Building Smarter AI Oversight: Boost Compliance and Operational Success

Challenge

A lack of oversight on AI use-cases

As our client's data science initiatives expanded across multiple teams, they faced a critical challenge: there was no centralized way to track and manage all data science and AI use cases throughout the organization. Management recognized this gap and requested a central inventory system for all data science use cases. The key issues faced were:

1. Operational inefficiency: teams were unknowingly duplicating work because they could not see what other departments were developing. This wasted resources and created inconsistent practices across projects addressing similar problems. Without visibility, it was impossible to reuse components or standardize quality across initiatives.
2. Strategic misalignment: AI projects were consuming significant resources without assurance of alignment with the data strategy. Without central coordination, teams worked in isolation, often focusing on lower-priority problems while missing opportunities to collaborate on strategic initiatives.
3. Risk Management gaps: Without a comprehensive view of all AI systems, compliance risks were growing undetected. The organization needed a standardized review process to consistently manage AI-related risks across all projects.
4. Data Quality issues: When data quality issues arose, the organization couldn't trace how these problems affected model predictions. There was no way to connect issues in data sources to model outputs, making it impossible to determine which AI systems might be affected by specific data problems.

Approach

A phased implementation of model inventory

We implemented a gradual approach to building AI governance, starting with a simple declarative inventory that grew more sophisticated over time:

Phase 1: Requirements Analysis based on AI lifecycle

We recognized that AI projects have distinct phases: ideation, research & development, industrialization, and decommissioning. Each phase requires different documentation standards, with certain information being mandatory or optional depending on the project stage.We assigned data scientists and AI translators (a bridging role between technical and business) - as the primary owners of this documentation since they had the most direct knowledge of the projects. Throughout the governance process design, we aligned our approach with industry standards and regulatory requirements.

Phase 2: Implementation in Collibra

Since the client was already using Collibra for data governance, we extended this platform to host the use case inventory. We trained AI translators to document their existing use cases through standardized workflows. This created the foundation for a comprehensive catalog of all AI initiatives.

Phase 3: Extension of metadata and automation with integrations to model development environment

After establishing the basic inventory, we connected it to the client's Data Science Platform to capture technical metadata about the models. This integration allowed us to track model versions automatically and link technical details to business context. By connecting development environments with the governance system, we automated metadata collection during development to reduce the documentation burden of data scientists and to ensure quality and consistency of meta-data.

Impact

A steerable AI organization

What started as a simple request for an AI use case inventory grew into a comprehensive governance framework that transformed the organization’s AI landscape.A centralized inventory now tracks over 300 AI models across the three main development teams, integrating both business and technical metadata. This has delivered value across four key areas:

• Development teams now have visibility into each other’s work, significantly reducing duplicated efforts and enabling reuse of components. This promotes consistency and speeds up development.
• Leadership can now steer AI initiatives based on clear priorities and alignment with the broader data strategy. Teams collaborate more effectively and focus on high-impact use cases.
• The inventory supports a standardized risk review process and provides a complete view of all AI systems, enabling better oversight and helping prepare for EU AI Act compliance.
• By linking models to their data sources, the organization can quickly identify which systems are impacted by data quality issues, improving reliability and trust in AI outputs.

What began as a tactical solution has become a strategic asset—enabling smarter decisions, safer AI, and greater impact across the organization.Let’s explore how we can streamline your AI processes and strengthen governance, together.