Generative and agentic AI initiatives are exposing a critical reality: Many enterprise data platforms were built for analytics, not for AI systems that need to reason, adapt, and act in real time. Join Forrester VP and principal analyst Noel Yuhanna for an interactive session on the Data Platform AI Readiness Tool, a practical assessment designed to help organizations evaluate whether their data platform is ready to support AI at scale. During this webinar, attendees will learn how to assess their capabilities across key areas including data foundations, architecture, AI data access, model lifecycle management, agentic AI, and governance. The session will demonstrate how to interpret assessment results, identify readiness gaps, and prioritize investments required to build a scalable, AI-ready data platform.Key takeaways: Assess your organization's readiness to support generative and agentic AI on its data platform.Evaluate capabilities across data foundations, architecture, AI data access, governance, and agentic AI.Target audience level: all levels

The biggest takeaway from Snowflake Summit 2026 wasn’t another AI announcement; it was a fundamental shift in what enterprises should expect from a data platform for AI. The conversation has moved beyond building models and copilots to operationalizing agentic AI at scale. It requires more than AI infrastructure; it demands a lakehouse capable of delivering […]

The enterprise data lakehouse is evolving. Once designed primarily to consolidate data for analytics, today’s lakehouse has become the operational foundation for agentic AI, delivering the trusted, governed, and real-time data that intelligent agents require to reason and act. As AI shifts from generating insights to executing business processes, organizations must rethink what they expect […]

AI is exposing a fundamental flaw in today’s data landscape: The decades-old practice of copying data wherever it needs to be consumed is becoming economically and operationally unsustainable. As organizations scale AI across increasingly distributed clouds, platforms, and business domains, they face escalating challenges related to data sprawl, governance, performance, and cost. This report examines the emerging zero-copy data architecture and how it enables organizations to deliver seamless, governed access to data while reducing complexity, accelerating insights, and supporting enterprise-scale AI.

In our evaluation of data lakehouse providers, we identified the most significant ones and researched, analyzed, and scored them. This report shows how each provider measures up and helps you select the right one for your needs.

The Databricks Data + AI Summit 2026 signals a shift from experimentation to enterprise-scale, agentic AI. With over 30,000 attendees (representing a 36% annual increase) and global participation across more than 150 countries, Databricks is positioning itself as a foundational platform for data-intelligent applications. Last year, we wrote that Databricks went “beyond the lakehouse” by […]

As enterprises accelerate investments in generative and agentic AI, many are realizing their data platforms aren’t built to keep up. While models and agents are easier than ever to adopt, most data infrastructure remains optimized for analytics rather than for real-time AI systems that can reason, adapt, and act. This assessment tool helps you quickly determine whether your data platform is ready to support AI at scale and pinpoints the critical gaps you need to close to turn AI ambition into operational reality.

Agentic AI is exposing a foundational gap in most enterprise data strategies: Data without meaning is unusable for autonomous systems. Agents don’t just retrieve data — they interpret, decide, and act. Without explicit context, they guess. And when agents guess, they get joins wrong, misinterpret metrics, and act on flawed assumptions. This is why ontologies, […]

Google Cloud Next 2026 opened with Thomas Kurian declaring the end of the AI pilot era and Sundar Pichai comparing the enterprise refrain of last year (“Can we build an agent?”) to today’s: “How do we manage thousands of them?” Google Cloud Next ’26 answers the second question with a single product story: Gemini Enterprise […]

Organizations face growing pressure to scale AI and analytics, yet fragmented data models and inconsistent definitions remain major obstacles. Modern data modeling is evolving to enable shared semantics, real-time representations, and unified meaning across distributed environments. The next generation of data modeling will be intelligent, automated, and domain-aware, adapting to evolving data platforms and use cases. This report explores the future of data modeling and key considerations for building scalable data foundations for AI.