Over the past several months, I surveyed 70 senior commercial leaders across life sciences, medical device, and diagnostics companies to understand how they're actually putting AI to work — beyond the hype.
The findings reveal an industry in motion, but not yet at scale. There's significant white space for leaders willing to move past experimentation.
The majority of leaders report regular AI use for specific tasks like drafting content, summarizing calls, and basic research. But only a small fraction have built repeatable, integrated workflows.
Most usage is still ad hoc — a sales rep polishing an email in ChatGPT, a marketer drafting a LinkedIn post, a product manager summarizing a call transcript. Useful, but episodic.
In regulated industries like medtech and diagnostics, this matters. Ad hoc AI use creates compliance blind spots, inconsistent messaging, and missed productivity gains. The leaders pulling ahead treat AI workflows as products — designed, documented, and reused.
When asked who owns AI strategy, the most common answer was "handled by each department" or "no centralized ownership." Combined with the top blockers cited — messy data, no clear priorities, and integration challenges — this points to a structural gap.
AI rarely fails because the technology isn't ready. It stalls because no one is accountable for outcomes.
Practical move: even if you can't justify a full-time AI lead, designate a senior owner — often a VP of Commercial Operations or Marketing — to set priorities, vet tools, and track ROI. In life sciences specifically, this person should partner closely with regulatory and IT to pre-clear use cases.
Marketing and content is where AI is most embedded. Roughly two-thirds of respondents use AI to draft posts, emails, and web copy, and many repurpose long-form assets into short-form content. This is low-hanging fruit and worth doubling down on.
Prospecting tells a different story. While many use AI to identify target accounts and decision-makers, only a handful rate their AI prospecting as "very" or "extremely effective."
For commercial teams in medical device and diagnostics — where buying committees are complex and call points are highly specialized — generic prospecting tools underperform. The opportunity: build custom enrichment workflows tied to your ideal customer profile (e.g., hospital systems by procedure volume, lab networks by test menu, IDNs by GPO affiliation).
Two areas stood out as underutilized.
First, AI in customer support remains underdeveloped — many teams still run fully manual support despite proven knowledge base and ticket routing tools.
Second, AI in sales training and onboarding is mostly aspirational; nearly half are interested but haven't started. For complex clinical sales, AI-powered role-play coaches and on-demand product knowledge assistants can compress ramp time dramatically — a real edge when reps need to fluently discuss clinical evidence, reimbursement, and KOL data.
Perhaps the most telling finding: most leaders are not yet using AI to run multi-step processes — the "take an input, generate an output, update a system, notify someone" workflows that genuinely move the needle.
This is where the next 12 months of competitive advantage will be won.
1. Pick one repeatable workflow. Call summary → CRM update → follow-up draft is a great starting point. Build it end-to-end.
2. Name an owner with authority and a small experimentation budget.
3. Measure something — time saved, pipeline created, ramp time reduced. ROI doesn't have to be perfect to be persuasive.
The leaders who win with AI in life sciences won't be those with the most tools. They'll be those who turn experiments into systems.