Most enterprise data teams are buried. A business user files a ticket for a number, waits three days, gets a dashboard that almost answers the question, then files another ticket to adjust it. Enterprise agentic analytics breaks that loop by putting AI agents to work directly on governed data, so the person who has the question can get a trusted answer in the time it takes to type it.
This shift is already happening at scale. A 2025 PwC survey found that 79% of organizations say AI agents are being adopted in their companies, and Deloitte projects that the share of generative AI adopters piloting agentic AI will climb from 25% in 2025 to 50% by 2027. Gartner now tracks agentic analytics as a top emerging capability for enterprise BI in its 2025 Market Guide. This post explains what agentic analytics is, the benefits it brings to enterprise teams, how it sharpens decision-making, the features that separate real platforms from thin demos, and where Dremio fits.
Key highlights:
Agentic analytics is a method of data analysis where autonomous AI agents discover the right data, plan and run the analytical steps, interpret the results, and return answers or recommended actions with little manual direction. Instead of a person writing SQL or clicking through a dashboard, an agent reads the question, decides what data it needs, runs the queries, checks the output, and explains what it found. You can read a deeper breakdown in our guide to agentic analytics.
The difference from older approaches comes down to who does the work and how many steps the system can handle on its own. Traditional BI gives you dashboards. You still have to read them, form a hypothesis, and ask a data analyst to dig deeper. Conversational analytics added a chat box on top, so you could ask "what were sales last quarter" and get a single answer back. That is useful, but it stops at one question. Agentic analytics goes further. It chains multiple steps together, follows the trail, and acts.
| Capability | Traditional BI | Conversational analytics | Agentic analytics |
|---|---|---|---|
| Who drives the analysis | The analyst | The user, one question at a time | The agent, end to end |
| Typical output | A static dashboard | A single answer | An answer, the reasoning, and a recommended action |
| Multi-step reasoning | Manual | Limited | Built in |
| Acts on the result | No | No | Yes, within set guardrails |
Here is a concrete example. Sales drop in one region. A traditional dashboard shows the dip and stops there. An agent segments the data by traffic source, device, and geography, finds the drop is concentrated among mobile users, cross-references recent deployments, spots a payment gateway change from a specific date, then flags it with a suggested fix. Same data. Very different result.
Explore this interactive demo and see how Dremio's Intelligent Lakehouse enables Agentic AI
The benefits of agentic analytics show up most clearly at the team and organization level, not just for one analyst running one report. When every business function can ask hard questions and get trusted answers without waiting in a queue, the whole company moves faster.
For years, self-service meant handing business users a dashboard tool and hoping they could find what they needed. Most could not, so they filed tickets anyway. Agents change the floor of what self-service means, because the user no longer has to know table names, join logic, or where the data lives.
A marketer can ask why churn rose last month and get a real answer, not a link to a report. That widens the pool of people who can actually use enterprise data. See how this connects to a broader self-service analytics strategy.
Data teams spend a huge share of their week on repetitive request work. Pull this number. Add a filter. Rebuild this view. Agentic analytics absorbs that recurring load, which frees engineers and analysts to work on the harder problems that actually need a human.
The effect on the data team is real and measurable. Amazon's SCOT Finance Analytics team used Dremio to eliminate 60 hours of work per project and cut query times from 60 seconds to 4 to 6 seconds, while supporting more than 1,000 users. That is time the team got back to build, not babysit.
Productivity gains compound when answers arrive in seconds instead of days. A product manager validates a hunch before the standup. A finance lead checks a forecast assumption mid-meeting. The work keeps moving because nobody is stuck waiting on a request.
This is the quiet benefit that adds up over a quarter. Shave a two-day wait off a hundred decisions and you have changed how the business operates, not just how fast one query runs.
Ask five analysts to define "active customer" and you may get five answers. That inconsistency is where trust in data breaks down. Agents that draw from a shared semantic layer apply the same definition every time, so the number is the number no matter who asks.
Consistency matters more as more people and more agents query the same data. When every answer traces back to one governed source of truth, you stop arguing about whose spreadsheet is right and start acting on the result.
Traditional business intelligence tends to concentrate in the hands of a few power users. Everyone else consumes pre-built reports. Agentic analytics flips that ratio, because natural language lowers the skill barrier to near zero.
That scale is the point. When a thousand people can each get a governed answer to a specific question, BI stops being a bottleneck and becomes a shared utility the whole company runs on.
Agentic analytics improves decision-making by collapsing the distance between asking a business question and receiving a relevant, trusted answer you can act on. The slow part of most decisions was never the thinking. It was the waiting: waiting for a query, a report, a follow-up, a meeting. Agents remove most of that wait.
McKinsey's June 2025 research on the agentic AI advantage makes the same point at the enterprise level. The value shows up when organizations move past insight generation into faster decision cycles and automated follow-through. The companies that win are the ones that act on what they learn, not the ones that simply learn faster.
