I often say in talks that the next ten years may bring more change than the previous hundred years combined. It is a large statement, and lately I have been thinking more about what it actually means for people leading organizations.
What I keep returning to is that this is not one change. It is happening at three levels at once, and we often discuss them as though they were the same conversation.
There is what is happening globally. There is what is changing inside organizations. And there is what people are experiencing in their own work and careers.
For leaders, these three levels are connected.
1. The global level
At the global level, AI has become much bigger than a technology story.
There are serious questions about safety and security. Economies will change as AI changes how work is done, and the global workforce is already beginning to feel that shift.
Beneath all of this is an even larger question: what kind of society are we building as AI becomes part of everyday life?
Governments are working out how these systems should be governed while the technology itself is still moving. Many of the rules and standards are being shaped in real time, and no one has a finished answer.
What we can already see is that the decisions being made now will reach far beyond the companies building the technology. They will affect how people work and how opportunities are created.
It is easy to look at all of this and feel that these decisions belong to someone else. Governments. Technology companies. Policy experts.
I do not think they do.
The choices made inside organizations are part of this, including the questions leaders are willing to raise about how AI is used.
The scale of the issue may be global, but the direction is still shaped by people. That includes the people leading organizations.
2. Organizations: management is changing
Inside organizations, the change is already much more practical.
The technology conversation is moving faster than the management conversation. AI is giving people new management responsibilities faster than organizations are preparing them to take them on.
This is the management work behind Human-AI Management: helping people direct, review, and take responsibility for work that is increasingly carried out with intelligent systems.
AI agents are already entering workflows. People are beginning to direct and review work done by AI agents, and decide when a person needs to step in.
That raises a new set of management questions:
- How do we prepare people to manage and oversee AI agents?
- What decisions should remain with people?
- What controls do we need around how AI agents are used?
- How do we keep human accountability clear as AI becomes part of everyday work?
These are becoming leadership questions.
For the past few years, much of the focus has been on AI technology strategy. Now I am hearing a different conversation in leadership rooms: what management strategy do we need for the people who will be overseeing these systems?
That shift matters because most people taking on these responsibilities have never been trained to manage anything other than people. Yet they may soon be responsible for work carried out partly by AI and expected to judge whether that work is good enough to use.
This is why one idea from The Human-Agent Orchestrator continues to resonate so strongly:
We are the last generation to manage only humans.
Organizations now need to prepare people to manage work that is increasingly supported and carried out by AI agents and intelligent systems.
And the question I keep taking back into these conversations is becoming very practical:
What management principles do we need to help people oversee increasingly capable AI systems and make good decisions around their work?
3. People: work is changing underneath the role
This is the level closest to people, and the one I hear discussed least in leadership conversations.
People’s work will change because the work inside organizations is changing. Often the title stays the same while the work itself changes underneath it. What people actually do on a Tuesday can change long before anything on paper does.
The knowledge people have spent years building becomes more important, and at the same time harder to see. Much of it is tacit knowledge: knowing when something is going wrong before anyone says it out loud, or recognizing a problem because they have seen it before.
Much of that knowledge has never been written down in a way that organizations can see clearly.
For leaders, that matters.
As work changes quickly, organizations need to know what people have learned and find ways to help that knowledge remain connected to the people and teams who have built it.
People also need to learn how to make AI work for them so that it increases the value they can create with what they already know. An experienced professional can use AI to research faster and take on work that once required far more time or support.
That is where I see one of the biggest opportunities.
AI can help people do more with the experience they have already built.
