Artificial intelligence is now embedded across financial services. It supports customer interactions, automates operational processes, informs credit and risk decisions, and increasingly influences regulated advice and support journeys. As adoption accelerates, regulators and industry leaders are focusing less on whether firms use AI and more on how it is governed.
A single, uniform set of controls is rarely appropriate for such a wide range of use cases. What firms need instead is a contextual AI governance framework. One that reflects how and where AI is used, the level of risk involved, and the potential impact on consumers and markets.
Why financial institutions need contextual AI governance frameworks
AI is used across functions with very different risk profiles. A system that summarises meeting notes presents a different level of risk to one that shapes product recommendations or automates eligibility decisions. Treating these systems as equivalent creates blind spots in oversight and control.
Recent scrutiny from UK lawmakers and regulators reflects this concern. Parliamentary committees and supervisory bodies have highlighted risks linked to opaque decision-making, automation failures, exclusion of vulnerable consumers, and systemic effects where AI is deployed at scale. These risks do not arise uniformly. They depend on context.
A contextual AI governance framework allows firms to apply proportionate oversight. It recognises that not all AI systems require the same level of scrutiny, documentation, testing, or senior oversight. At the same time, it ensures that higher-risk use cases receive the attention they demand.
What “contextual” means in AI governance
Contextual AI governance is based on how an AI system is used rather than how it is built. The same underlying technology can create very different risks depending on its role within a business process.
Context typically includes several dimensions:
- Purpose: Whether AI is used for internal efficiency, decision support, automation, or autonomous action.
- Impact: The extent to which outputs influence customer outcomes, financial decisions, or regulatory obligations.
- Risk exposure: The potential for consumer harm, bias, operational disruption, or financial instability.
- Human involvement: The degree of human oversight, challenge, and intervention in decision-making.
A contextual AI governance framework categorises AI use cases across these dimensions and applies controls accordingly. This avoids both under-governing high-risk systems and over-engineering low-risk ones.
Regulatory drivers shaping contextual AI governance in the UK
The UK has not introduced AI-specific financial services regulation, but this does not mean firms are operating in a regulatory vacuum. Existing frameworks already apply, particularly the Consumer Duty and the Senior Managers and Certification Regime.
Regulators have made it clear that firms remain accountable for outcomes, even where decisions are influenced or automated by AI. Senior managers are expected to understand the systems they oversee to a reasonable degree and to be able to evidence control.
Recent calls from lawmakers for AI-specific stress testing reinforce this direction of travel. The focus is on preparedness. Firms should be able to demonstrate that they understand how AI systems behave under pressure, how failures would be detected, and how harm would be mitigated.
A contextual approach aligns well with this expectation. It supports proportionate governance while providing regulators with confidence that higher-risk applications are subject to stronger oversight.
Core components of a contextual AI governance framework
While implementation will vary by firm, effective contextual AI governance frameworks tend to share several core components.
Risk-based classification of AI use cases
The starting point is a clear inventory of AI systems and use cases. Each should be classified based on purpose, impact, and risk. This classification drives governance requirements.
For example, a low-risk internal productivity tool may require basic documentation and periodic review. A system influencing customer eligibility or advice outcomes may require enhanced testing, ongoing monitoring, and senior approval.
Defined ownership and accountability
Every AI system should have a clearly identified business owner. This individual is accountable for performance, risk management, and compliance. Technical teams play a critical role, but accountability must sit with the business.
Clear ownership supports escalation, challenge, and regulatory engagement. It also aligns with expectations under the Senior Managers regime.
Proportionate testing and monitoring
Context determines the depth and frequency of testing. Higher-risk systems should be subject to more rigorous validation, scenario analysis, and ongoing monitoring. This includes monitoring for data drift, performance degradation, and unintended outcomes.
Stress testing plays an important role here. It allows firms to assess how AI behaves in abnormal conditions and to plan responses before issues arise.
Documentation and traceability
Documentation should reflect context. Firms should be able to explain why a particular level of governance is applied to a given system. This includes rationale for classification, design decisions, and control measures.
Traceability is particularly important where AI influences regulated outcomes. Firms need to evidence how decisions are made and how risks are managed.
Integrating contextual AI governance into existing risk and compliance models
One of the most common pitfalls is treating AI governance as a standalone programme. This often results in parallel processes that are difficult to maintain and poorly aligned with existing controls.
A contextual AI governance framework should be embedded into existing risk, compliance, and operational resilience structures. This includes:
- Aligning AI risk assessments with conduct and operational risk processes.
- Incorporating AI oversight into established committees and reporting lines.
- Mapping AI controls to existing policies under Consumer Duty and operational resilience.
This integration reduces complexity and supports consistency. It also makes governance more resilient as regulatory expectations evolve.
The role of senior leaders in contextual AI oversight
Senior leaders do not need to understand the technical detail of every AI model. They do need to understand how AI is used within their areas of responsibility, what risks arise, and how those risks are controlled.
A contextual framework supports this by surfacing the systems that require senior attention and by providing clear, structured information about their operation and impact. This enables informed oversight without technical micromanagement.
Crucially, it also supports accountability. When roles and responsibilities are clearly defined, decision-making becomes more transparent, both internally and to regulators.
Preparing contextual AI governance frameworks for future guidance
Regulatory guidance on AI will continue to develop. Firms that rely on rigid, prescriptive frameworks may struggle to adapt. Contextual governance offers greater flexibility.
By focusing on principles, risk assessment, and proportionality, firms can evolve their controls as expectations change. Stress testing and scenario analysis can be expanded or refined without redesigning the entire framework.
This approach supports innovation. It allows firms to deploy AI where it adds value, while maintaining confidence that risks are understood and managed.
What effective contextual AI governance enables in practice
A well-designed contextual AI governance framework does more than reduce regulatory risk. It provides clarity for teams, confidence for senior leaders, and reassurance for regulators and customers.
By governing AI according to how it is used and the outcomes it influences, financial institutions can scale AI responsibly, protect consumers, and maintain trust in an increasingly automated environment.
