AI Governance: Control and Trust Over AI in Your Organisation

AI governance refers to the internal policies, oversight structures, and accountability mechanisms an organisation puts in place to ensure that artificial intelligence systems are developed and deployed responsibly, lawfully, and in alignment with the EU AI Act (Regulation 2024/1689) and sector-specific regulations. In the EU, the AI Act requires providers and deployers of high-risk AI systems to maintain documented governance frameworks, including risk management systems and human oversight procedures. Organisations without adequate AI governance face regulatory sanctions, reputational risk, and potential liability for algorithmic decisions that affect individuals.

Our AI governance team combines legal expertise in digital regulation with practical knowledge of machine learning systems and software development processes.

The Oversight Gap

Artificial intelligence has penetrated business processes far faster than internal oversight structures have developed. Organisations make critical decisions — about hiring, credit, pricing, customer service — using models whose internal workings are not transparent to the executives who are accountable for those decisions. This gap between AI adoption and supervisory capacity is the fundamental governance problem we address.

Starting with the Inventory

An effective AI governance framework begins with knowing which systems exist. The corporate AI inventory is surprisingly incomplete in most organisations: systems purchased from external vendors are rarely formally registered, models developed by data science teams are not always documented in a way accessible to compliance functions, and AI tools embedded in third-party applications are frequently invisible to risk officers. Opacity about your own AI technology estate is the starting point for most regulatory and reputational problems.

The Ethics Committee as Decision Authority

The AI ethics committee is the central oversight mechanism — not a merely consultative body, but the decision point on whether a new system may be deployed, under what conditions, with what human oversight mechanisms, and with what periodic review schedule. When a regulator investigates an AI-related incident, the existence of a functioning committee with records of its deliberations is the most powerful evidence of organisational due diligence. We design these committees with clear mandates, balanced composition across legal, technology, and business functions, and procedures that do not obstruct innovation while maintaining meaningful control.

Algorithmic Auditing and Bias Detection

Algorithmic auditing and bias detection are the technical controls that give substance to the governance framework. Analysing whether a recruitment model produces systematically higher rejection rates for women or candidates from certain ethnic backgrounds is not a theoretical exercise: it is an obligation arising from the AI Act, the GDPR, and existing anti-discrimination law. We develop audit methodologies adapted to each type of system and coordinate the process with internal data teams or system providers. For organisations subject to AI Act compliance requirements, these audits also serve as evidence of the continuous post-market monitoring obligations applicable to high-risk systems.

AI Governance as a Commercial Asset

Robust AI governance is increasingly a prerequisite in commercial relationships. In financial services, healthcare, and professional services, large institutional clients and corporate buyers conduct due diligence on their suppliers’ AI systems as part of third-party risk management. An organisation with a robust governance framework, an up-to-date inventory, and documented AI policies holds a significant advantage in these evaluations over competitors who cannot demonstrate control over their own systems.