AI Governance is becoming Pharma’s Next Big Compliance Challenge
As generative AI integrates itself deeper within GxP workflows, regulators are paying far closer attention to explainability, validation, oversight and accountability. The EU AI Act, along with growing FDA and EMA scrutiny, is forcing pharma firms to rethink how AI systems are monitored and controlled when deployed.
Recent FDA warning letters have already highlighted failures involving AI-generated compliance documentation that was not properly reviewed by humans, and around the world regulatory bodies are echoing the same message: AI can support regulated processes, but responsibility still sits with the humans within the organisation using it.
Against this backdrop, Research and Markets have launched a new executive training programme focused specifically on “Decision-Grade AI in GxP Environments”, to satiate the growing demand for practical AI governance frameworks. This programme is not about coding or building models, rather it is about how pharma leaders govern AI safely in practice, especially when those systems influence regulated decisions or documentation.
The training covers areas like pharmacovigilance, AI, decentralised clinical trial analytics, AI-assisted regulatory authoring, validation frameworks and explainability. So essentially, the exact operational challenges that many PV organisations are already internally grappling with. AI governance is now rapidly evolving from an IT department problem to a board-level operational challenge involving QA, compliance, regulation and risk management teams, and for good reason.
“AI adoption within pharmaceutical organisations is accelerating rapidly, but many firms are still developing the governance structures needed to support compliant and defensible use of these technologies in regulated environments.” Ross Glover, CEO of Research and Markets
AI systems are now capable of generating narratives, interpreting safety data and influencing regulatory workflows as a whole, but these systems can also behave unpredictably, evolve over time and produce outputs that are tricky to explain.
Therefore, we can see that there is a new type of compliance challenge for regulated industries. The organisations that succeed with AI over the next few years may not necessarily be the ones deploying the most advanced models. They will likely be the ones that have the strongest validation, can prove auditability, and can explain and safely govern the outputs of these systems.
