teams, and ensures AI is applied to real problems in ways people will actually adopt.
7. Quick wins aren't optional. Organizations that aren't showing tangible results within the first few months are watching interest evaporate. The pilot to program to proof of impact path is the one that's actually working.
8. The 3-year bets are operational, not aspirational. All first drafts generated by AI. Organizational efficiency means doing 2-3x with the same team.
The real gap
What strikes us most: the gap between "we have AI" and "AI is working for us" is enormous, and it's not a technology problem. It's an organizational, trust, and change management problem.
That's exactly the problem we started Peer AI to solve.
We're grateful people were willing to be that candid. Hearing what's actually happening on the ground, instead of the slide-deck version, is rare, and it's what makes getting this group together worth doing.
If you're navigating this and want to join future conversations, reach out. We're building a community of executives who are figuring this out together with real talk.
