I’ve spent a lot of time lately thinking about AI-native companies. What makes a company AI-native? How is that different from simply adding AI to a company that already exists? Where do AI agents fit? Where does company knowledge live? And how do all these systems work together?

As I’ve been experimenting with these ideas, I think I’ve stumbled onto a useful way of thinking about the problem.

The Intelligent Enterprise may work a lot like the human body.

It has a brain. It has memory. It has a nervous system. It has arms and legs that do the work. Most importantly, all of these pieces have to work together.

Start With the Brain

Imagine the top of the Intelligent Enterprise as three connected parts: the human, the LLM, and organizational memory.

Each serves a different purpose. The human provides direction and judgment. The Large Language Model provides intelligence and reasoning. Organizational memory provides the information and history the intelligence needs to understand the company.

Together, these three pieces begin to look surprisingly similar to a brain.

The Human Provides Purpose

I don’t believe the human disappears from the Intelligent Enterprise. Quite the opposite. The human’s role becomes incredibly important because someone has to decide what the organization is trying to accomplish.

Humans establish goals and values. They decide how much authority an intelligent system should have. They make judgments when the answer isn’t clear. And ultimately, humans remain accountable for the organization.

The human doesn’t need to perform every task, however. We already don’t operate companies that way.

A CEO doesn’t personally approve every purchase order, schedule every production run, answer every customer email, or collect every overdue invoice. Organizations delegate authority to people throughout the company.

The Intelligent Enterprise does the same thing. The difference is that some of that delegated authority can now go to intelligent systems.

The LLM Becomes the Intelligence Layer

In this model, the LLM sits near the center of the enterprise. Its job isn’t simply to answer questions. It interprets intent, reasons about problems, creates plans, retrieves information, considers alternatives, and determines what should happen next.

This is very different from the way we have traditionally used business software.

Traditional business software waits for us. We open an application, navigate through menus, find information, decide what to do, and enter instructions into another application. The human provides much of the intelligence connecting all those systems together.

An Intelligent Enterprise begins to change that relationship.

The LLM becomes an intelligence layer capable of working across the organization. But intelligence alone isn’t enough. Intelligence needs something to reason from.

It needs memory.

The Company Needs Organizational Memory

This may be one of the most important parts of the architecture.

The LLM doesn’t need to contain the company’s memory. It needs governed access to the company’s memory.

When we think about organizational memory, it’s tempting to think only about documents. Policies, procedures, contracts, meeting notes, project documentation and decisions certainly matter, and systems such as SharePoint can become an important part of that memory.

But organizational memory is much bigger than a document library.

The ERP remembers what the company bought and sold. It remembers customers, vendors, inventory, production, invoices, payments, purchase orders, and thousands or millions of transactions that describe the history of the business.

The CRM remembers relationships with customers and prospects. It can remember conversations, opportunities, activities, sales history, and commitments.

SharePoint can remember something different. It can preserve policies, procedures, project history, decisions, contracts, specifications, lessons learned, and the reasons behind decisions.

Other databases and business systems remember still other parts of the organization.

Together, these systems form something much more powerful than a collection of applications.

They form organizational memory.

That changes the way I think about enterprise architecture.

Historically, we’ve tended to think about the ERP, CRM, SharePoint, databases, and other applications as separate systems. Each application has a purpose, and employees move between them to do their jobs.

But an LLM doesn’t necessarily need to think about the company that way.

From the perspective of the intelligence layer, these systems can become different parts of the company’s memory.

One system remembers transactions. Another remembers relationships. Another remembers procedures. Another remembers decisions.

The intelligence layer can retrieve the appropriate memory when it needs it.

That is a very different model of the enterprise.

The Brain Doesn’t Have to Know Everything

This distinction is important because it addresses one of the common misunderstandings about enterprise AI.

We don’t need to somehow put the entire company inside an LLM.

The LLM doesn’t need every invoice, procedure, customer record, production transaction, contract, and management decision stored inside the model.

The human brain doesn’t work that way either.

We retrieve memories when we need them.

The Intelligent Enterprise can do something similar.

When the LLM needs to understand why the company changed a policy, it might retrieve a decision document from SharePoint. When it needs to know whether an item is available, it might retrieve inventory from the ERP. When it needs to understand the history of a customer relationship, it might access the CRM.

The intelligence stays relatively centralized while memory can remain distributed across governed systems.

The LLM doesn’t have to contain the enterprise. It has to be able to understand and safely access the enterprise.

Now We Need a Nervous System

A brain isn’t very useful if it can’t communicate with the rest of the body.

That’s where another part of this architecture appears.

The Intelligent Enterprise needs a nervous system.

That nervous system can include MCP, APIs, connectors, events, and messaging systems. These technologies allow the intelligence layer to communicate with organizational memory and with the systems that perform work.

Model Context Protocol, or MCP, is especially interesting because it creates a standard way for AI systems to discover and interact with tools and information.

Think about what the nervous system does in the human body. Information travels in both directions. The brain sends instructions to the body, and the body continually sends information back.

The same thing has to happen inside an Intelligent Enterprise.

Imagine the intelligence layer needs to know how much inventory is available. The request travels through the nervous system to the ERP. The ERP responds that 412 units are available.

