Unsupervised Named in Gartner's Market Guide for Agentic Analytics for the Second Consecutive Year

Gartner published its second Market Guide for Agentic Analytics in February 2026. Unsupervised was named a Representative Vendor in both editions. The report's most notable new prediction: by 2028, 60% of agentic analytics projects relying solely on MCP will fail because they lack a consistent semantic layer.

That prediction is worth unpacking, because it explains why so many AI-for-analytics pilots stall after initial demos. An LLM can generate a SQL query. But “revenue” means different things in different tables, “churn” is calculated differently by different teams, and no amount of prompt engineering fixes that. Gartner calls semantic alignment “foundational for effective agentic analytics” in both the 2025 and 2026 editions. In 2026, they put a number on it.

“By 2028, 60% of agentic analytics projects relying solely on MCP will fail due to the lack of a consistent semantic layer.”

Gartner, “Market Guide for Agentic Analytics,” Deepak Seth, Georgia O'Callaghan, Fay Fei, Jeroen Cornelissen, 9 February 2026.

How the Market Grew in One Year

The 2025 edition was Gartner's first Market Guide for this category. It described a market of about 30 vendors, mostly in pilot and experimentation mode. One year later, the 2026 edition counts 37 vendors and describes a market entering early-scale deployment. Established ABI vendors (Databricks, Snowflake, SAP) are adding agentic features. New specialized players are emerging alongside them.

The report cites adoption data from the 2025 Gartner Generative and Agentic AI in Enterprise Applications Survey: 41% of organizations have deployed or are actively deploying AI agents. 54% have deployed or are exploring goal-driven agents that operate autonomously. 79% of IT leaders expect significant productivity impact.

Source: Gartner, “Market Guide for Agentic Analytics,” 9 February 2026.

Three Ideas in the Report Worth Paying Attention To

1. “Perceptive analytics” is a new concept

The 2026 edition introduces a term that wasn't in the 2025 report: perceptive analytics. The idea is systems that don't wait for a query. They continuously monitor data and surface context-aware insights on their own—detecting anomalies, spotting emerging trends, flagging risks before anyone asks. Gartner argues that organizations adopting this approach gain “real-time situational awareness that enables proactive, context-aware decisions rather than reactive analysis.”

This matters because it draws a line between two kinds of agentic analytics: reactive (answer questions faster) and proactive (find things no one thought to look for). Most current tools are reactive. The report suggests the value shifts toward proactive over time.

2. LLMs and production analytics are diverging

“By 2028, 60% of users will use general-purpose LLMs for ad hoc and exploratory analysis, while production-grade reporting will remain in traditional ABI platforms.”

Gartner, “Market Guide for Agentic Analytics,” Deepak Seth, Georgia O'Callaghan, Fay Fei, Jeroen Cornelissen, 9 February 2026.

In other words: ChatGPT is great for asking a quick question about your data. It's not what you run your weekly business reviews on. The report sees these as two separate markets with different requirements for accuracy, governance, and repeatability.

3. Differentiation is moving away from models toward governance

The 2026 report is explicit: market leaders will emerge based on “semantic consistency, explainability, cost controls, and delegation frameworks, rather than model sophistication alone.” The vendors that win won't be the ones with the best LLM. They'll be the ones whose agents produce reliable, auditable results that an enterprise can trust at scale.

The report organizes vendors into three categories: traditional ABI platforms adding agentic features, capability-centric platforms (startups with deep technical specialization), and domain-specialized platforms (industry-specific). Differentiation is happening across all three.

Sources: Gartner, “Market Guide for Agentic Analytics,” Anirudh Ganeshan, Souparna Palit, David Pidsley, 21 February 2025; and Gartner, “Market Guide for Agentic Analytics,” Deepak Seth, Georgia O'Callaghan, Fay Fei, Jeroen Cornelissen, 9 February 2026.

Where Unsupervised Fits

The semantic layer problem is the reason Unsupervised exists. Our platform is built around a semantic model that maps business entities, relationships, and policies across an organization's data—so “revenue” means the same thing regardless of which database it comes from. Our agents don't generate SQL from LLMs. They operate on this structured layer.

We deploy three types of agents:

These agents are in production at enterprise scale. AT&T used them to identify over $100M in new opportunities. A Fortune 500 healthcare payer found $58M. Across all deployments, Unsupervised has surfaced over $1B in actionable value.

“Unsupervised's AI Data Analysts have delivered strong ROI—improving our key metrics while empowering our team with faster, smarter access to data insights.”