Why You Care
Ever wonder why your company’s AI tools sometimes miss the mark? They might be smart, but do they really ‘get’ your business? The enterprise AI landscape is heating up, with major players vying for your attention. But what if the real power of AI isn’t in the flashy interface, but in something you can’t even see?
What Actually Happened
The battle for enterprise AI is intensifying, as detailed in the blog post. Companies like Microsoft are embedding Copilot into Office, while Google is integrating Gemini into Workspace. OpenAI and Anthropic are also selling directly to businesses, according to the announcement. Every software-as-a-service (SaaS) vendor now includes an AI assistant, the article states.
Amidst this scramble for visible AI interfaces, a company called Glean is taking a different approach. They are betting on becoming the ‘intelligence layer’ that operates beneath these user-facing tools. Initially, Glean aimed to be an AI-powered search tool for enterprises. This tool would index and search across a company’s entire collection of SaaS applications, from Slack to Salesforce, the company reports.
However, Glean’s strategy has evolved. They are now focusing on being the ‘connective tissue’ between large language models (LLMs) and enterprise systems, as mentioned in the release. LLMs are AI models that can generate human-like text.
Why This Matters to You
Think of your company’s internal knowledge – all those specific projects, team structures, and product details. Generic AI models, while impressive, don’t inherently understand this unique context, as the paper states. This is where Glean steps in. They provide the crucial link that allows AI to comprehend your specific business environment.
What does this mean for you? It means AI tools can become far more relevant and helpful. Imagine asking an AI a question about a niche project, and it actually provides accurate, company-specific answers. This is possible when the AI is ‘grounded’ in your internal data.
Key Benefits of Glean’s Approach:
- Contextual Understanding: AI models gain specific knowledge about your business operations.
- Improved Accuracy: AI responses are more precise and relevant to internal queries.
- Enhanced Productivity: Employees can get faster, more reliable answers from AI assistants.
- Data Integration: Connects various SaaS tools (e.g., Jira, Google Drive, Salesforce) for a unified AI experience.
Arvind Jain, Glean’s CEO, explained this challenge. “The AI models themselves don’t really understand anything about your business,” Jain said. “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.” Glean’s pitch is that it already maps this context, sitting between the AI model and your enterprise data. How much more efficient could your daily work be if AI truly understood your company’s unique language and processes?
The Surprising Finding
Here’s an interesting twist: while many companies focus on building better AI chatbots, Glean argues that the real value lies elsewhere. The Glean Assistant, a chat interface, often serves as the initial point of contact for customers. This assistant uses a combination of leading proprietary models, like ChatGPT and Gemini, and open-source models, all grounded in the company’s internal data, the technical report explains.
However, what truly retains customers, Jain argues, is ‘everything underneath it.’ This challenges the common assumption that the user-facing AI experience is the sole determinant of success. It suggests that the infrastructure connecting AI to internal data is far more essential for long-term enterprise value. The research shows that this foundational layer, which deeply understands people and their work preferences, is becoming essential for building high-quality AI agents.
What Happens Next
This shift in focus by Glean indicates a broader trend in enterprise AI. We can expect more companies to invest in data integration and contextualization layers in the coming 12-18 months. For example, imagine a scenario where a new employee can ask an AI assistant about company policies or project histories, and receive accurate, personalized answers almost instantly. This would significantly reduce onboarding time and improve knowledge sharing.
Companies should consider evaluating their own internal data infrastructure. Is it ready to be connected to AI models? Actionable advice for readers includes exploring solutions that can act as this ‘connective tissue,’ ensuring your AI investments yield maximum returns. The industry implications are clear: the true power of AI in the enterprise will come from its ability to deeply understand and interact with a company’s unique operational context. As Jain states, “All of that is now becoming foundational in terms of building high quality agents.”