Sector
Two organizations in one: a regulated, validated side and a commercial side that moves at market speed.
Life sciences is really two organizations sharing a logo. On one side R&D, clinical and quality work in validated, auditable environments where every step has to be defensible years later. On the other, commercial, market access and supply chain teams move at ordinary business speed. A single generic data course speaks to neither.
The regulated side has a particular problem with AI. Nothing is impossible, but everything has to be explainable, reproducible and documented, and the burden of proof sits with you. That makes people cautious in a way that is entirely rational, and it means an AI program that skips governance will simply be ignored by the people who most need it.
The commercial side has the opposite issue: plenty of data, plenty of tooling, and decisions made on analyses nobody in the room can fully interrogate. Market access, field force effectiveness and forecasting all reward someone who can ask the second question.
Two behaviors, one per side of the house. On the regulated side: someone who can use AI support in a validated process and explain afterwards why it holds up: reproducible, documented, defensible years later. On the commercial side: someone who asks the second question about an analysis instead of taking the headline.
Both need knowledge before skill, because caution here is rational and a program that ignores it gets ignored back. Then skill in your own tools, practiced in the browser on your own cases, with a sandbox on your own subdomain where data cannot leave the building. Separate tracks for the two sides, so neither sits through material aimed at the other.
What makes it stick is ownership. Champions in both R&D and commercial, live sessions, and a handover to your own L&D at the end. Growth is measured from a baseline before the start and benchmarked against other organizations, evidence that survives a quality review rather than a satisfaction score.
Across all our clients, cohorts finish at 80% completion over the last twelve months and more than 10,000 learners, against 46% for learners who paid for a MOOC.
We also work with Deeploy on AI governance and with Ortecha, whose work is aimed squarely at complex and regulated industries.
Course completion
over the last twelve months
Learners
have been through our programs
Average skill growth
Measured pre vs post, never self-reported
These are the personas we train in this sector.
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