Building AI Capability
Building AI Capability
What Is AI Governance, and Why Responsible AI Depends on It
Siobhán O'Leary
Making governance tangible for life sciences leaders preparing for a changing regulatory landscape.
By Siobhán O'Leary | The Institute of Applied AI | Building AI Capability series | August 2026
Start with what it is not
AI governance is not a policy document sitting in a shared drive. It is not an ethics statement on a website. And it is not a project that IT completes and hands over.
AI governance is the operating discipline that lets an organisation answer 3 questions with evidence: where AI is being used, what it is allowed to touch, and who is accountable when it influences a decision. In life sciences, where every material decision must already be traceable, that definition will feel familiar. It is quality thinking applied to a new class of system.
How governance connects to responsible AI
Responsible AI is the commitment. Governance is the mechanism that makes the commitment demonstrable.
The distinction matters because the audience for each is different. Responsible AI speaks to patients, employees and the public. Governance speaks to auditors, inspectors and regulators. One is a promise. The other is proof.
An organisation can hold sincere values about fairness, transparency and human oversight and still fail an inspection, because values do not produce records. Governance does. It converts principles into named owners, defined boundaries, review steps and audit trails. When a regulator, a client auditor or your own board asks how AI is controlled on site, the answer is either an artefact someone can open or it is a claim. Governance is the difference.
Why this matters now
The regulatory landscape around AI is no longer directional. The EU AI Act is in force and applying in stages. Transparency obligations apply. AI literacy has been a legal requirement for deployers since 2 February 2025, with national supervision live since 2 August 2026. In parallel, life sciences regulators on both sides of the Atlantic have published positions on AI in the medicinal product lifecycle, and client auditors are beginning to ask AI questions inside standard supplier audits.
For quality and validation leaders, this lands somewhere specific: audit readiness. AI use that sits outside existing quality, data integrity and change control processes is not neutral. It is an unmanaged system influencing regulated work. The organisations in the strongest position are not the ones with the most AI. They are the ones who can show their working.
What good looks like
In our work with regulated organisations, mature AI governance tends to be recognisable by 3 characteristics. It is proportionate, scaled to the risk of the use case rather than applied as a blanket. It is owned, with a named individual accountable for each AI-enabled process rather than a committee. And it is evidenced, producing records as a by-product of normal work rather than as a documentation exercise before an audit.
None of that requires a large programme to begin. It requires the decision to treat AI as something the organisation governs, rather than something that happens to it.
Our own standard
We hold ourselves to the same test we describe. In 2026, our founder and CEO, Siobhán O'Leary, achieved the Artificial Intelligence Governance Professional (AIGP) certification from the IAPP, the global body for privacy and AI governance professionals. The AIGP is the recognised international credential for developing and deploying AI responsibly, covering AI systems, the emerging legal landscape including the EU AI Act, and the practical management of AI risk.
For our clients, it means the guidance we give on governance, literacy and responsible adoption is grounded in a certified, internationally recognised body of knowledge, held to a standard set outside our own walls. Advice on accountability should itself be accountable.
About the author: Siobhán O'Leary, AIGP, is an Applied AI Advisor and co-founder of The Institute of Applied AI, helping organisations build AI capability that is grounded in literacy, governance, and practical adoption. She publishes AI in Motion, a weekly newsletter for leaders navigating AI beyond the headlines.
