Anthropic Made Enterprise Knowledge Access Look Like Table Stakes

A whole category of enterprise software sells one promise. Connect to all of your company's data, understand it, and act on it. Glean is the company most associated with that promise. It indexes more than 100 connected apps, builds a knowledge graph across them, runs permissions-aware search, and offers an assistant and agents that turn scattered internal information into answers. Glean describes its platform as connecting and understanding all of your enterprise data so it can generate trusted answers and automate work.

Anthropic just published a post about how it runs its own internal analytics, and read closely, it makes the case that this promise is becoming a baseline expectation. Glean still does hard things well. The most expensive problem it solves, though, looks increasingly like something a competent data team can assemble on its own once it accepts where the real work sits.

The headline number

As reported by InfoQ, Anthropic says Claude now handles roughly 95 percent of its internal business analytics requests, with about 95 percent accuracy in aggregate. Employees query business data directly instead of filing tickets with the data team. The data science and data engineering group behind the work, Chen Chang, Clement Peng, Justin Leder, Johanne Jiao, and Josh Cherry, describe the payoff as freeing people up for the harder work of causal modeling, forecasting, and machine learning rather than answering the same questions over and over. The full writeup lives on the Anthropic blog.

The figure worth dwelling on is the starting point. Out of the box, Claude answered 21 percent of analytics questions correctly. After the team encoded their analytical workflows and business context as skills, accuracy climbed past 95 percent overall and approached 99 percent in some domains. That is a swing of more than 70 points, and very little of it came from a better model.

Where the lift actually came from

Anthropic is candid about the cause. The result owes more to data governance, semantic definitions, and operational discipline than to any jump in model quality. Their argument is that AI analytics can only ever be as reliable as the data platform sitting under it, which puts the weight on data modeling, testing, metadata management, and quality checks.

The setup runs on four layers. Data foundations cover the governed models, metrics, and metadata, essentially the warehouse. The knowledge layer holds semantic definitions, lineage, and business context, the reference surfaces an agent consults to find its way around. Skills encode the repeatable analytical workflows. Validation systems check outputs for correctness and consistency.

The team's own framing gives the game away. They describe the knowledge layer as what turns a phrase like "weekly active users" in a stakeholder's question into one governed entity in the data model, instead of several conflicting versions scattered across dashboards. That is close to the value Glean's Enterprise Graph is sold on. Anthropic built a version of it on a dimensional model and a semantic layer, queried through skills.

People in the data world picked up on this fast. Francesco Mucio, a BI and data architect, wrote that the real answer to how Anthropic does this is a semantic layer, with the agent consulting it first to work out dimensions, metric definitions, and joins rather than hitting tables directly. Arsenii Antonenko, a QA automation engineer, made the broader observation that more deployments keep landing on the same lesson, that context definition matters more than raw model capability.

Why this reads as a problem for Glean

Glean's value rests on three things: connecting to everything, mapping the relationships in a graph, and acting on the result through agents. Anthropic's writeup shows that the understanding and the acting both sit downstream of a governed semantic layer and a library of skills, and those are artifacts a capable data org can build, own, and version on its own.

This does not mean Glean is in trouble next quarter. Searching across messy, permissioned, unstructured information spread over dozens of systems is genuinely hard, and Glean does it well. What changes is the buyer's question. When the models are commoditizing and the leverage comes from your own semantic definitions plus a handful of encoded workflows, a buyer starts asking what the platform provides that they cannot put together on top of governed data and an agent they already license. A vendor whose pitch is turning company knowledge into answers now has to answer that question, because a five-person team published a working recipe.

The honest caveats

The reaction to the post was mixed, and the skeptics raise a fair objection. Analytics is supposed to be deterministic. The same question should return the same number every time, and an agent that lands around 95 percent accuracy is a different reliability contract than a hand-built report. For finance, or anything that gets audited, 95 percent does not ship. The discussion on r/BusinessIntelligence captured a lot of that pushback.

A few other things temper the table-stakes reading. Anthropic is not a typical company. It employs strong data engineers, keeps an unusually clean internal data culture, and has every incentive to make Claude look good on its own work. The jump from 21 to 95 percent is the cost, not the freebie. Building the governed datasets, the semantic layer, the skills, and the validation systems is exactly the slow, unglamorous work most organizations underfund, and part of what Glean sells is the option to skip it. Skills also have to be maintained. Anthropic stresses that human-owned documentation and structured definitions matter more than raw query history, which is an ongoing burden rather than a one-time install.

So "Glean killer" goes too far. A fairer reading is that Glean's premium now has to be earned above the semantic layer rather than at it.

The takeaway

Anthropic's three stated principles for AI analytics are to keep one source of truth for every metric, make the right data easy to find, and keep catching stale definitions as they go. Those three are data engineering principles. They happen to be the precondition for AI to work at all. The model was the easy part, and the post says as much without quite saying it.

For anyone selling AI that understands the enterprise, that is the shift to watch. The model layer is commoditizing, and so is the connector layer. The defensible ground is the governed semantic layer and the trust that sits on top of it, and Anthropic just published a credible blueprint for owning that in house.

Access to company knowledge, and the ability to act on it, used to be the whole product. It is starting to look more like the price of entry.