This week’s article is a guest post by Carol McEwan, organizational effectiveness expert. Thanks to Carol for sharing this important point of view.
Something I say a lot is, “It’s not important to you until it’s important to you.”
Two years ago, Melissa Reeve saw where AI was heading. It was important enough to her that she wrote Hyperadaptive: Rewiring Your Organization to Become AI-Native.
The premise was not that organizations needed another technology transformation. It was that AI would expose something deeper: organizational systems designed for a world that no longer exists.
At the time, that conversation was early. It may not have been important to you.
Yet.
It isn’t early anymore.
For the last few months, I have been reviewing new research each week across organizational readiness, strategic alignment, operational agility, and the practical use of AI in strategy execution. Across those briefs, I have watched the questions leaders are asking become broader, harder, and much more urgent.
A familiar question keeps coming up:
How do organizations scale AI?
But that question brings others with it:
How do humans and AI work together?
How should workflows and decision rights change?
How do we govern systems that can increasingly act, not just advise?
How do we keep strategy, investment, and execution connected?
What happens when the organizational system itself becomes the constraint?
For example, McKinsey’s August 2026 survey finds that high-performing AI organizations are more likely to redesign workflows and have committed leadership. Microsoft’s 2025 Work Trend Index describes a shift toward teams of humans and AI agents, with people setting direction and overseeing workflows.
These reports approach the issue differently, but my takeaway is the same: the way the organization works deserves as much attention as the technology it adopts.
The challenge is not simply AI adoption.
The challenge is the systems surrounding AI.
Moving beyond random acts of AI
You may have heard Melissa refer to “random acts of AI.” The phrase resonates because people immediately recognize what it describes.
A team adopts a copilot. Someone experiments with an agent. A function automates a process. HR launches AI training. IT builds infrastructure. A business unit creates its own AI strategy. Another creates a governance committee. Someone buys another platform.
None of that is necessarily wrong. But activity is not the same as capability.
In many organizations, increasingly powerful technology is being layered onto systems designed for slower information flows, centralized decision-making, annual planning cycles, fixed structures, rigid budgets, functional silos, and human-only work.
The technology is moving faster than the systems around it.
That is when random acts of AI start colliding with reality.
What does a constrained system look like?
Many of these problems do not initially look like organizational-system problems.
Execution: We can’t get the pilots to scale.
Governance: Everything takes too long to approve.
Alignment: Everyone agrees on the strategy, but we keep funding things that don’t support it.
Talent: We don’t have the right AI skills.
Culture: People are resisting.
Delivery: We can’t move fast enough.
Technology: Our data isn’t ready.
Learning: We can’t keep up with the pace of change.
Step back for a moment and ask yourself:
Is the system making the desired behavior difficult, risky, or impossible?
For example:
Are slow decisions the result of unclear authority?
Is poor alignment the result of disconnected planning, investment, and execution?
Is resistance a rational response to incentives, risk, or ambiguity?
These questions are worth considering because AI did not create these problems.
AI did not create slow decision-making, rigid budgeting, fragmented information, competing priorities, or functional silos. It did not create the gap between strategy and execution. It did not create organizations where leaders ask people to act autonomously but still require escalation for every important decision.
These are age-old conditions creating age-old problems. AI is simply making them harder to ignore.
And because AI is accelerating the speed at which work, information, experimentation, and decisions can move, those constraints are becoming more visible and more costly.
Creating the conditions for change
AI readiness. Does that sound familiar? Do we have the data, the technology, the governance, the skills, and the leadership support? Don’t get me wrong. Those are useful questions. But you will never “be ready.”
The moment you think you are, the technology changes. The work changes. Customer expectations change. Regulation changes. Competitive pressures change. The capabilities your organization needs change.
So perhaps the more important question is not whether your organization is ready for AI. It is whether the organizational system has created the conditions for change.
Can your organization sense what is happening, understand what matters, make decisions, move people and investment toward what matters most, learn from the results, and adapt continuously?
Not just once. Continuously.
Organizations put enormous effort into product design. We study customers, observe behavior, identify friction, test assumptions, build feedback loops, measure outcomes, learn, and improve.
Yet the systems through which thousands of people make decisions, respond to customers, share information, manage risk, and execute strategy are often inherited rather than intentionally designed. When those systems stop working, we tend to blame the people or reorganize the boxes rather than examine the system people are being asked to work within.
By organizational system, I do not mean the org chart.
I mean how purpose guides decisions, how information and authority move, how priorities and investments are determined, how work crosses boundaries, how learning happens, how incentives shape behavior, and how strategy becomes action.
Those are design choices. They should be intentional.
This does not mean throwing away the frameworks and practices you already use. There is real value in that work. The question is how it connects across the organization. Improving how teams deliver will only take you so far if funding, decision-making, governance, and incentives keep pulling in different directions. We need to build on what works and connect it so the whole organization can benefit.
Maybe one last transformation
I have never been particularly fond of transformations.
The word suggests there is a destination. We are here. We need to get there. So we create a program to close the gap.
But what happens when “there” keeps moving?
AI will not be the last disruption. The technology will continue to change. The work will continue to change. Customer expectations, regulation, competition, and the capabilities organizations need will continue to change.
So maybe the goal is not another transformation.
Maybe it is one last transformation: replacing episodic change with the capability for continuous adaptation.
A shift toward an organizational system that creates the conditions for change rather than waiting until change becomes urgent enough to require another transformation.
Because random acts of AI will not fix an organizational system that is getting in the way.
And if any of the symptoms in this article felt familiar, perhaps this has become important to you.
Start with something that keeps getting stuck. Follow one decision from the moment someone knows what needs to happen to the moment they are able to act. Where does it wait? Who has the authority? What makes it difficult to move forward? That is one place to begin seeing the system.
So where is your organization today?
Before deciding what to change, it helps to understand where you are.
The HyperAdaptive Waypoint Finder can help you see what may be enabling your organization today, what may be getting in the way, and where you might focus next.
It only takes a few minutes and it is free.
About Carol McEwan
For more than 30 years, I’ve worked across organizational effectiveness, Business Process Re-engineering, operating-model design, workforce planning, leadership, technology, and organizational growth — as an advisor, Vice President, Managing Director, and CEO.
I’m also known as “Community Carol.” Community building has been an important part of my career, but I see it as part of the larger organizational system: how people connect, how information moves, how decisions get made, and whether people are truly able to contribute.
Today, I’m particularly interested in what AI means for how organizations operate — not simply how they adopt AI tools, but how AI changes work, decision-making, knowledge, authority, and the ability to continuously adapt.
I’m most interested in focused engagements where I can quickly understand what is really going on, help leaders see the system clearly, and determine what to do next, and can be reached at: https://www.linkedin.com/in/cmcewan/