That information travels back to the intelligence layer.

Now the LLM can reason about it. Maybe 412 units is plenty. Maybe it represents only two days of demand. Maybe a purchase order needs to be created. Maybe production needs to be changed.

Memory has informed intelligence.

Now intelligence can turn into action.

Agents Become the Arms and Legs

This is where AI agents enter the picture.

If the LLM represents intelligence, agents become part of the system that carries out work.

A Purchasing Agent might review inventory and create a purchase order. A Sales Agent might respond to a customer request. A Collections Agent might identify overdue invoices and begin the collection process. A Production Agent might recommend changes to a production schedule. A Customer Service Agent might investigate a shipment problem.

These agents don’t need unlimited authority. In fact, they shouldn’t have unlimited authority.

They need clearly defined jobs and boundaries.

That’s not terribly different from humans inside an organization. A purchasing manager may have authority to issue a $5,000 purchase order but need approval for a $500,000 purchase.

An AI Purchasing Agent could operate under similar rules.

The brain provides intelligence. The nervous system carries instructions. The agents carry out actions using the systems available to them.

Then information about what happened travels back.

The Intelligent Enterprise Needs Control

The human nervous system doesn’t simply send unlimited commands throughout the body. It has feedback, limits, warning signals, and different levels of control.

The Intelligent Enterprise needs the same thing.

Agents need identities and permissions. They need authority limits and escalation rules. Their actions need to be monitored and recorded. Some actions should happen automatically, while others should require human approval.

I think of this as Agent Ops: the operating discipline required to safely manage a growing workforce of AI agents.

As companies deploy more agents, this may become just as important as building the agents themselves.

The question won’t simply be, “Can the agent do it?” The more important questions become, “Should the agent be allowed to do it? Under what conditions? Who knows that it did it? And what happens when something goes wrong?”

Those are management questions, not just technology questions.

What About Companies That Aren’t AI-Native?

This model doesn’t only apply to companies built from scratch around AI.

Think about a traditional manufacturer. Its “arms and legs” already exist. They might be departments, machines, warehouses, ERP systems, salespeople, production workers, accounting teams, and customer service departments.

An AI-enabled company doesn’t have to replace all of those things.

Instead, it can begin building an intelligence layer above them.

The nervous system connects that intelligence to the existing organization. Over time, agents may take responsibility for more tasks. Some work remains human. Some work remains traditional software. Some work becomes autonomous. Some physical work may eventually be performed by intelligent machines.

This creates an important bridge between the traditional enterprise and the AI-native enterprise.

A company doesn’t necessarily need to tear out its existing systems to become intelligent.

It needs to make its organizational memory accessible to intelligence and make its operational systems accessible to controlled action.

The Feedback Loop May Be the Most Important Part

There is one final part of the human-body analogy that I think matters.

The system has to form a loop.

The organization senses what is happening. It reasons about what it senses. It acts. It observes what happened. It learns from the result. And important information becomes part of organizational memory.

In simple terms:

Sense → Reason → Act → Learn → Remember

Then the cycle begins again.

That may be the real difference between simply using AI tools and building an Intelligent Enterprise.

Most companies today use AI one interaction at a time. Someone asks a question, gets an answer, copies the answer somewhere, and continues working.

The Intelligent Enterprise is different.

Intelligence becomes part of the operating architecture of the company.

It can access organizational memory. It can communicate with systems. It can direct agents. It can observe results. It can retrieve previous decisions. And within carefully designed limits, it can act.

The Brain of the Intelligent Enterprise

Put all of this together and a surprisingly simple picture emerges.

At the top is the brain: humans provide purpose, judgment, authority, and accountability; the LLM provides intelligence and reasoning; and the company’s connected systems provide organizational memory.

That organizational memory isn’t one giant database. It can remain distributed. SharePoint can remember knowledge and decisions. The ERP can remember transactions and operations. The CRM can remember relationships. Other systems can remember their own parts of the enterprise.

Connecting the brain to the rest of the company is the nervous system: MCP, APIs, connectors, events, permissions, and other integration technologies.

Below that is the body. Agents, employees, software systems, departments, machines, and equipment actually perform the work of the company.

And surrounding all of it must be governance. Agent Ops provides authority, permissions, monitoring, escalation, testing, and accountability so that intelligence can safely become action.

I’m calling this model The Brain of the Intelligent Enterprise.

I don’t think this is simply another way of describing an AI chatbot attached to a company.

I think it points toward a different way of designing companies.

For decades, we’ve built organizations around humans operating software. Humans have been the intelligence layer connecting one application to another. We read the email, check the ERP, open the spreadsheet, remember what happened last year, make a decision, and tell another person or system what to do.

Now we can begin separating those functions.

The company can have durable organizational memory. It can have an intelligence layer capable of reasoning across that memory. It can have a nervous system connecting intelligence to the organization. And it can have agents capable of taking action within clearly defined authority.

Humans don’t disappear from that architecture.

They move toward the top of it.

They provide purpose, judgment, values, authority, and accountability.

The intelligent systems increasingly handle the sensing, remembering, reasoning, coordinating, and acting required to carry out that direction.

If that model proves correct, the Intelligent Enterprise won’t simply be a company that uses AI.

It will be a company designed to think.