Organizations are investing heavily in AI initiatives, yet many struggle to move beyond pilots into durable enterprise adoption. The issue is rarely model accuracy. It is fragmented data foundations, unclear ownership, and governance gaps that surface once AI systems intersect with real operational complexity.

In this session, Ashley Rivera examines why technically sound AI solutions still fail to create decision confidence. Drawing from enterprise experience leading multi division data architecture and automation initiatives, she will outline practical architectural principles that align data design, integration boundaries, and operating models with executive expectations. Attendees will gain a structured lens for designing AI systems that scale responsibly and earn long term trust.

You will learn:

• How to evaluate whether existing data architecture can responsibly support enterprise AI initiatives at scale.
• How to identify architectural, ownership, and governance gaps that erode trust in AI driven decision systems.
• Design integration and operating models that align AI capabilities with executive decision making and long-term sustainability.