Most AI transformations fail not because of technology, but because organizations lack the structural frameworks to deploy, monitor, and govern autonomous systems effectively. Drawing from deploying production AI systems for multiple companies, this session presents a battle-tested approach to AI transformation that balances innovation velocity with operational safety.

Through the lens of AURA (Agent Autonomy Risk Assessment), an open-source framework I co-developed for monitoring and deploying autonomous agents in production, we'll dissect three critical phases of AI transformation: Assessment (measuring AI readiness and usefulness across teams and infrastructure), Implementation (deploying systems that deliver measurable ROI), and Governance (maintaining oversight without throttling innovation).

Key takeaways include:
- An AI readiness and safe implementation assessment that identifies organizational gaps before they become expensive failures
- Metrics from production deployments
- Practical AI oversight mechanisms
- Case studies from implementing AI systems

CTOs will leave with immediately actionable models of AI implementation, allowing to leverage this revolution to improve company processes in a responsible way.
If you’re fielding pressure to become an “Agentic, AI-first, AI-powered, AI-everything” company and trying to separate hype from value, this session provides the pragmatic roadmap for leading AI transformation that delivers ROI while managing the unique risks of autonomous systems.