AI governance: transforming compliance into a lever for innovation

The Challenge

A large international banking group was engaged in a transformation accelerated by the rise of artificial intelligence across its business lines, from asset management to private banking and core support functions, and found itself facing several critical challenges. AI use cases were multiplying without a common framework, which exposed the organization to increased risks in terms of bias, security, compliance and transparency.

This rapid evolution was also taking place in a context of growing regulatory pressure, notably with the European AI Act and the ISO 42001 standard. The risks at stake were multi-faceted: algorithmic bias and ethical impacts, data security and confidentiality, insufficient transparency and traceability, and regulatory non-compliance.

In this changing landscape, the main challenge was to drive innovation while maintaining trust, risk control and consistency of practices. Before the project, governance was fragmented and dependent on local initiatives, AI risks were poorly identified with no centralized registry, the group found it difficult to respond to audits and regulatory requirements, and innovation was hampered by fear of risk. Management therefore wanted to move from an opportunistic approach, fragmented across entities, to structured, industrialized governance capable of anticipating regulatory and ethical requirements. At this critical turning point, governance became not just a safeguard, but a strategic imperative.

The Solution

Talan mobilized a multidisciplinary team spanning Data & AI, Risk, Compliance, IT and Change. The team began by mapping AI uses and the associated risks across the entire group, then co-developed an AI governance framework: a group policy defining principles, roles and responsibilities, and coordination with data governance.

Operational tools were then developed: an AI model registry, risk matrices, validation procedures and compliance checklists. A monitoring and reporting system was deployed to ensure traceability, auditability and continuous improvement, and teams across the business lines, IT and compliance were trained and acculturated to AI issues and to the new governance. Together, these elements form a comprehensive framework that integrates naturally into existing data governance and risk management systems.

The approach was based on collaborative workshops, the integration of international standards (ISO, OECD, EU AI Act) and adaptation to local specificities. Its strength lies in an integrated vision: AI governance is designed as a natural extension of data governance and risk management. The tools, templates and processes created enable industrialization across the group, while remaining pragmatic and adapted to operational realities. The involvement of stakeholders from the design stage, combined with a tailor-made acculturation and training strategy, ensured rapid and sustainable adoption. The aim was to industrialize practices for sustainable and scalable adoption.

The Impact

The engagement replaced a fragmented approach, dependent on local initiatives, with a harmonized AI governance framework validated by management. The group now has a centralized registry of AI models, a map of AI risks with the associated controls, and automated procedures for validation, monitoring and reporting.

The organization is now able to anticipate and demonstrate its compliance, notably with the EU AI Act, whereas it previously found it difficult to respond to audits and regulatory requirements. Responsible innovation has accelerated, and the confidence of both the business lines and clients has been strengthened, where innovation was previously hampered by fear of risk.

AI governance, meaning the rules, processes, roles and tools that regulate the development, use and control of AI systems, aims to ensure regulatory compliance, risk management and responsible adoption within the organization. Strong governance enables risks such as bias, security, insufficient explainability and regulatory non-compliance to be identified, mitigated and monitored. When well thought out, AI governance becomes a lever for compliance, trust and innovation: a harmonized framework makes it possible to control risks while unlocking the potential of AI applications, and the industrialization of processes and the upskilling of teams are essential conditions for sustainable adoption. With this approach, Talan helps organizations transform AI governance into a real competitive advantage.