ServiceNow’s recent work on the rise of the agentic enterprise identifies four foundational capabilities that will define the next generation of organizational performance: data, workflows, orchestration, and governance. The framework pushes the conversation beyond AI experimentation toward institutional design. That’s where executive attention needs to focus.
For the past two years, most organizations have experimented with AI. They’ve deployed copilots, launched pilots, and tested narrow use cases. But as ServiceNow argues, the challenge is no longer gaining access to AI. The challenge now is building the enterprise to operationalize it. That marks the beginning of a much larger transformation.
The first stage of the agentic enterprise embeds enterprise-owned AI agents into workflows. These agents automate processes, augment employees, and increasingly execute tasks on behalf of the organization. The next stage will look fundamentally different. It will involve the rise of what I call “identic AI.”
Identic AI is not AI for you; it’s AI as you.
It’s a personal intelligent agent trained on your knowledge, goals, values, and preferences. It learns continuously, travels with you, and aligns with your interests. Enterprise AI agents belong to the company. Identic AI belongs to the individual. That distinction may define the next era of management.
For the first time, employees will come to work with their own intelligent agents. Customers will engage with companies through their own AI agents. Suppliers and partners will do the same. The enterprise will no longer operate as a closed system of internal intelligence; it will function as a network of interacting intelligences. That shift changes the nature of the business itself.
Then came the personal computer. Suddenly, knowledge workers had computing power on their desks. Spreadsheets transformed finance. Word processors transformed communication. Desktop databases changed local decision-making. Productivity rose dramatically, but organizations remained fragmented. Intelligence became distributed, but coordination did not.
Nearly 90 years ago, Nobel-winning economist Ronald Coase explained why businesses exist. His theory was simple: Organizations internalize activity when transaction costs in the open market run too high. Coordinating work inside an enterprise often costs less than coordinating it through a market.
Digital technologies have lowered those transaction costs for decades. The web reduced search costs. Cloud computing reduced infrastructure costs. Platforms reduced coordination costs. Identic AI may further reduce those costs.
When individuals use intelligent agents to negotiate, coordinate, learn, and execute on their behalf, the economics of coordination change again. Functions once performed more efficiently inside the business may now operate more efficiently across open networks.
That pushes organizations to look less like hierarchies and more like dynamic networks. We’ve seen hints of this before. The networked enterprise was one of the defining ideas of the internet age. But identic AI may make that idea operational at scale.
Instead of managing rigid internal structures, organizations will orchestrate fluid networks of employees, contractors, customers, suppliers, and machine agents. Enterprise boundaries will become more porous, more dynamic, and more permeable. The business becomes less a container of talent and more a platform for coordinating talent.
Peter Drucker once described management as the booster of the faint signals that passed for communication in the Industrial Age and early Information Age. Managers amplified, interpreted, and coordinated weak signals moving through the organization. They created coherence out of limited visibility.
The signal environment changes dramatically when every employee gains access to immense intelligence, every process generates continuous feedback, and every decision draws on real-time analysis. Organizations move from information scarcity to intelligence abundance. Management no longer revolves around transmitting information, but rather shaping context.
I’ve always held that context determines what matters. AI can synthesize information, but leaders still establish priorities, define values, and set direction. Management becomes more, not less, strategic.
That shift also transforms execution. Ram Charan and Larry Bossidy argued in their bestselling book, Execution: The Discipline of Getting Things Done, that execution, not strategy, is what really matters in success. That insight may no longer be true. AI agents can increasingly take over much of the machinery of execution.
AI agents monitor workflows, allocate resources, track milestones, identify risks, and optimize decisions in real time. They coordinate across functions faster than humans and operate continuously without pause.
For decades, organizations promoted managers because they executed exceptionally well. That still matters. But as execution becomes more automated, the premium transitions upward toward imagination, judgment, and strategic clarity.
Leaders will spend less time asking whether people are doing things right and more time asking whether the organization is doing the right things.
This also redefines technology leadership. The chief information officer remains important, but the title has become too narrow. AI is not a department. It forms the foundation of the new enterprise. It shapes workflows, decisions, governance, talent, and strategy. That makes AI every manager’s business.
The most immediate challenge for managers may be this: How do you manage someone with an infinite number of vice presidents?
