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