Glean positions itself as the enterprise AI intelligence layer that connects AI models to company data and systems, rather than competing for the user interface.

Key differentiators include an abstraction layer for model access to switch or combine LLM providers, deep connectors to Slack, Jira, Salesforce and Google Drive to map information flows and enable in-app actions, and a governance layer to manage permissions and retrieval.

Founded seven years ago to be the Google for enterprise search, Glean now focuses on grounding model reasoning in organizational context to enable high-quality autonomous agents.

Implementation follows a phased approach with pilots and gradual expansion; governance and permissions tend to take longer than technical setup, requiring careful data access mapping and regulatory compliance.

The key question is whether platform giants could erode value by gaining deep native access, but many enterprises run multi-suite environments, making a unified, compliant intermediary layer appealing for accuracy, cost control, portability and security.

Industry trends point to consolidation of specialized AI firms, emerging interoperability standards, stricter ethics and privacy regulations, and a growing need for vendor-agnostic AI layers across diverse software ecosystems.

Governance and a context-aware approach are critical for scaling AI in enterprises, where deployments depend on access controls and reliability, not just experiments.

Governance and access control are essential for enterprise-wide AI deployment, preventing exposure of internal data and ensuring adherence to permissions.

Architecturally, enterprises should focus on comprehensive data access, strict permission and security, workflow enhancement, and measurable ROI through productivity gains.

The shift from tools to autonomous AI agents is central, with agents capable of tasks like data analysis, reporting and scheduling, underscoring the need for reliable performance across scenarios.

Glean uses deep connectors to ingest content and metadata, track usage, and preserve end-to-end permissions, enabling agent-like actions such as creating tickets, pulling documents, updating records and drafting emails within access controls.

Large enterprises cannot simply upload all data to a model; the governance layer enables controlled, scalable deployment and reduces reliance on a single model provider or toolset.