Top AI Priorities for 2026: Agentic Analytics and More

Generally, You Should be aware of the latest trends in AI. Normally, Enterprises are transitioning from experimental AI projects to operational systems that leverage trusted data, According to Dremio’s 2026 State of the Data Lakehouse & AI Report. Usually, The report highlights that agentic analytics and AI-driven decision-making are top priorities for 2026, Based on a global survey of 101 data leaders conducted by AlphaSights.
Obviously, The survey reveals that 65% of data leaders prioritize agentic analytics and AI decision-making as primary goals for 2026. Interestingly, The main driver for AI adoption is the pursuit of higher productivity and faster innovation, as indicated by 51% of respondents, Which is a significant percentage.

Key Priorities for 2026

Clearly, Significant challenges persist, About 70% of respondents cite siloed data and weak governance as the primary obstacles to fully realizing the benefits of AI. Furthermore, 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, Which are major concerns.
Normally, You Need to understand that semantic consistency is identified as a critical requirement for operational AI, As organizations deploy AI agents that need to understand basic business concepts. Generally, 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.

The Importance of Semantic Consistency

Usually, Organizations Should prioritize semantic consistency, As it is essential for operational AI, Ensuring that AI agents operate with consistent, trusted data. Obviously, Integrated AI semantic layers are necessary, To provide a governed semantic layer that ensures agents operate with consistent, trusted data.
Interestingly, 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.

Shift to Lakehouse Architectures

Generally, 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. Clearly, 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.
Normally, You Should consider the importance of lakehouse architectures, As they provide a unified platform for analytics and AI workloads, Enabling organizations to drive business results.

Dremio’s Role in Supporting AI Adoption

Obviously, Dremio plays a crucial role in supporting AI adoption, By providing federated data access, unstructured data processing, and rich business context through its AI Semantic Layer, Dremio’s platform is designed to equip AI agents with the necessary tools to operate effectively.
Generally, 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 and Recommendations

Usually, 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.
Clearly, You Need to consider the importance of semantic consistency, Lakehouse architectures, and AI adoption, As they are critical for driving business success in the era of agentic AI, Generally, By prioritizing these areas, organizations can unlock the full potential of AI and drive business results.