A blog about Product Management, Data Management and Self-Driven Learning
Use case retirement is essential for AI governance, ensuring AI initiatives are reviewed and sunsetted when no longer valuable or risk-managed. Organisations often avoid this due to a lack of triggers for reassessment. Continuous improvement in governance acknowledges ongoing gaps as signs of a healthy program and fosters proactive data management and strategic use case evaluation.
An AI use case register serves as a centralised inventory of AI initiatives, enabling visibility for executives and business teams to avoid duplicate investments. Essential fields track various aspects of each use case. Regular updates ensure accuracy, while a future "agent checkout" model allows low-risk use cases to be self-served, streamlining governance.
Implementing a standardised scoring model for AI use case triage expedites decision-making and enhances risk evaluation. By objectively assessing ideas on business value, feasibility, risk, and alignment, organixations can filter out non-AI requests and identify true AI opportunities, fostering organisational learning and refining data governance practices over time.
This content discusses the integration of AI into the DASUD framework, emphasising the importance of defining problems and capturing AI use cases effectively. It outlines a structured intake process and a tiered approval workflow to streamline AI implementation while ensuring governance. The goal is to create organisational learning and maximise return on AI investments.
Data governance experts often struggle to assert themselves in AI governance roles, viewed primarily as data specialists. By reframing their existing skills - such as data lineage and compliance - into relevant AI governance language, they can articulate their value. Clear positioning statements can help them demonstrate their ability to lead AI initiatives confidently.
Nigel D'Souza 