The Enterprise AI Land Grab Is On: Why Glean Is Building the Layer Beneath the Interface

The race for enterprise AI dominance is intensifying. Microsoft bundles Copilot into Office. Google pushes Gemini into Workspace. OpenAI and Anthropic sell directly to enterprises. Every SaaS vendor now ships an AI assistant.[1] Yet amid this visible scramble for the interface, one company is betting on something far less obvious: becoming the invisible intelligence layer that powers them all.

That company is Glean, and its strategy reveals a fundamental truth about enterprise AI in 2026—the real value isn’t in the chatbot; it’s in what sits beneath it.

The Problem With Bundled AI

The current approach to enterprise AI follows a familiar playbook: tech giants and startups build flashy interfaces—chat windows, copilots, assistants—and promote them as the solution to workplace productivity. It’s intuitive, visible, and easy to market. But it misses something critical.

Large organizations don’t operate in isolation. They run on sprawling ecosystems of tools: Slack for communication, Salesforce for customer relationships, Jira for project management, Google Drive for documents, ServiceNow for IT operations. Data lives everywhere, fragmented across incompatible systems with different permission structures and access controls.

When companies try to deploy AI at scale, they face a brutal reality: you can’t simply load all your internal data into a model and figure out the details later.[1] The stakes are too high. Sensitive information must be protected. Compliance requirements must be met. Permissions must be enforced. One mistake—an AI system revealing confidential data to the wrong person—can destroy trust and create legal liability.

This is where the bundled approach breaks down. A Copilot locked into Microsoft 365 can’t seamlessly access your Salesforce data. A Gemini instance in Google Workspace struggles to orchestrate actions across your entire tech stack. Each AI assistant becomes another siloed tool, unable to see the complete picture of how your organization actually works.

Glean’s Bet: The Infrastructure Layer

Glean’s founding vision was straightforward: be the Google for enterprise, an AI-powered search tool that could index and search across a company’s entire SaaS library.[1] But seven years in, the company has evolved. Today, Glean is positioning itself as something more fundamental—the connective tissue between AI models and enterprise systems.

The shift is strategic. Rather than competing with Microsoft, Google, OpenAI, and Anthropic in the interface space, Glean sees them as partners. “Our product gets better because we’re able to leverage the innovation that they are making in the market,” said Glean CEO Arvind Jain.[1] Instead of forcing enterprises to choose one model or one productivity suite, Glean offers a neutral infrastructure layer underneath.

This approach rests on three pillars:

Model Abstraction: Glean acts as an abstraction layer, allowing enterprises to switch between or combine models—ChatGPT, Gemini, Claude—as capabilities evolve.[1] Organizations aren’t locked into a single provider; they can optimize for cost, performance, or specialized capabilities.

Deep Connectors: Glean integrates deeply with Slack, Jira, Salesforce, Google Drive, and 100+ other business tools, mapping how information flows across them and enabling agents to act inside those systems.[1][4] This creates a unified knowledge graph of your enterprise.

Permissions-Aware Governance: Perhaps most importantly, Glean builds a governance layer that retrieves the right information while filtering based on who’s asking and what they’re authorized to access.[1] This is the difference between piloting AI solutions and deploying them at scale.

The Market Validates the Thesis

Investors have clearly bought into Glean’s vision. The company raised a $150 million Series F in June 2025, nearly doubling its valuation to $7.2 billion.[1] This valuation reflects confidence that Glean has identified a genuine market need—one that won’t be solved by traditional tech giants focused on their own ecosystems.

What’s particularly striking is that Glean doesn’t require massive compute budgets like frontier AI labs. The company operates a “very healthy, fast-growing business” by focusing on software architecture and integration rather than model training.[1] This makes the business model more sustainable and defensible.

Enterprise Context and Autonomous Agents

In December 2025, Glean unveiled its third-generation platform: Enterprise Context. This system combines memory, connectors, indexes, personal graphs, enterprise graphs, and governance to power autonomous agents capable of reasoning and adapting in real-time.[3] These agents can interpret intent from natural language, dynamically select tools, and communicate their reasoning—moving beyond rigid automation toward genuine AI autonomy.

Glean also launched Skills, an open standard allowing employees to create and share reusable expertise.[2] Rather than keeping knowledge locked in individual heads, organizations can package domain-specific workflows and best practices as skills that AI agents can invoke. Sales teams can automate account planning. Support teams can reduce response times. Engineering teams can streamline deployment processes.

The Broader Implications

Glean’s strategy suggests that the real enterprise AI winners won’t be those with the fanciest interfaces or most advanced models. They’ll be the companies that solve the unglamorous but critical problem of connecting disparate systems, managing permissions, and enabling AI to operate reliably at scale.

The enterprise AI land grab is real, but the most valuable territory isn’t visible on the surface. It’s the layer beneath the interface—where data flows, permissions are enforced, and AI actually gets work done.

Original source: TechCrunch – The enterprise AI land grab is on. Glean is building the layer beneath the interface.