You need enterprise analytics and AI to reduce non-fuel O&M, lower Value-at-Risk, and improve gross margins—not produce more pilots. That requires unified data, native governance across your workloads, and an architecture that stays open as your tools evolve.

In this session, we'll show how leading utilities are operationalizing advanced analytics and AI by unifying data into a single, governed foundation. On that foundation, analytics teams deploy production-scale models using their preferred frameworks, tools, and partners—choosing their own AI within governance that satisfies enterprise risk requirements from day one. Through practical examples, you'll see organizations shift from slow, ungoverned experimentation to fast, controlled iteration—where native governance eliminates the tradeoff between speed and control. You'll leave with clear enterprise-level financial outcomes tied to controllable metrics and a decision framework for your next architectural investment.

Session Takeaways:
•    Assess whether your current data architecture can support AI workloads at enterprise scale without custom infrastructure
•    Describe the native governance capabilities required to scale AI across business functions while satisfying risk and compliance requirements
•    Design an evaluation framework for your next platform investment tied to controllable financial outcomes