Many organizations are already using AI and continue to develop new use cases across the enterprise. As adoption grows, so does the complexity: different AI technologies, applications, risk profiles, and stakeholders must all be managed within a consistent framework.
The challenge is not simply to identify promising AI use cases. Two key questions must first be addressed: How can your ambition for AI be translated into a practical strategy, and how can that strategy be embedded into existing processes, responsibilities, and controls?
Alongside internal requirements, organizations also face an evolving regulatory landscape. Regulations such as the European Union Artificial Intelligence Act (EU AI Act) and the Digital Operational Resilience Act (DORA) require clear responsibilities, transparent decision-making, and structured risk management. This is where AI governance becomes critical.
Without defined processes and accountability, AI initiatives can remain stuck at the strategic or conceptual stage. Use cases may be discussed and prioritized but fail to progress into production—or may reach implementation without adequately addressing regulatory and risk requirements.
The key task, therefore, is to integrate AI governance into existing organizational and process structures in a practical and sustainable way. This is precisely the work we performed for a major German financial institution.
We first analyzed the bank’s existing processes and identified where the use of AI introduced additional requirements. The objective was not to establish a separate governance structure, but to integrate AI governance as seamlessly as possible into the existing process landscape.
This involved close collaboration with relevant stakeholders across business units, IT teams, and control functions.
Key components of our approach included:
- AI system risk classification: A classification model determines the appropriate level of scrutiny for reviews, testing, and approvals based on the risk profile of each AI system.
- Standardized templates and evaluation logic: AI use cases can be documented in a structured and consistent way, giving control functions a common basis for assessment and decision-making.
- Advanced testing procedures: In addition to functional testing, our approach addresses AI-specific considerations such as bias, fairness, and model behavior.
- Continuous operational controls: Monitoring and review processes help ensure that model and data behavior continue to be assessed after go-live.
- Process-supporting tooling: Clearly defined workflows provide a foundation for efficient process management and create opportunities for further automation and simplification over time.
It’s also important to note that this approach deliberately goes beyond minimum compliance requirements. In addition to relevant regulatory obligations, international standards such as ISO/IEC 42001 were incorporated into the governance model. These standards provide a structured framework for managing AI systems and support the systematic establishment of governance, risk management, and continuous monitoring.
By combining regulatory requirements with recognized standards and existing organizational processes, the bank can address current compliance needs while building a scalable and sustainable governance structure for the long term.
The bank now has an end-to-end governance process covering the development, implementation, operation, and decommissioning of AI systems. Those responsible for AI initiatives have greater clarity on:
The new processes also establish clear review requirements and escalation paths, while recurring controls help maintain compliance throughout an AI system’s life cycle.
In this way, AI governance becomes the link between ambition and execution. It provides the structure organizations need to turn an AI strategy into operational reality—while maintaining transparency, managing risk. and establishing clear lines of responsibility.
CGI supports organizations in making that transition by combining AI, data, risk, regulatory, and process expertise to design governance models that work in practice. By embedding governance into existing structures rather than treating it as a separate compliance exercise, organizations can create the foundations for responsible AI adoption at scale, driving innovation while maintaining the control, accountability, and transparency required for long-term trust.
Learn more about our artificial intelligence and responsible AI experience and capabilities.
