Many organizations see AI and advanced analytics as key to their future success. A recent report shows that most are focused on building agentic systems and making smarter decisions with data. However, they face significant hurdles related to data management and governance that could slow down progress.

Top Priorities for AI in 2026

According to a global survey, about 65% of data leaders say that agentic analytics and AI-driven decision making are their main goals for the next year. They want systems that can act on data more intelligently and autonomously. Achieving higher productivity and faster innovation are the main reasons for adopting AI, with half of the respondents emphasizing these benefits.

Many organizations are moving beyond simple experiments. They are shifting their focus to building reliable, trusted AI systems. This transition requires a solid foundation of high-quality, unified data that can support complex AI applications and decision-making processes.

Challenges in Data Readiness and Governance

The report highlights that 70% of organizations see siloed data and weak governance as their biggest obstacles. When data is stored in separate systems, it becomes difficult to get a complete and accurate picture. Poor data quality and missing semantic definitions also hinder AI deployment, with 40% citing these issues as major blockers.

Semantic consistency is especially important as AI agents need to understand basic business concepts. Without a governed semantic layer providing shared definitions, AI systems can struggle to interpret data correctly. This gap can slow down AI adoption and limit its effectiveness in operational settings.

Despite these challenges, organizations are rapidly consolidating their analytics and AI workloads onto data lakehouses. Nearly all respondents (92%) plan to move most of their workloads to this architecture within the next year. By 2027, most expect the lakehouse to become their primary data platform, reflecting a strategic shift towards unified data environments.

Running AI and machine learning directly on the lakehouse is a top priority for 78% of organizations. They see it as essential for operational AI and smarter decision-making. Additionally, 81% aim to eliminate redundant data copies, which helps reduce costs and improve data consistency.

The findings show that enterprises are designing their data infrastructure with agentic intelligence in mind. This is not just hype but a real architectural approach to meet constraints like cost, governance, and data quality. Many are also facing issues like data duplication and inconsistent definitions, which they are actively working to resolve.

Inspired by

• https://ai-techpark.com/survey-agentic-analytics-ai-decision-making-lead-2026-priorities/