In this solo episode of AI For Pharma Growth, Dr Andree Bates explores the AI capability problem pharma has not properly named: training that works in the room, but fails to hold inside the organisation.

Dr Andree explains why one-off workshops, generic AI fluency programmes and broad learning platforms are not enough. They may teach people what AI is, what it can do and where it can fail, but they rarely teach the exact workflows, judgement calls and regulatory context people need for their own roles.

The episode looks at why AI capability fades over time. Some people leave training and build valuable new workflows, while others forget how to apply what they learned within weeks or months. In pharma, that matters because many AI use cases depend on cognitive, accuracy-based judgement: deciding whether a generated summary faithfully represents a source, whether a claim is substantiated, or whether an output can safely enter a regulated workflow.

Dr Andree also explains why generic training can create risk. If usage rises faster than judgement, teams may become more confident with AI without becoming more capable in the workflows where mistakes carry regulatory, compliance or patient safety consequences.

The key message is clear: AI capability needs to be maintained, role-specific and grounded in pharma reality. Training once, or training generically, is not a capability plan.

Topics Covered

Why AI training often fails to hold
The difference between awareness and capability
Why generic AI fluency is not enough
Role-specific AI workflows in pharma
Skill decay and why 90 days matters
Cognitive judgement and regulatory risk
Why confidence can outpace competence
Shadow AI and unmanaged tool use
What real AI capability support must include
The Pharma AI Enablement Institute

Eularis helps pharma and biotech leaders turn AI activity into board-defensible governed strategy and measurable commercial outcomes.

If your CFO asked tomorrow for the projected return of each major AI initiative - by year, across three years, with explicit adoption, operating cost and redeployment assumptions - could you produce an answer that survives scrutiny?

And if you could: would you know which of those initiatives most moves the company toward the outcomes it's exposed on over the next three years? Those are two different questions, and most organisations can't answer either. A strong initiative-level ROI tells you a project is defensible. It doesn't tell you it belongs among your top five. Capital spent on a second-order opportunity is capital no longer available for a first-order one — and no amount of downstream rigour recovers value that was never strategically prioritised.

The Eularis AI Strategic Blueprint models both levels: a financial case for every prioritised initiative, and a rigorously modelled ranking of which ones create the most material value against your commercial objectives — then sequences them by dependency rather than enthusiasm, with governance designed for pharma's regulatory reality. See what a board-defensible AI strategy contains → eularis.com/ai-strategic-blueprint-for-pharma

About the Podcast
AI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.

Dr. Andree Bates LinkedIn | Facebook | X

E233: The Diagnostic Room: The AI Capability Problem Pharma Hasn't Named

AI For Pharma Growth

E233: The Diagnostic Room: The AI Capability Problem Pharma Hasn't Named