Attend any data conference (or coffee shop in a tech area) and you’ll be sure to notice that agentic AI has become the most talked-about topic in enterprise technology. The conversations all started around productivity, such as building content or developing code, with ChatGPT, ClaudeCode, or Gemini. Now, the conversation has pivoted to analytics and connecting agents with enterprise data or what I call “Agentic Analytics.”
The concept is simple enough. Agentic analytics consists of systems that don’t just analyze data but create dashboards. It also consists of visualizations that don’t just provide the perspective of what’s happening but why it is happening and what to do about it. The result? Significantly improved AI-driven recommendations which optimizes business operations and results in higher growth, better margins, lower costs, faster cash conversion cycles, and more customers.
Making the Vision of Agentic Analytics a Reality
In a recent Dremio global survey of 101 data and analytics leaders through a third-party agency, respondents overwhelmingly revealed that there is a strong momentum behind agentic analytics. However they also made it clear that most organizations are lacking the fundamentals to operationalize it.
When asked about priorities for the next two years, nearly two-thirds of respondents (65%) said agentic analytics and AI-driven decision-making was their primary goal for 2026. That level of intent signals a shift in mindset. AI is no longer treated as an experimental capability or an innovation side project. Leaders expect it to play a direct role in how the business operates.
Just as telling are why organizations are investing in AI. A majority of respondents (51%) said the main motivation is higher productivity and faster innovation. This is a pragmatic framing, as AI is being positioned as a way to reduce friction (think loads of tickets to lots of specialized resources), accelerate decisions, and help teams keep pace with growing demands. In other words, success will be measured by measurable impact, not novelty.
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Despite the urgency, most leaders are candid about what’s holding them back. Seven out of ten respondents identified siloed data and weak governance as the top barriers to realizing the benefits of AI. That statistic is significant because it shifts the conversation away from models and tools. The problem isn’t access to AI technology, it’s the environment those systems have to operate within.
Nearly half of respondents cited the lack of unified, AI-ready data as a major challenge. Another 40% pointed to poor data quality and missing semantic definitions. These issues tend to show up early in AI initiatives, but their impact grows as analytics and systems become more autonomous.
An agent can only act responsibly if it understands the data it’s working with. When definitions vary across teams, or when data quality is inconsistent, AI agents inherit those flaws. The result isn’t just slower progress, it’s poor results and reasoning, which leads to both lower quality and faster decisions. Not a good combination.
One of the more interesting themes in the survey was the emphasis on semantic consistency. As organizations move toward agentic systems, shared definitions and metrics are no longer optional. Human analysts can compensate for ambiguity by asking questions, double-checking assumptions, and applying judgment. AI agents don’t have that luxury. If “revenue,” “customer,” or “conversion” mean different things in different contexts, an agent may produce results that are technically correct but operationally wrong.
That’s why many leaders now view semantics as part of the core data foundation. It’s not about reporting aesthetics, but about enabling AI systems to reason consistently across domains and make decisions that align with how the business actually operates.
The survey also points to a broader architectural shift already underway. By 2030, respondents expect more than 90% of analytic workloads to move from legacy warehouses to lakehouse environments. This migration is often discussed in terms of cost and performance, but the data suggests something deeper. Organizations want fewer copies of data, clearer governance, and a single foundation that supports analytics and AI together.
As AI systems move closer to execution—approving actions, triggering workflows, or interacting with customers—trust becomes the central concern. Leaders need confidence that decisions are based on complete, governed, and current information. Fragmented architectures make that trust difficult to achieve.
Traditional analytics tolerate a certain level of imperfection: dashboards can be corrected, reports can be re-run, but agentic systems are raising the stakes. However, when AI systems act on insights, errors propagate faster and further. A misinterpreted metric or outdated dataset doesn’t just inform a bad decision, it can automate one.
That’s why the survey data points to a clear conclusion: organizations that invest early in unified data, strong governance, and semantic alignment will move faster with agentic AI, and with fewer setbacks. Those that delay may find themselves constrained not by AI capability, but by the foundations they built years earlier. The enthusiasm for agentic analytics is real, and it’s justified. But the data suggests that progress will be determined less by how quickly organizations adopt new tools and more by how deliberately they address data fundamentals. Agentic AI isn’t blocked by a lack of ambition. It’s constrained by readiness.
For leaders, the message is straightforward. If you want AI systems that can act with confidence, start by making sure the data they rely on is unified, well-governed, and clearly understood. Everything else builds from there.
About The Author Of This Article
Read Maloney is CMO at Dremio
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