This distinction is becoming increasingly important because intelligence, by itself, has never been the difficult part of running a business.

Every organization already contains enormous amounts of intelligence. It exists in experienced employees, documented procedures, engineering standards, operational playbooks, historical project data, enterprise applications, and now, increasingly, in AI systems capable of generating recommendations in seconds. The challenge has rarely been creating more information. The challenge has always been bringing that information together at the moment people need to make decisions.

Artificial intelligence amplifies that challenge.

Every new AI assistant, every specialized agent, and every automated workflow introduces another participant into an already complex operating environment. While each system may perform its own task exceptionally well, few understand the wider operational context in which the organization is actually working. One agent may optimize a schedule while another recommends procurement changes, yet neither understands the commercial commitments, engineering constraints, regulatory requirements, or decisions being made elsewhere in the business.

The result is not a shortage of intelligence.

It is a shortage of coordination.

This is a problem that traditional enterprise software was never designed to solve. ERP systems manage transactions. CRM systems manage customer relationships. Engineering platforms manage design information. Project controls manage schedules. Collaboration platforms facilitate communication. Each performs an important function, but each represents only one part of the operational picture.

As AI becomes embedded across all of these systems, the need for something above them begins to emerge.

Rather than another application competing for users' attention, organizations increasingly need a way of connecting the intelligence that already exists across their business. They need a shared operational understanding that links people, AI agents, enterprise systems, workflows, decisions, and changing business conditions into a single, coordinated view of execution.

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This is the capability I believe will define the next generation of enterprise software.

It is what we describe as Operational Coordination Intelligence.

Unlike systems of record, which capture what has happened, or AI assistants, which generate recommendations, an Operational Coordination Intelligence layer focuses on how work is coordinated as it happens. It maintains the relationships between activities, decisions, dependencies, documents, people, and AI agents, allowing each participant to operate with a richer understanding of the wider context.

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That distinction matters because organizations do not create value through isolated decisions. They create value through coordinated execution.

A single decision on a capital project can affect engineering, procurement, construction, commissioning, commercial commitments, safety, and operations. The quality of that decision depends less on how intelligent the recommendation is than on whether everyone affected understands its implications and can respond accordingly.

The same principle applies far beyond industrial projects. Healthcare, logistics, manufacturing, financial services, government, and technology companies all face the same underlying challenge. As organizations become increasingly dependent on AI, they also become increasingly dependent on coordination.

Ironically, the more intelligence we introduce into an enterprise, the more valuable coordination becomes.

This shift has implications that extend well beyond operational efficiency.

Every coordinated decision creates knowledge. Every resolved issue, every successful workflow, every interaction between people and AI contributes to a growing understanding of how the organization operates. When captured and governed effectively, that knowledge does not disappear when a project finishes or an employee leaves. Instead, it becomes part of the organization's accumulated capability.

Over time, this accumulated capability becomes something far more valuable than better project execution.

It becomes intellectual capital.

For decades, organizations have measured financial assets, physical assets, and increasingly digital assets. The next decade may see a growing recognition that an organization's ability to continuously create, govern, and reuse operational knowledge represents another strategic asset entirely. Companies that coordinate work effectively will not simply execute more efficiently. They will build knowledge that compounds over time, becoming increasingly difficult for competitors to replicate.

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That is why I believe the conversation around enterprise AI is beginning to change.

The first phase was about making machines more intelligent.

The second phase will be about making organizations more coordinated.

Those that recognize this shift early will not only extract greater value from AI, they will begin building one of the most important assets of the AI era: their intellectual capital.

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