We’re entering an uncomfortable phase: many companies will have access to increasingly powerful AI models but those models will also become increasingly similar.
The models will continue to evolve, as will AI agents, making it easier to automate tasks, analyze information, generate content and support decision-making. As organizations gain access to the same AI models, agents and platforms, technology alone becomes less of a differentiator.
So, what will turn that intelligence into something that sets one company apart from another?
The answer lies in something companies have spent decades building, although few manage it as a core capability: the way they decide, execute and learn. This includes:
- Their processes
- Their data
- Their exceptions
- Their past decisions
- Their accumulated experience
- Their operating culture
This is why two companies with similar products, technologies and profiles can achieve very different results.
A company’s true intellectual property
Every company has its own way of doing things. Over the years, it develops its own criteria for making decisions, ways of running processes, policies, controls, exceptions, methodologies and ways of interpreting the business.
Some of this knowledge is formalized in procedures and systems. Some is represented in data. But a vast amount remains distributed across documents, meetings and past decisions. Above all, much of it still resides with people.
The people who know when an exception is reasonable. Who understand why a rule is applied one way in one country and differently in another. Who know what concepts such as quality, risk, customer experience or efficiency actually mean within their organization.
And yet there’s a paradox. For decades, we’ve protected patents, brands, algorithms and data, while a fundamental part of the knowledge that explains how we know how to run the business remains difficult to capture, reuse and scale.
It is probably one of the most important forms of corporate intellectual property an organization has.
As long as people were primarily responsible for executing processes, this model could work. But AI agents change the rules.
AI will be commonplace. Operational knowledge won’t
When we work on deploying agents in complex enterprise environments, we see that an agent can have access to the most advanced model on the market, highly sophisticated intelligence and every corporate system and still not know how to act in many situations.
If we want to trust agents to operate within our businesses, they need to understand how the business operates.
Before generative AI, much of this way of working was embedded in applications. Agentic AI is different. Its flexibility and adaptability come precisely from the fact that not everything is predefined.
An AI system, for example, may understand banking regulations perfectly but not an organization’s own risk criteria. It may understand how a supply chain works but not the decisions and exceptions learned through 20 years of operations. It may know customer service best practices but not what delivering a differentiated experience means for a particular brand.
A model provides general intelligence. But the company must provide the specific knowledge that turns that intelligence into its own way of operating.
In other words, AI needs the right context to act appropriately within each process and support the right decisions.
And I believe this will be one of the fundamental differences between companies over the next decade: their ability to capture, model, govern and continuously update their distinctive knowledge so that it becomes AI-ready usable by both people and agents.
This knowledge goes far beyond data and semantics. It includes processes, rules, policies, decisions, exceptions, experience and historical context.
It should also incorporate something much harder to represent: the principles and criteria that embody an organization’s culture.
If agents are gradually becoming active participants in business processes, knowing the data isn’t enough. They also need to understand “how we do things here.”
So, how much of the way we decide, execute and learn are we turning into living context for these agents?
We call this capability Company Brain: the operational memory that can turn decades of company experience into competitive advantage in the AI era.
When the technology itself starts to look increasingly similar, Company Brain could be what sets your company apart.
Which leads to another question: Is your operational knowledge already structured and governed so that people and agents can use it or is it still scattered across systems, documents and the minds of experts?
In my next post, I’ll explore what Company Brain is and what it isn’t.