Case Study:
AI Risk Management Training
Innovative Training for Building AI Risk Management Capability
The Challenge
In response to growing regulatory scrutiny and the rapid adoption of AI technologies, a major financial services organisation sought to uplift its workforce’s capability in understanding, identifying, and mitigating AI-related risks.
The organisation faced several challenges: AI was being integrated into business-as-usual processes, however there was inconsistent application of risk management practices across the broader business. Regulatory requirements were evolving, and the business needed to ensure employees could make informed decisions about AI initiatives, recognise potential risks, and comply with internal standards and external obligations.
The client required an engaging, practical, and scalable AI risk training program that would address specialist risk management requirements across a diverse employee base.
Our Approach
To ensure both depth and accuracy, risk subject matter experts and stakeholders from multiple lines of defence provided insights and iterative feedback into the design, shaping the content to align with regulatory and business needs. This close collaboration with SMEs and stakeholders was vital in ensuring the training remained accurate, relevant, and compliant.
Scenarios were co-created with experts to be embedded within the learning. Embedding scenario-based learning was critical in highlighting the potential real-world impact of AI risks and the critical need for effective controls, making the training highly relevant and practical. Scenario development drew on investigations of historical and peer-reported events.
In our design, we ensured integration and alignment with pre-existing guidance documentation and Standards, ensuring learners had access to relevant policies and practical job aids throughout their training.
Outcomes
The final program comprised four stand-alone modules, each lasting between 5–15 minutes and designed to be completed individually or in sequence. Interactive activities were embedded within each module, equipping employees to apply risk management standards in their daily roles.
Following release, the modules received a strong reception from learners, earning an average rating of 4.9 stars and attracting positive feedback.
Employees emerged better equipped to identify AI risks, understand interconnected dependencies, and apply the organisation’s risk management standards in practice.
Conclusion
The program delivered an uplift in employee capability, enhancing the organisation’s ability to mitigate AI risks and meet evolving regulatory obligations. Overall, this targeted and flexible training initiative empowered financial services employees to manage emerging risks with confidence.