Introduction
Right now, most life science commercial teams are stuck in AI adoption purgatory. ChatGPT. Claude or CoPilot licences are paid for. Someone has run a workshop. A few people use it to tidy emails. And the board is starting to ask where the productivity gains went.
You are not alone. Our survey of ELRIG Drug Discovery 2025 exhibitors found that 37% of commercial teams operate in what we call unstructured exploration. They have tools. They have no programme. The result: 69% of individuals in those organisations do not use AI daily despite having access.
Access is not adoption. That gap is where most commercial teams are losing ground.
Tools do not change behaviour
The instinct when AI lands is to issue licences and let people experiment. It feels democratic. It is also why most programmes stall. Without structure, AI becomes a personal productivity hack for the curious and an inconvenience for everyone else.
Compare that with structured programmes. Where commercial teams have a pilot or operational programme in place, daily usage jumps to between 53% and 73%. The difference is in design.
Start with three jobs
The most expensive mistake commercial leaders make is trying to AI-enable everything at once. The teams that pull ahead pick three jobs and execute on those.
A good starting trio for life science commercial teams looks like this:
- Meeting preparation. Before a customer call, brief the AI on the account, the prospect, and the product. Get a one-page summary, three likely objections, and two competitive angles. Saves an hour per call and improves the conversation.
- Market intelligence synthesis. Feed it competitor websites, press releases, and your own win-loss notes. Ask for the patterns you would miss. Most teams do this monthly. The good ones do it weekly.
- Content adaptation. Take one piece of long-form content and have AI generate the eight versions you need across LinkedIn, email, sales enablement, and event collateral. Edit hard. Do not publish raw.
Three jobs. Repeatable. Measurable. Defensible to a CFO.
Augment before you automate
This is the principle that separates the teams that get value from the teams that burn cycles. AI should assist your people first, so they learn to reframe their work through an AI lens. Automation comes later, once the workflow is understood and the use case is proven.
The temptation to skip straight to automation is strong. The cost of doing so is silent. You end up automating broken processes and calling it progress.
Measure what gets used
The single most overlooked metric in commercial AI programmes is daily active usage. How many of your team are using AI to do their actual job, today? Licences, training completions, and ROI projections all tell you less.
If that number is below 50% after six months, the programme is failing. Look for the missing use case, the missing champion, or the missing measurement loop.
Barriers in our survey ranked in this order: data quality (44%), governance (36%), and skills (24%). Budget and ROI uncertainty came last. The blockers are operational. Fix the operations.
The harder shift comes next
Getting started with AI is the easy part. The harder shift is using AI to bring the customer into the room, every roadmap meeting, every campaign brief, every sales call. That is where commercial teams stop optimising existing work and start building an advantage that compounds.
More on that in the next piece, or if you can’t wait, contact Jenny Gillard at Pivotal Scientific or Matt Wilkinson at Strivenn.
AI in your commercial team is either working or it is wasted budget. The question is whether you are willing to start properly.
