Key Priorities for 2026
According to Dremio’s 2026 State of the Data Lakehouse & AI Report, enterprises are transitioning from experimental AI projects to operational systems that leverage trusted data. The report, based on a global survey of 101 data leaders conducted by AlphaSights, highlights that agentic analytics and AI‑driven decision‑making are top priorities for 2026.
The survey reveals that 65% of data leaders prioritize agentic analytics and AI decision‑making as primary goals for 2026. The main driver for AI adoption is the pursuit of higher productivity and faster innovation, as indicated by 51% of respondents.
However, significant challenges persist. About 70% of respondents cite siloed data and weak governance as the primary obstacles to fully realizing the benefits of AI. Additionally, nearly half of the respondents point to a lack of unified, AI‑ready data, while 40% highlight issues with poor data quality and missing semantic definitions.
The Importance of Semantic Consistency
Semantic consistency is identified as a critical requirement for operational AI. As organizations deploy AI agents that need to understand basic business concepts, there is a growing need for a governed semantic layer to provide clear, shared definitions. Missing semantic context is a major blocker for 40% of respondents, underscoring the necessity of integrated AI semantic layers that ensure agents operate with consistent, trusted data.
Shift to Lakehouse Architectures
The survey also reveals a significant shift in enterprise data strategy, with organizations rapidly consolidating analytics and AI workloads onto the lakehouse. Nearly all organizations (92%) plan to migrate most of their analytic and AI workloads to the lakehouse within the next year. By 2027, 87% expect the lakehouse to be their primary data architecture.
Respondents view unified data as essential for operational AI, with 78% planning to run AI/ML workloads directly on the lakehouse. Additionally, 81% cite eliminating redundant data copies as a top priority for 2026. The findings indicate that enterprises are designing for agentic intelligence not as a trend, but as an architectural necessity driven by real‑world constraints such as cost, governance, and data quality.
Dremio’s Role in Supporting AI Adoption
Dremio, the pioneer of The Agentic Lakehouse, emphasizes the importance of open, interoperable technologies to avoid vendor lock‑in and support AI adoption across enterprises. Dremio’s platform is designed to equip AI agents with federated data access, unstructured data processing, and rich business context through its AI Semantic Layer.
In the era of agentic AI, data engineering teams face the challenge of manually tuning performance for thousands of users and agents asking unpredictable questions every second. Dremio’s Agentic Lakehouse addresses this by autonomously managing itself, removing undifferentiated management tasks, and allowing engineers to focus on initiatives that drive business results.
Dremio’s solutions are trusted by thousands of global enterprises, including Shell, TD Bank, and Michelin. Built on open standards, Dremio co‑created Apache Polaris and Apache Arrow, and is the only lakehouse built natively on Apache Iceberg, Polaris, and Arrow.
Conclusion
As enterprises aim to harness the full potential of AI, addressing data challenges and adopting modern architectures like the lakehouse will be crucial. Dremio’s solutions support these efforts by providing federated data access and autonomous management, enabling organizations to focus on driving business results.
