Ready to move beyond AI pilots and drive real production value in 2026?

The shift from experimental AI projects to enterprise-wide, reliable AI is the defining challenge for data and AI leaders this year.

In this exclusive CDO Magazine webinar, top data leaders share their predictions, lessons learned, and core strategies for making AI reliable at scale.

In this session, you’ll gain practical approaches to tackling the three primary challenges facing data and AI organizations today:

• AI foundations: Scaling complex data environments and managing your data estate holistically.
• Using AI in your processes: Leveraging AI to dramatically increase productivity, especially in incident response and troubleshooting.
• Building AI: Delivering reliable, trusted, and well-adopted AI agents in production, requiring observability across your entire data + AI estate.

Hear game-changing insights from our expert panel:

• Gurpreet Atwal, Chief Data Officer at Truist
• Subha Madhavan, VP & Head, AI/ML, Quantitative & Digital Sciences, R&D at Pfizer
• Maddie Daianu, Head of Data & AI, Intuit Credit Karma
• Moderated by: Barr Moses, Co-Founder and CEO, Monte Carlo

Key Topics Covered:

• How industry leaders define “AI readiness” today and where that definition is headed over the next five years.
• The critical differences between deploying AI vs. traditional ML — and when to choose each approach.
• The essential first steps to ensure your data estate can support ambitious AI initiatives.
• Which “good enough” data practices of the past will become unacceptable in the AI-driven workflows of 2026.
• How to prioritize data quality metrics and communicate the value of AI reliability to business stakeholders.