As enterprises adopt generative AI, companies like Glean are building infrastructure layers that connect internal knowledge systems to AI interfaces.
The enterprise AI race is entering a new phase — one less focused on flashy chat interfaces and more centered on infrastructure.
While generative AI models dominate headlines, companies are quietly competing to become the connective tissue between enterprise data and AI-driven workflows. Among them is Glean, which is positioning itself as a foundational knowledge layer beneath Artificial Intelligence applications.
The interface isn’t the moat
Large language models and copilots may provide the front-end experience, but enterprises face a more complex challenge: connecting AI systems to fragmented internal knowledge bases spread across Slack, Google Drive, Salesforce, and dozens of other platforms.
Without structured access to reliable company data, Artificial Intelligence assistants risk hallucination, redundancy, or security breaches.
This is where enterprise search platforms are stepping in.
Building beneath the surface
Glean’s approach focuses on:
- Unified indexing of enterprise knowledge
- Permissions-aware search
- Contextual data retrieval for Artificial Intelligence workflows
Rather than competing directly with model providers, the company aims to integrate across ecosystems — becoming the infrastructure layer that ensures Artificial Intelligence systems pull from authoritative sources.
Why timing matters
Enterprise spending on Artificial Intelligence has surged as companies move from experimentation to operational integration.
However, CIOs remain cautious about:
- Data security
- Regulatory compliance
- Knowledge governance
The “Artificial Intelligence land grab” isn’t simply about deploying tools — it’s about owning the workflow backbone that connects data, people, and machine intelligence.
A structural shift in SaaS
If generative Artificial Intelligence changes how employees interact with software, it may also reshape the SaaS hierarchy.
Traditional dashboards could give way to conversational interfaces. But those interfaces will depend on structured, searchable, permissioned knowledge graphs.
In that sense, the real battleground may not be the chatbot itself — but the infrastructure layer beneath it.
As enterprise Artificial Intelligence spending accelerates, companies that control the connective layer between data and interface may define the next era of workplace software.
