E231: The Diagnostic Room: You didn't have an AI problem. You had a capability problem.

<p>In this solo episode of AI For Pharma Growth, Dr Andree Bates explores why many pharma teams do not have an AI problem at all. They have a capability problem.</p><p>Dr Andree starts with a simple question: when was your team last properly trained on AI for their specific role? Not when they were given access to tools, licences or a generic use policy, but when they were trained to use AI effectively, safely and compliantly in their actual workflow.</p><p>The episode challenges the usual explanations for disappointing AI results: the model was not good enough, the vendor was wrong, the data was not ready, or the organisation resisted change. In many cases, the tools work, the pilots are useful and the training lands. But the working knowledge needed to use AI well is uneven, fragile and decays over time.</p><p>Dr Andree explains why this matters so much in pharma. High-value AI work is often judgement-led: medical information responses, payer materials, safety narratives, regulatory documents and MLR-compatible content. AI can support these tasks, but only when users can tell the difference between a strong draft and a merely plausible one.</p><p>She also discusses the research behind skill decay, including why cognitive and accuracy-dependent skills fade faster than simple speed-based or physical skills. That is especially important in pharma, where the cost of a confident but wrong output can become a compliance, regulatory or patient safety issue.</p><p>The key message is clear: AI capability is not something you achieve once. It has to be maintained. The functions that lead in AI will not simply be the ones with the most licences or training events. They will be the ones that treat capability as something with a rate of decay and build systems to keep it current.</p><p>Topics Covered</p><ul><li><p>Why AI underperformance is often a capability problem</p></li><li><p>The difference between access, policy and real training</p></li><li><p>Why confident AI use varies across teams</p></li><li><p>AI in judgement-led pharma workflows</p></li><li><p>Skill decay and why 90 days matters</p></li><li><p>Why high-value AI workflows are often forgotten fastest</p></li><li><p>The risk of outdated working knowledge</p></li><li><p>Why training is ignition, not maintenance</p></li><li><p>The limits of AI champions and internal portals</p></li><li><p>Three questions to ask your function this week</p></li></ul><p>Eularis helps pharma and biotech leaders turn AI activity