The most important shift for IT leaders is not that lakehouses now support AI, but that the platform is being repositioned from an analytical back end to part of the runtime fabric for business action. That raises the procurement bar. A lakehouse chosen mainly for query speed or storage economics may prove inadequate if agentic use cases depend on low-latency policy enforcement, auditable lineage, event responsiveness, and reliable integration with operational systems.
This changes the trade-off discussion. Openness matters, but so does the operational cost of stitching together an open ecosystem versus adopting a more integrated platform. CIOs should test whether “open” in practice means portable data, portable metadata, and portable governance policies — not just support for open table formats. Otherwise, organizations risk moving lock-in from storage into orchestration, security, or AI-serving layers.
Governance also needs to be treated differently. In an analytics model, poor quality data typically degrades insight. In an agentic model, it can trigger incorrect actions, policy violations, or inconsistent customer interactions. That makes data observability, access control granularity, rollback capability, and human-override mechanisms part of operational risk management, not just data management hygiene.
Before committing to a platform, technology leaders should pressure-test three questions:
- Which agentic use cases truly require lakehouse-centred execution versus a separate operational architecture?
- Can governance controls follow data across clouds, models, and partner environments?
- What is the failure model when an AI-driven workflow acts on stale, incomplete, or improperly authorised data?
The winning architecture may be the one that best constrains AI safely at enterprise scale, not the one with the strongest benchmark story.
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 The Forrester Wave™: Data Lakehouses, Q3 2026. 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 Forrester Wave™: Data Lakehouses, Q3 2026 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.
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