Plenty of vendors will tell you they do agentic analytics. Most of them are pointing a large language model at a database and calling it a day. That setup hallucinates column names, ignores governance, and falls over the moment two people ask at once. Real enterprise agentic analytics needs a foundation underneath the agent. Here are the seven features that separate a working platform from a demo.
| Feature | What it gives you |
|---|---|
| Natural language query capability | Plain-English questions translated into correct, optimized SQL |
| Unified enterprise data access | One governed view across every source, no copying |
| Semantic context and business definitions | A shared vocabulary so agents read your data correctly |
| Governance and access controls | Permissions enforced at the engine, not the chat layer |
| Explainable and auditable results | A traceable record of what the agent ran and why |
| Scalable query performance | Sub-second answers that hold up under concurrent load |
| Agent and application interoperability | Open standards so any agent or tool can connect |
Natural language query capability lets a user ask a question in plain English and get an accurate answer back, with the system handling the translation into SQL behind the scenes. Good natural language processing is more than pattern matching. It reads intent, maps business terms to the right tables, applies the correct filters and joins, and runs an optimized query, all without the user touching code.
Why it matters:
Unified enterprise data access means agents can reason across all your data, on-prem and cloud, lakehouse and database, without anyone copying it into a single proprietary store first. A query engine that federates across sources gives the agent one place to look, so it never has to guess which silo holds the answer. Strong unified data analytics and clean data access are what make a question like "which customer segment has the highest refund rate by product category" answerable at all, since that single question touches customer, order, and product data at once.
Why it matters:
Semantic context is the layer that tells an agent what your data actually means. Without it, an agent sees columns named amt, stat, and cust_id and has to guess, and wrong guesses produce SQL that runs fine but returns the wrong answer. A Semantic Layer maps those raw fields to business concepts like "active customer" or "annual recurring revenue," along with the relationships between tables, so the agent reasons with your vocabulary instead of inventing its own.
Why it matters:
Governance decides who sees what, and a real agentic platform enforces it at the query engine, not in the chat interface where an agent could route around it. Strong data governance means permissions travel with the data end to end, so an agent acting for a given user returns only the rows that user is cleared to see. The agent has no side channel and no way to bypass the rule.
Why it matters:
Explainability means you can see how the agent reached its answer, and auditability means there is a permanent record of it. Every query an agent runs should create a job entry that captures the SQL, the user or service account behind it, the timestamp, rows returned, and run time. That record is what lets a governance team review activity, set alerts for unusual patterns, and prove compliance later.
Why it matters:
Scalable performance is the difference between a slick demo and a platform a thousand people can lean on. When agents fire unpredictable data querying at all hours, manual tuning breaks down, so the system has to optimize itself. Automatic materializations and query rewrites can keep response times sub-second even as load climbs, which ties directly into performance management at enterprise scale. In real deployments, this approach accelerated 80% of workloads without manual tuning and dropped some query times from 13 seconds to 1 second.
Why it matters:
Interoperability means any agent or tool can connect to your data through an open standard instead of a custom integration. The Model Context Protocol gives compliant agents a single governed gateway, so you configure it once and any agent that speaks the protocol connects automatically. Real interoperability keeps you free to bring whichever agent you want, from Claude to ChatGPT to Gemini to your own, without rebuilding the plumbing each time.
Why it matters:
Dremio is an open, high-performance lakehouse platform built to speed up AI and analytics across distributed enterprise data. It is The Agentic Lakehouse, the only data platform built for agents and managed by agents, and it brings every feature above together in one place. Explore the full platform in Dremio's Agentic Lakehouse.
What you get with Dremio:
Dremio runs natively on Apache Iceberg, Polaris, and Arrow, and the company is a co-creator of Arrow and Polaris and a key Iceberg contributor. That open foundation is why agentic analytics on Dremio means no lock-in and no proprietary storage tax. Try connecting your AI agents to governed lakehouse data through Dremio's MCP server with a free trial at dremio.com/get-started.
Book a demo today and explore how Dremio can power agentic analytics across your enterprise data.
An AI agent takes a question, figures out which data it needs, generates and runs the queries, checks the results, and returns an answer with its reasoning. It leans on a semantic layer to read your data correctly, a governance layer to stay within permissions, and a query engine to run fast. The agent handles the multi-step chain that a human analyst used to run by hand.
Traditional BI gives you dashboards and reports that you read and interpret yourself. Agentic analytics does the interpreting, follows the trail across multiple steps, and recommends an action. BI tells you what happened. An agent tells you what happened, why, and what to do about it, then can act within guardrails you set.
No. It changes what analysts spend their time on. Agents absorb the repetitive pull-this-number work, which frees analysts for the harder problems: defining metrics, designing the semantic layer, validating models, and governing how agents behave. The role shifts from running queries to shaping and overseeing the system that runs them.
The biggest risks are wrong answers from missing context, data exposure from weak governance, and runaway cost from unoptimized queries. Each maps to a feature above. A strong semantic layer prevents bad SQL, engine-level access controls prevent leaks, and self-optimizing performance keeps cost in check. The risk is not the agent. It is deploying one without the governed foundation underneath it.
Deploy agentic analytics directly on Apache Iceberg data with no pipelines and no added overhead.
By unifying data from diverse sources, simplifying data operations, and providing powerful tools for data management, Dremio stands out as a comprehensive solution for modern data needs. Whether you are a data engineer, business analyst, or data scientist, harnessing the combined power of Dremio and Apache Iceberg will undoubtedly be a valuable asset in your data management toolkit.
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