Shipping AI Is Easy. Running It Is Hard: Building AI Systems That Last
About this session
Building an impressive AI demo is easier than ever. Building an AI system that continues making reliable decisions months after deployment is an entirely different challenge.
As organizations race to adopt copilots, agentic AI systems, and intelligent workflows, many discover that the biggest obstacles aren't the models themselves—they're decision quality, governance, accountability, feedback loops, and organizational readiness. Success with AI increasingly depends not only on what we build, but on how we operate, evaluate, and continuously improve these systems as business needs evolve.
Drawing on real-world experience building enterprise AI systems at scale, this session explores the organizational and technical patterns that separate AI initiatives that create lasting business value from those that struggle after deployment. Attendees will learn a practical framework for designing AI systems that remain reliable, trustworthy, and adaptable over time. Whether you're shaping AI strategy, leading product teams, or deploying agentic AI across the enterprise, you'll leave with actionable insights for building AI systems that deliver sustainable business impact.
Speaker
Key takeaways
- Recognize the organizational and operational challenges that cause AI initiatives to lose effectiveness after deployment, even when the underlying models perform well.
- Learn a practical framework for designing AI systems with strong governance, clear decision boundaries, accountability, and continuous feedback.
- Understand how to build and operate trustworthy AI systems that continue delivering business value as users, data, and business priorities evolve.