That question captures the reality of identic AI. A junior employee with a powerful personal AI agent may gain access to expertise, research capability, market intelligence, legal reasoning, and strategic analysis that once required an executive suite.
Capability democratizes. Organizational dynamics move. Authority changes. Expectations evolve. Experience takes on a new meaning. A fresh graduate entering the workforce may, through a personal AI agent, wield functional capability that rivals that of a seasoned executive in many domains.
That’s not to say that experience loses value. Value simply migrates. Human judgment, wisdom, trust, and relationships become more important. Information advantages become less important because extraordinary intelligence becomes widely accessible.
What about the HR department? Human resources will feel this transition immediately. When hiring people, organizations will still look at resumes. But increasingly, they may also examine a candidate’s personal AI agent. How does it reason? What does it know? How does it learn? What capabilities has it developed?
The person and the AI agent may function as a single unit of capability. That reality raises difficult questions about knowledge ownership:
- When someone leaves an organization, what stays and what goes?
- If identic AI has learned from enterprise workflows, internal knowledge, and strategic context, where do leaders draw the boundary between personal intelligence and corporate intellectual property?
Those governance questions will become central. ServiceNow rightly emphasizes governance as one of the pillars of the agentic enterprise. But identic AI expands the governance challenge. Leaders will need to govern not only enterprise systems, but also the interaction between enterprise AI agents and personal AI agents.
I've spent more than 40 years helping companies navigate successive waves of technological change. The rise of AI reminds me of the early days of the personal computer, although on a vastly larger scale.
When the personal computer emerged in the 1980s, it transformed individual productivity. Spreadsheets revolutionized finance. Word processors changed communication. Desktop databases gave knowledge workers analytical capabilities that had previously been unimaginable. But the PC, by itself, did not reinvent the enterprise.
In many companies, valuable information simply became trapped on individual hard drives. The real transformation occurred only when personal computers became connected through local area networks, enterprise applications, and eventually the internet. Individual productivity became enterprise capability.
AI is not simply another PC. Whereas the PC amplified human productivity, AI amplifies human intelligence. Increasingly, AI reasons, learns, decides, and acts on our behalf. Its impact on business will be orders of magnitude greater than anything the PC achieved. But the lesson of the PC era still applies: If capability remains isolated at the individual level, the enterprise will never realize its full potential.
That’s why the rise of identic AI creates both an extraordinary opportunity and a profound management challenge. Every employee will soon have a persistent digital counterpart that understands their knowledge, relationships, priorities, and objectives.
These systems will dramatically increase individual capability. But unless that intelligence can be connected through shared enterprise context, governed workflows, institutional memory, and trusted coordination, the enterprise will simply replace PC silos with AI silos.
There’s a second problem that’s even more dangerous: enterprise sovereignty. If the intelligence infrastructure of an organization runs entirely on platforms owned and controlled by a handful of frontier AI companies, the enterprise risks surrendering control over its most valuable emerging asset: the accumulated intelligence of the organization.
Every prompt, workflow, decision, customer interaction, exception, and successful outcome can reveal how the business truly operates. Together, they constitute a living map of the company’s knowledge, processes, relationships, judgment, and intellectual property.
This doesn’t mean that organizations should avoid frontier models. These systems will remain important components of the enterprise technology stack. But leaders should not confuse access to a powerful model with ownership of enterprise intelligence.
The enterprise must control its data, institutional memory, AI agent identities, permissions, workflows, and the intelligence generated through their use. It must also be able to change models or providers without losing that accumulated knowledge. Otherwise, the organization risks becoming dependent on an external platform that increasingly understands its business while the company fails to build a durable intelligence asset of its own.
The challenge for leaders is therefore no longer simply to deploy better AI, nor even merely to connect it. It’s to transform individual intelligence into a shared and sovereign enterprise capability, one that belongs to the business, compounds within the business, and can be governed by the business in accordance with its own strategy, values, and obligations.
The enterprises that lead the AI era won’t necessarily be those with the most powerful models or the smartest AI agents. They’ll be those that design their organizations so that human and machine intelligence continuously reinforce one another, allowing knowledge, judgment, and experience to compound into an enduring enterprise capability.
The agentic enterprise is emerging now. Identic AI marks the next stage in that evolution.
Done right, it will enable the redesign of the enterprise, probably the biggest change to how capability is orchestrated to innovate and create goods, services, and shareholder value since the founding of the organization itself.
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