There’s a lot of hype about Causal AI in Data Science as an enabler of model explainability, which can help data scientists improve trust in the models they build for business stakeholders. But IT leaders should be thinking about the untapped potential of Causal AI too.

Causal AI can help IT and DevOps professionals be more productive, freeing hours of time spent troubleshooting so they can instead focus on building new applications. But when applying Causal AI to IT use cases, there are several domain-specific intricacies that practitioners and developers must be mindful of.

The relationships between application and infrastructure components are complex and constantly evolving, which means relationships and related entities are dynamically changing too. It’s important not to conflate correlation with causation, or to assume that all application issues stem from infrastructure limitations.

• We’ll define Causal AI, explain what it means for IT, and talk through specific use cases where it can help IT and DevOps practitioners be more efficient.
• We’ll dive into practical implementations, best practices, and lessons learned when applying Causal AI to IT and DevOps.
• Attendees will leave with tangible ideas about how Causal AI can help them improve productivity and concrete next steps for getting started.