Designing AI-Ready Data Platforms for Enterprise Scale
Webinar Speaker: TBD
Date: Tuesday, February 17, 2026
Time: 9:00 a.m. PT / 12:00 p.m. ET
As organizations accelerate investments in artificial intelligence, many are discovering that AI success depends less on individual models and more on the strength of the underlying data platform. To support enterprise-scale AI and advanced analytics, data platforms must deliver trusted, governed, and accessible data while remaining flexible enough to support rapidly evolving workloads. Yet many enterprises continue to struggle with fragmented architectures, siloed data, and legacy platforms that were never designed for modern AI use cases. As a result, analytics and data teams face challenges scaling experimentation into production, maintaining governance and security, and balancing performance, cost, and agility across the organization.
In this TDWI webinar, we’ll explore what it takes to design and operate AI-ready data platforms at enterprise scale. Drawing on TDWI research and real-world enterprise examples, the session will examine architectural patterns, governance approaches, and operational practices that enable organizations to support analytics, machine learning, and emerging AI workloads on a unified foundation—while preparing for future innovations such as autonomous and agentic AI systems.
Join TDWI and Google as they discuss how organizations are modernizing their data platforms to improve data quality and trust, simplify access across hybrid and multicloud environments, and move AI initiatives from experimentation into production.
Topics include:
- What “AI-ready” really means for enterprise data platforms beyond individual models
- Core architectural patterns for supporting analytics, machine learning, and AI workloads at scale
- How unified data access, governance, and security enable broader AI adoption without increasing risk
- Operational considerations for scaling AI experimentation into production across teams and use cases
- Preparing data platforms to support future innovations such as autonomous and agentic AI systems
Date: February 17, 2026
Time: 9:00 am PT