The importance of artificial intelligence grows every day, yet only 5% of enterprise AI pilots deliver measurable impact.
There are many causes for this, but one of the most common we see as a data and AI consulting company is the difficulty in accessing various business-wide data sources. This often results in gaps in insights and limitations, eventually disengaging multiple groups from adopting AI.
Luckily, this means that AI is not the issue.
When organizations tackle AI without the right technology and guidance, complexity and costs climb while results stall. Conversely, teams that operationalize AI around a connected foundation, or a single company brain
, are pulling ahead and achieving measurable results like:
Reducing miscommunication between departments
Saving time looking for information across the company
Discovering Subject-Matter Experts (SMEs)
Creating subject assistants
This blog explores how to overcome common AI adoption hurdles by building a company “Brain” with Glean so that your organization can join the 5% that turn AI into sustained, measurable impact.
Why Build a Single Work AI “Brain” for Your Enterprise?
Most companies don’t fail at AI because models are weak; they fail because knowledge is fragmented across various tools, teams, and preferences. Seemingly, daily, there are new AI solutions that enter the market, making it difficult to choose just one as the main solution. When every department adopts its own AI, you get silos that trap insights and stall momentum.
The cure for this is to have a single, enterprise Work AI platform that gives everyone a single place to search, create, and act. Ideally, this platform would be guided by company context and governed by permissions, not platform boundaries.
With a unified brain
actions happen in context. Teams can ask questions, draft assets, find experts, and trigger workflows from the same surface without hopping between apps or losing meaning. Agentic systems orchestrate multi-step tasks and call approved tools, escalating only when human review is needed.
And with an observe, learn, improve loop, workflows get faster and smarter over time as bottlenecks are identified and the next best step is automated. The result is end-to-end value with measurable compounding impacts vs isolated wins.
Centralize Knowledge, Context, and Access
It is unavoidable that your business’s data is spread across multiple platforms. Knowledge is more than simply data living in databases; it also includes the countless files produced every day and the various platforms that you use internally. At phData, for example, we have data sprawled across Snowflake, Google Docs, ServiceNow tickets, Slack chats, Salesforce records, and people’s heads (just to name a few).
Accessing all of these sources can be challenging and frequently overwhelming, especially if you are unfamiliar with all the available resources and which ones to go to for a particular answer. The solution? Indexing all of these knowledge sources in a single location and using AI to recommend the best places to uncover the information based on the user’s questions and needs.
This is where Glean shines.
What is Glean and How Does it Work?
Glean is a strong enterprise AI platform that analyzes your company’s data to provide citation-backed answers and insights. When deployed, Glean becomes an expert who reviews every document and participates in every conversation at your organization.
It allows corporate search across your internal systems by indexing content via native and custom connectors and then building an understanding of that information, allowing you to find what you’re looking for in one spot. It integrates several enterprise data sources, including documents, messages, tickets, and code, into a single, permission-aware index.
Most importantly, Glean replicates source permissions. Users only see results that they already have access to in the originating apps, and those access rules are enforced during query time.
You can ask natural language questions, summarize content, analyze findings from several sources, and receive answers with transparent citations to validate the results. When enabled, Glean can use real-time web search and company information to provide current context while maintaining enterprise controls.
Glean’s major advantages come from its horizontal, platform-agnostic strategy, which stands in contrast to other vertical solutions designed for specific ecosystems. Customers who use these more specific products frequently discover that capabilities outside the ecosystems are underdeveloped, difficult to manage, and less relevant to their enterprise-wide requirements.
How Glean Works Under the Hood
Glean’s strategy for building context is largely based on its Knowledge Graph, which acts as a structured, machine-readable abstraction layer that connects all different enterprise data, including people, documents, tools, and projects, to form a cohesive network. Unlike basic indexes, this graph is based on semantic links between items, which are frequently expressed using a triplet structure (subject, predicate, object).
This organized method maintains data accuracy, allowing the system to perform complicated multi-step reasoning and consistently expose subtle insights across organizational silos. The Knowledge Graph effectively mitigates common Large Language Model (LLM) weaknesses such as ambiguity, entity conflation, and difficulty with precise, deterministic fact recall, providing the necessary contextual structure for enterprise-grade applications.
By giving teams one trusted hub for discovery and action, you enable company-wide querying, accelerate expert discovery, and power downstream agents with clean, governed context. The company brain
isn’t just a search box; it’s the secure connective tissue that lets AI work like your organization actually works.
A Company Brain to Unlock Your Potential
Having a powerful work AI platform means more than simply implementing another AI tool
into the organization. That means training an artificial intelligence that can truly understand the business and the different factors that influence departments in order to produce more valuable resources across the company. It will accelerate users to get onboarded in the company and into internal projects, the creation of new content, making decisions by reducing the research time, and even facilitating the integration of different systems.
You will be creating a powerful company brain that understands the business and, if implemented accordingly, can even use external sources to help with your research. With a centralized platform that integrates numerous systems, information could be accessed from anywhere and used for a variety of purposes.
