AI has moved from experimentation to expectation, but most data architectures haven’t kept up.

Across industries, organizations are racing to scale AI initiatives, only to encounter the same hard truths: data that can’t be trusted, models that can’t be explained, pipelines that don’t hold under pressure, and governance that slows everything down instead of enabling it. The issue isn’t a lack of tools or talent; it’s that the underlying architecture wasn’t designed for the speed, scale, and ambiguity AI introduces.

This candid keynote panel brings together enterprise data leaders, architects, and governance experts to examine what actually breaks when AI moves beyond the pilot phase and why. Building on the day’s themes of foundational design, data modeling, and context, the discussion will surface the architectural gaps that organizations consistently overlook, from fragmented control planes and missing metadata to unclear ownership and inconsistent meaning across systems.

Panelists will share real-world lessons learned, the decisions that created leverage, the ones that created risk, and what they wish they had addressed earlier. More importantly, they’ll explore what “AI-ready” actually looks like in practice, and how organizations can move from reactive fixes to intentional, scalable architecture.

Attendees will leave with:

• A clear understanding of where most architectures fall short in AI environments
• Insight into the architectural decisions that matter most under real-world pressure
• Practical guidance for aligning data, context, governance, and execution at scale

This is the moment to move beyond AI ambition and confront the architectural reality required to make it work.

Speakers:

MODERATOR: Sara Nash
Practice Lead, Semantic Engineering and AI
Enterprise Knowledge, LLC

Sara is the Practice Lead for Semantic Engineering and AI at Enterprise Knowledge LLC. She specializes in Semantic Layer and Enterprise AI initiatives, leading throughout strategy, design, and implementation. Sara has led taxonomy/ontology design, knowledge graph implementation, recommendation engine solutions, and advanced analytics platforms across financial services, pharmaceuticals, ESG consulting, intelligence, and more.

Panelist 1

Kevin Fair
Sales Leader, Information Architecture Data Integration
IBM

Kevin Fair is a seasoned sales leader with over 27 years of experience in the Data Integration and Governance space. Since joining IBM in 2005 through its acquisition of Ascential Software, Kevin has held multiple senior roles across Sales, Strategy, and Subject Matter Expertise. He has been a key contributor to the growth and success of IBM’s Data Integration portfolio, including IBM DataStage and IBM Replication offerings. Kevin brings deep technical expertise and strategic insight to help clients unlock the full value of their data 

Panelist 2

Aashoo Saxena
VP of Product Management
Informatica

Aashoo Saxena is VP of Product Management at Informatica. He leads the Cloud Modernization initiative and is focused on delivering migration automation, best practices, and self-service tools & methodologies to help customers with their journey to cloud.

Panelist 3

Niruta Talwekar
Data Engineer
Meta

Niruta Talwekar is a Data Engineer at Meta with experience in building large-scale data and AI systems. She holds a Master's in Computer Information Systems from Georgia State University and is an IEEE Senior Member. Her work focuses on fairness engineering, trust in AI, and ethical automation. She was nominated for the 2025 Women in Tech Global Awards and actively mentors with Rewrite the Code.