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 from their lakehouse platform. This evolution redefines vendor evaluation, prioritizing AI readiness, trust, openness, and operational intelligence over storage and query performance.
New Lakehouse Capabilities Support Agentic Use Cases
To help organizations navigate this shift, Forrester evaluated 14 leading lakehouse vendors in its latest Data Lakehouse Wave report. The evaluation reflects the changing role of the lakehouse in the AI era and identifies the capabilities that will distinguish platforms capable of supporting enterprise-scale agentic AI. While traditional data management capabilities remain important, the report makes it clear that future-ready lakehouses must also serve as the execution layer for AI-driven applications.
The key takeaways from the evaluation are that:
The lakehouse is becoming an execution layer for agentic AI. The Data Lakehouse is no longer limited to storing and serving data for analytics. Instead, it must continuously provide trusted, governed, real-time context that AI agents can use to reason, decide, and act. This shift fundamentally changes how organizations should evaluate vendors. Rather than prioritizing storage-centric capabilities alone, buyers should assess how effectively a lakehouse supports AI-native workloads, real-time operations, and integration with enterprise AI ecosystems.
Trust is the foundation of an AI-ready lakehouse. As AI agents increasingly make autonomous decisions, weaknesses in data quality, governance, lineage, or security become execution risks rather than analytical limitations. Organizations should prioritize lakehouse platforms that embed governance, automated lineage, fine-grained access controls, continuous data quality monitoring, and policy enforcement as core platform capabilities.
Open architectures are critical for long-term AI success. AI is evolving rapidly, requiring organizations to integrate multiple models, orchestration frameworks, cloud environments, and data services. Lakehouses built on open table formats, interoperable metadata standards, extensible APIs, and zero-copy data sharing provide the flexibility needed to adapt while reducing vendor lock-in. Vendor evaluations should therefore consider ecosystem interoperability and architectural openness as strategic differentiators rather than optional features.
AI enablement is the new competitive differentiator for lakehouse platforms. Storage, scalability, and query performance have become table stakes. Today, Lakehouses distinguish themselves by delivering real-time context, semantic intelligence, vector-native capabilities, and AI-ready data services that enable autonomous agents to retrieve, reason, and act on enterprise data. Organizations should evaluate vendors based on how effectively their platforms operationalize AI, as this capability will define enterprise value and competitive advantage in the next generation of data platforms.
Interested in learning more about the evolving lakehouse landscape and which platform is the best fit for your organization? Read the latest Wave report and schedule a guidance session with me. We can help identify the capabilities that matter most for your use cases and develop a roadmap for selecting a platform that will support your AI strategy.
