Companies today are struggling with how to capture and record the tools, models and vendors their employees are using to conduct essential AI governance and reduce risks. To build a maintainable AI inventory, every institution requires the most foundational step of AI governance: deploying a clear AI use case intake process. However, perfection is relative when it comes to build a good AI use case intake process.

If your company’s process requires too much information from business owners upfront, it could result in poor quality, confusing responses, and teams avoiding starting the process altogether. On the other hand, if your process does not capture enough data, you risk missing important context to address relevant governance concerns. Once the process is complete, the question of how much work your teams need to do to review that information and use it remains.

This panel discussion will feature AI governance leaders sharing their tips, tricks and pitfalls for how they approached the challenge of building effective intake processes. Through case studies and perspective sharing, panelists will present how they built their initial intake processes, the lessons from trialing these processes with stakeholders, and how they integrated, adjusted and scaled their process. Attendees will come away from the panel with a better understanding on how to balance concerns and implement an effective AI use case intake process.

What you will learn:

The types of information that can and should be collected upfront during the intake process, and the breakdown between business, technical, and governance teams.
Risks, pitfalls, and objections from stakeholders that can lead to less effective intake processes, and how to mitigate them through collaboration.
How to implement a ‘tiered’ intake process that quickly identifies high risk/impact use cases that need more governance inputs.

Sponsored by: Trustible

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