How to build an Enterprise Brain: Five steps to take today

A recent Forbes article named Microsoft, Glean, and UnifyApps as companies leading the development of the Enterprise Brain-a shared context and governance layer designed to give AI systems one consistent understanding of the business. The implementation question is what most enterprises are still working out.

The reason it matters is structural. AI agents fail in production not because the model underperforms, but because the context they need-the contract governing a customer account, the approval rules on a procurement request, the warranty terms on a service case-lives in other systems and was never brought together. That’s the agent trap: a demo that works and a production deployment that doesn’t. It’s also the 80%: the context, governance, connectivity, and workflow work that has nothing to do with the model and everything to do with whether AI reaches production.

Building an Enterprise Brain is how you begin to solve the 80%, at the enterprise level, rather than once per tool and once per use case. This piece builds on five Enterprise Brain actions outlined by Sandy Carter in Forbes. Below, we unpack each one through the lens of UnifyApps' Enterprise Brain framework. 

1. Inventory your knowledge sources before you build anything

The first mistake most enterprises make is treating knowledge as a search problem. Make company information searchable, give an AI application access to it, let the model retrieve what it needs. That works when the answer lives in one document. It breaks when a business question requires information from five systems and an understanding of how those pieces relate.

Before you build anything, map what your enterprise actually knows and where it lives. For each major domain-customers, contracts, products, suppliers, employees-identify which systems hold the authoritative record, which systems hold related context, and where the same entity appears under different identifiers. That map is the foundation of your knowledge layer. It gives AI a consistent view of entities across tools rather than five systems with five different answers.

Action today: pick one domain, such as your customers, and list every system that holds a record about them. That’s your first knowledge inventory.

2. Define governance at the enterprise level, not per tool

Knowledge gives the Enterprise Brain a picture of how the business operates. Governance gives it the rules for how that information can be accessed and used-and if you define governance per tool, every new AI system creates another set of permissions and policies to maintain.

The practical cost is vendor dependency: when a connector changes how it authorises, you re-permission. When a default setting flips, legal reopens a data-processing question. When a model retires, an integration gets rewritten. None of these are unreasonable changes. They’re just yours to absorb, one at a time, indefinitely.

Governance defined at the enterprise level covers identity, permissions, data access, compliance requirements, approval policies, and auditability-once, applied across every AI system that draws on the Enterprise Brain. An employee who can view a customer account but not change its commercial terms carries that rule into every workflow, not just the one where it was first coded.

Action today: for your top three AI use cases, write down who is allowed to see what and who is allowed to act on what. If the answer differs by tool, that’s where tool-level governance is accumulating debt.

3. Map your existing system capabilities as reusable actions

Actionability is the most underbuilt of the three contexts. Enterprises focus on what AI should know and what rules should govern it, then discover that the execution layer-the actual ability to update a record, create an order, open a case, route an approval-gets rebuilt from scratch inside each new agent.

Your enterprise systems already contain those capabilities. A CRM can update a customer record. An ERP can create a purchase order. A service platform can open a case. The work is not building those capabilities-it’s making them available to AI as governed, reusable actions rather than bespoke integrations recreated for every use case.

At UnifyApps, with 1000+ pre-built connectors and 10,000+ pre-built actions available, the catalogue of what’s already mapped is a starting point, not a ceiling. The question is which of your specific system capabilities need to be defined and governed at the enterprise level.

Action today: list the five most common things AI should be able to do-not retrieve, but do-in your top use case. Check how many are already available as governed actions and how many would need to be built.

4. Connect the three through an enterprise context graph-start with one use case

Built separately, Knowledge, Governance, and Actionability remain three isolated pieces an AI agent still has to connect at runtime. The enterprise context graph is the structure that links them into one representation of the business.

A procurement request shows how this works in practice. The knowledge layer tells the AI agent that the supplier already has a contract with the company, with the relevant pricing and purchase history attached. Governance determines whether the requester can approve the purchase or whether it needs a sign-off. The action layer provides a way to create the request, route it through the appropriate workflow, and update the underlying systems. The context graph connects those pieces: the supplier relationship links to the contract, the employee making the request, the policies governing approval, and the systems that update when the transaction moves forward.

The result is that the agent works with a complete representation of the business-not a set of isolated lookups stitched together at the agent layer.

Start with one use case. The goal is not to map the entire enterprise before you build anything. It’s to connect the three contexts for one well-defined problem and prove the graph holds under production conditions.

Action today: take the use case from Step 3 and draw the connections-which knowledge entities does it touch, which governance rules apply, which actions does it need to execute. That drawing is the first version of your context graph.

5. Compound: the second use case should cost less than the first

The true test of whether the Enterprise Brain is working is whether the second use case starts from firmer ground than the first.

If the second use case reuses knowledge entities, governance rules, and actions already defined, the build becomes assembly-first rather than construction. Cost drops. Time to production shortens. Each new use case draws on what already exists rather than rebuilding it-and that compounding effect is what turns a collection of AI pilots into a horizontal operating system.

If the second use case costs the same as the first, the three contexts are still living inside individual tools. The brain isn’t built yet.

Action today: once the first use case is in production, map which of its knowledge entities, governance rules, and actions the second use case could reuse. The overlap is your compound return.

Build once

The five steps above are not five separate projects. They’re one architecture, built incrementally, starting with the use case where the context gap costs you the most today.

The businesses that get AI into production-and keep it there - solved the 80% once. Every knowledge entity mapped, every governance rule defined at the enterprise level, every action catalogued pays forward into every use case that follows. The second costs less than the first. The fifth costs less than the second.

If you’d like to see what this architecture looks like for your environment,  schedule a session with the team.