Enterprise AI’s Critical Layer: How Glean’s Ingenious Strategy Builds the Intelligence Beneath the Interface

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Enterprise AI’s Critical Layer: How Glean’s Ingenious Strategy Builds the Intelligence Beneath the Interface

DOHA, Qatar – October 2025. While tech giants battle for control of the enterprise AI interface, a fundamental shift is occurring beneath the surface. Glean, a company that began as an enterprise search tool, is now executing a pivotal strategy: building the indispensable intelligence layer that connects powerful but generic large language models (LLMs) to the specific, permissioned context of a business. This approach, detailed by CEO Arvind Jain at Web Summit Qatar, addresses the core challenge of enterprise AI adoption—moving from impressive demos to secure, scalable deployment.

Glean’s Evolution from Enterprise Search to AI Connective Tissue

The enterprise AI landscape is currently dominated by visible, interface-level competition. Microsoft bundles Copilot into its Office suite, while Google aggressively integrates Gemini across Workspace. Furthermore, leading AI labs like OpenAI and Anthropic sell directly to corporations, and virtually every SaaS platform now includes an AI assistant. Consequently, the market focus has centered on the chat window or the sidebar plugin. However, Glean’s seven-year journey has positioned it differently. Initially conceived as a “Google for enterprise” search tool, the company’s deep work in indexing and understanding connections across a company’s SaaS stack—from Slack and Jira to Google Drive and Salesforce—has become its foundational advantage. This historical context is critical for understanding its current market position.

The Foundational Problem: Generic Models Lack Business Context

Arvind Jain articulates the central issue with clarity. “The AI models themselves don’t really understand anything about your business,” he stated during the Equity podcast recording. “They don’t know who the different people are, they don’t know what kind of work you do, what kind of products you build.” Therefore, an LLM can generate text but cannot reliably act on proprietary data it cannot access or understand. This gap creates significant risks, including hallucinations, data leaks, and irrelevant outputs. Glean’s pitch is that it has already mapped this complex business context and can now sit as a neutral layer between the model and the enterprise’s data universe.

The Three Pillars of Glean’s Intelligence Layer Strategy

Glean’s solution is not a single product but a multi-layered platform. The Glean Assistant, a chat interface, often serves as the customer entry point. However, Jain argues the real retention driver is the infrastructure beneath it, built on three core pillars.

1. Model Access and Abstraction: Glean acts as a switchboard for LLMs. Instead of locking an enterprise into a single provider like GPT-4 or Claude, Glean’s platform allows companies to use, combine, or switch between leading proprietary and open-source models. This flexibility protects against vendor lock-in and enables leveraging the best model for a specific task. Jain views AI labs as partners, not competitors, stating, “Our product gets better because we’re able to leverage the innovation that they are making in the market.”

2. Deep System Connectors: True intelligence requires action. Glean integrates deeply with core enterprise systems—Slack, Jira, Salesforce, Google Drive—to understand information flow and, critically, to enable AI agents to perform actions within those tools. This moves AI beyond conversation into workflow automation.

3. Governance and Permissions-Aware Retrieval: This is arguably the most critical component for large-scale enterprise adoption. “You need to build a permissions-aware governance layer and retrieval layer,” Jain emphasized. The system must know who is asking a question to filter responses based on their access rights. It also verifies outputs against source documents, generates citations, and prevents hallucinations. This governance layer is the key differentiator between a departmental pilot and an organization-wide rollout.

Market Validation and the Platform Giant Question

Investors have signaled strong belief in this middleware thesis. In June 2025, Glean raised a $150 million Series F, nearly doubling its valuation to $7.2 billion. Unlike frontier AI labs with massive compute costs, Glean operates a capital-efficient, software-driven model with a fast-growing business. However, a significant strategic question remains: can this independent layer survive as platform giants like Microsoft and Google push deeper into the AI stack? These companies control vast surface area in enterprise workflows and are integrating AI directly.

Jain’s counter-argument hinges on neutrality and choice. Enterprises, he contends, do not want to be locked into a single model or a single productivity suite’s ecosystem. A standalone, neutral intelligence layer offers strategic flexibility, allowing businesses to choose best-in-class models and connect data across a heterogeneous software environment, not just within one vendor’s walled garden. The recent funding round suggests many investors agree with this assessment of enterprise buyer psychology.

The Real-World Impact on AI Deployment

The practical impact of this layer is accelerating safe AI deployment. Large organizations cannot simply dump all internal data into a model and hope a wrapper application sorts out permissions later. Glean’s system provides the necessary controls from the start. For example, an employee in marketing can ask a question about a product roadmap and receive an answer synthesized from documents in Confluence, discussions in Slack, and tickets in Jira—but only if they have viewing rights to all those sources. A finance colleague asking the same question might receive a different, appropriately scoped answer. This nuanced understanding is what transforms generative AI from a novelty into a reliable enterprise tool.

Conclusion

The enterprise AI race extends far beyond the chatbot interface. Glean’s strategy highlights the critical, if less visible, need for an intelligence layer that connects powerful generative models to the complex, governed reality of business data and workflows. By focusing on model abstraction, deep system integration, and robust governance, Glean is addressing the fundamental barriers to enterprise AI adoption at scale. As the market matures in 2025 and beyond, this infrastructure-focused approach may prove to be as strategically vital as the models themselves, determining not just who uses AI, but how safely and effectively they can use it across the entire organization.

FAQs

Q1: What is an “AI intelligence layer” in enterprise software?
An AI intelligence layer is the middleware infrastructure that sits between large language models (LLMs) and a company’s internal data and applications. It provides context, manages permissions, ensures data relevance, and allows different AI models to work with enterprise systems securely.

Q2: How is Glean different from Microsoft Copilot or Google Gemini?
While Copilot and Gemini are AI assistants deeply integrated into specific productivity suites (Microsoft 365, Google Workspace), Glean aims to be a neutral platform that connects multiple AI models to data across a company’s entire software ecosystem, regardless of vendor, with a strong focus on cross-platform governance.

Q3: Why is governance so important for enterprise AI?
Governance ensures AI responses respect user data access permissions, prevents the exposure of sensitive information, reduces hallucinations by grounding answers in verified sources, and provides audit trails. It is essential for compliance, security, and trustworthy deployment at scale.

Q4: What does “model abstraction” mean?
Model abstraction is the ability to use multiple AI models (e.g., from OpenAI, Anthropic, Google, or open-source) through a single platform. It lets enterprises choose the best model for a task, avoid vendor lock-in, and easily adopt new models as technology evolves.

Q5: Can a company like Glean compete with major tech platforms?
Glean’s competition thesis relies on offering neutrality and best-of-breed flexibility. Many enterprises use software from multiple vendors and may prefer an independent layer that connects everything over being tied to one platform’s integrated but limited AI ecosystem. Its recent $7.2 billion valuation indicates strong investor belief in this market position.

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