Glean provides several ways to access data, one of which is through the use of natural language in a chat format, allowing you to respond to questions, query data, find documents, or even locate experts in the organization. Another interesting feature is the ability to develop AI agents, which allows you to automate tasks using a no-code agent builder.
Glean agents are a great way to build AI assistants for particular topics. They can link to multiple internal sources, learning about the business and watching public conversations between specialists to help the team. It may also act as a technology expert by monitoring technical documentation and responding to questions. Agents may be configured as well to execute specific tasks and can be activated by external systems or scheduled.
With the glean capabilities, you can create a true company brain that can be accessed from many locations for a variety of purposes.
The Brain in Action
Glean revolutionizes decision-making by seamlessly fusing the enterprise knowledge graph with personal graphs. This unique combination doesn’t just enable support for complex, high-stakes choices. It delivers insights and experiences that are deeply personalized, benefiting both the overall organization and every individual within it.
Delivers tailored, proactive support based on individual work habits, goals, collaborators, and communication style. This means the Assistant can plan, reason, and execute complex, multi-step tasks, adapt to unique writing styles, and act as a true extension of each employee with no prompt engineering required.
Glean’s Personal Graphs:
Delivers tailored, proactive support based on individual work habits, goals, collaborators, and communication style. This means the Assistant can plan, reason, and execute complex, multi-step tasks, adapt to unique writing styles, and act as a true extension of each employee with no prompt engineering required.
Glean’s Enterprise Knowledge Graph:
A dynamic, continuously updated model that deeply understands an organization’s people, projects, processes, and data. This foundational intelligence enables AI to deliver context-rich, enterprise-wide insights and actions, making it possible to achieve enterprise superintelligence where AI helps everyone do more, at a higher quality, than any individual could alone.
Building AI Agents in Glean
The Glean Agent Builder is an enterprise-focused platform for creating, orchestrating, and governing AI agents at scale. The objective is to democratize access to data insights across an organization by integrating no-code agent creation with permission-based security.
This feature allows teams to create agents via prompt or through a visual builder, with enterprise data sources linked to them. Agents are powered by an agentic engine, which allows them to think, plan, and act while maintaining real-time control, management, and security required for business deployment. The agentic engine leverages LLMs, which you can choose as needed based on the tasks. Models such as OpenAI GPT, Google Gemini, and Claude Sonnet are examples of models that can be used in agents, with the option of mixing models for each task as desired.
These agents can be designed to be accessed via chat format for greater interactivity, scheduled for repetitive tasks, called from other systems via API call, or embedded in a website. This gives you a broad range of choices for creating agents for numerous purposes, allowing you to consume your company brain
from multiple locations.
This means that your Work AI platform does not have to compete with other AI solutions you may have. Glean can serve as your central AI solution, connecting to all enterprise sources and allowing other solutions to access its knowledge and act as a source for other automations. Several AIs may take advantage of Glean’s centralized knowledge as a resource. This allows you to use the powerful knowledge engine while still benefiting from legacy or other required solutions for any reason.
AI Agents in the business
Now, let’s look at a couple of ideas that you could use in your business, such as a security assistant or a subject-matter expert.
Omni Expert
In this example, we have a subject-matter expert in a BI technology named Omni. That agent connects to internal information from the business’s multiple projects using the technology, finds discussions from company specialists in Slack channels, and searches the tech provider documentation.
All of this is to better answer to any questions about this technology. The agent was trained to recognize the nature of the question and search specific locations based on the topic.
As you can see, it is possible to use some commonly asked questions as conversation starters. This allows new users to rapidly get their questions answered.
It is also particularly important to note that the agents can (and should) be documented as a best practice. You can add many comment boxes around the created agent to explain each step, or to create a longer description addressing broader objectives.
This functionality becomes increasingly critical as your agent’s complexity increases.
Ask Security Questionnaire
Another excellent example is how to create a security assistant that can reply and guide users based on company policies and recommendations offered by the security team via multiple channels of communication.
This makes it easier for anyone in the company to get questions answered without having to spend time looking up policies, contacting the security team, or opening tickets to verify information.
When the agent responds, it also indicates where you can find the information that the agent discovered and used to create the response.
There are many ways to create agents in Glean. Depending on how complex you want your agent to be, this could include multiple steps or only a few.
Glean also has the capacity to combine several guidelines into a single Plan & execute steps that can understand multiple requirements at once, link them to the company context, and provide the best response to the user.
Conclusion
In this article, we looked at how businesses frequently struggle to get the benefit of AI because knowledge is spread across tools and teams, resulting in knowledge silos and, eventually, AI silos that limit discovery, collaboration, and decision-making.
By creating an integrated company brain, organizations can accelerate onboarding new employees, minimize miscommunication, save time searching for files and answers, enable automations, and make it easier to identify specialists across the organization.
Glean offers enterprise search, natural-language conversations with visible citations, and connections that enforce source permissions during query time. The Agent Builder enables teams to create controlled, LLM-powered assistants who plan, act, and interact via chat, scheduling, APIs, or embeds, while leveraging centralized knowledge.
This makes Glean the central AI layer capable of powering other AI solutions and automations across the organization.
