What is AI Governance
AI is now embedded across UK financial services marketing teams. It drafts content, routes workflows and personalises communications at scale. But the frameworks for overseeing these systems have not always kept pace with their adoption. AI governance is the structure that closes the gap.
Let’s start with a short definition.
AI governance refers to the policies, controls and accountability structures that organisations put in place to ensure AI systems operate reliably, transparently and within regulatory obligations.
It covers the full lifespan of an AI system: how it is selected, deployed, monitored and decommissioned. It applies to systems built in-house and to functionality embedded in third-party tools.
Governance does not restrict what AI can do. It ensures the people deploying it remain accountable for what it produces.
The core principles
The UK government’s approach is principles-based and sector-specific. Rather than creating a single AI rulebook, it asks existing regulators to apply five core principles proportionately across their remit.
Safety and robustness. AI systems must function reliably across their lifecycle. Risks should be identified and managed on an ongoing basis.
Transparency and explainability. Organisations should be able to explain clearly how and why an AI system is being used. Decision-making should be understandable to the people affected by it.
Fairness. AI systems must not discriminate unfairly or produce outcomes that systematically disadvantage particular groups.
Accountability. Clear lines of responsibility must exist across the AI lifecycle, covering both the development and use of AI systems.
Contestability. Where AI influences a decision, there must be a clear route to challenge or appeal that outcome.
These principles do not apply at the same weight for every system. A tool used to draft marketing copy carries different governance requirements to a model used in credit decisioning. Proportionate application is the expectation.
Governance across the system lifecycle
Governance applies at every stage of an AI system’s life, not just at the point of deployment.
Organisations need a clear inventory of the AI systems they operate, including tools embedded in third-party platforms. Before deploying a system that could affect consumer outcomes, firms should document potential harms and the steps taken to address them. AI systems also need regular auditing. As real-world data shifts, model performance can degrade in ways that introduce bias or reduce accuracy.
AI governance in UK financial services
The FCA has confirmed it does not plan to introduce AI-specific regulation in the near term. Its focus is on whether firms can demonstrate effective oversight of systems that influence consumer outcomes.
For UK regulated firms, AI governance operates within an existing regulatory framework. The FCA and PRA have not introduced AI-specific rules. Existing obligations apply in full.
Financial promotions must be clear, fair and not misleading, whether content is human-authored or AI-assisted. Consumer Duty requires firms to avoid foreseeable harm and support good customer outcomes. Under SM&CR, accountability sits with named individuals, not systems or vendors.
Third-party oversight is a significant and often underestimated area. Where AI functionality is embedded in supplier platforms, firms remain responsible for the outcomes those systems produce.
What this means for marketing and compliance teams
Marketing sits at the intersection of several governance requirements. Financial promotions are subject to FCA scrutiny. Content volume is increasing. AI is being introduced into drafting, routing and approval workflows, often without a consolidated view of where it operates or how outputs are validated.
For compliance teams, the concern is whether that activity is visible, defensible and evidenced. For marketing teams, it is whether the workflow can support the pace and volume of output being asked of it.
Both questions point to the same gap: the distance between what a firm’s AI systems are doing and what its governance framework can see, evidence and defend.
Closing that gap requires visibility over where AI is active, clear accountability for the outputs it influences and evidence that does not need to be reconstructed after the fact.
A practical starting point
Our new report Governing AI in financial promotions sets out a fuller framework for how firms should structure AI governance across the financial promotions lifecycle, including the practical distinction between deterministic automation and generative AI and where each belongs within a controlled workflow.
The accompanying board paper also provides a concise summary for senior leaders.