You cant add more on top of being full

Human One | Dimension 03: Organisations and Operating Models

When I start working with a new organisation on a transformation programme, I ask the same question early on: where are you creating space for this to happen?

I get the same look every time: puzzled, occasionally a little defensive, as if the question itself were the problem.

So I draw two circles.

The first is large, and it represents everything the people in the organisation, or more specifically the function or business, are already doing. Strategic priorities, current projects, operations and delivery, performance reviews, team leadership, budget management, risk and compliance, relationship and vendor management, and the accumulated weight of previous transformations that have not quite landed yet. People are already at capacity.

Already at capacity

The second circle is smaller, sometimes only slightly. It represents the transformation: new systems, new tools, new ways of working, new capabilities to build, new adoption behaviours, and the new operational activities that come with the change itself.

Then I ask: if, in circle one, it is not growing, by that I mean the workforce is not increasing, and the budget is not expanding, and the time available to deliver is not moving further out, how does the second circle fit inside the first?

Two demands. Both Real.

The circles cannot merge without something giving way. When organisations try to force them together, without making that trade deliberately, the result is exhaustion rather than transformation, and in most cases no real change at all.

"You can't add more on top of already being full."

Kate Collins, Human One, Oliver Kate, 2026

The weight that keeps growing

This is not a problem unique to AI. I have drawn these circles in digital transformation programmes, in new technology rollouts, and in post-merger integrations. Every time, the conversation about what to add comes before the conversation about what to stop and the second conversation, more often than not, never happens.

What makes AI different is the compounding effect of scale and pace, accelerated by the targets being placed in front of leadership teams that make inaction feel like a competitive risk. A McKinsey report published this month puts it directly:

“Together, these advances make close to 60 percent of work hours theoretically automatable.”

"The Symbiotic Enterprise", McKinsey QuantumBlack, June 2026

So reaching these levels of efficiencies are landing on top of organisations already managing cost pressure, restructure, hybrid working models, several years of accumulated change and a new transformation. Furthermore, research published by Harvard Business Review in June 2026 is direct on what most organisations are actually seeing:

“In the [AI] business world, there is a lot of activity that produces marginal rather than game-changing benefits, so far.”

Harvard Business Review, How People Are Really Using AI in 2026, June 2026

And that is a capacity problem rather than a technology problem: the tools are increasingly available; organisations have not created the space to use them well.

In the first two posts in this series, I wrote about the individual experience of this period: the feeling of not keeping up, and what sustained cognitive overload does to a brain under constant pressure. What most organisations are now doing with AI lands directly on top of that. The people already stretched, already trying to stay current, are the same people now being asked to absorb a transformation onto a plate that has not changed in size. The organisational response to AI is, in most cases, compounding the biological and psychological load that individuals are already carrying.

Making room: what to stop

To create space, organisations have two options. They can increase capacity, adding more people, budget, or time. Or they can reduce what is already on the plate, stopping or scaling back work of equivalent size to what they are adding. The second option is where real prioritisation needs to happen, and it is consistently the harder conversation to have.

In practice, the stopping conversation looks like this. Ask every team to identify what they are currently doing that AI could take off their plate, and then actually take it off. Name the trade publicly: we stopped doing X to make room for Y. That signal matters. It tells people that the space was created deliberately, that leadership made a real choice, and that the transformation is being taken seriously enough to give it room.

In most teams, particularly affecting digital and technology, this conversation needs to come from the top, and it needs to be unambiguous. IT projects, compliance requirements, operational initiatives, and strategic programmes compete continuously for the same finite capacity. If leadership wants AI, they need to state clearly what they will forgo to make it possible. The CIO, or equivalent leader, then has to hold that line. Any concession, even a small one, signals that the space that was promised is not actually protected, and people will notice.

Watch, too, for signs that people are trying to keep both circles full. The early indicators are recognisable: “We’ll do it ourselves”, constant experimentation without direction, pressure to use AI across every task regardless of fit, and longer working hours as people add new learning on top of an already full schedule.

Making it acceptable to say we do not need AI for this is itself a leadership decision.

Being honest: what to say

The first conversation clears the plate. The second is the harder one.

The second conversation nobody is having is the honest one about what AI actually means for the people in the room.

If an organisation acknowledges that this period is hard and then adds another mandatory AI learning module to an already full schedule, or breaks its rules on what it has agreed to stop, the message lands as hollow. The credibility of this conversation comes from evidence that something was actually cleared. The two conversations are connected: the first creates the conditions for the second to land.

Most employees will be more affected by the quality of honesty in this conversation than by any specific answers. They need to hear what the organisation knows and what it does not yet know, and a genuine commitment to communicate as things become clearer. Fear sits largely in the gap between "AI will transform everything" and any specific information about what that means for their role, their skills, and their future here.

Three things tend to help close that gap.

• Be explicit: about what AI will do in your organisation, what it will not do, and which human skills remain central to the work.

  
  

• Equip managers: they are the forgotten group in most change programmes, often anxious themselves, under pressure to drive adoption, without the frameworks to hold this conversation with their teams. Give them talking points, frequently asked questions, and the permission to say they do not have all the answers yet.

  
  

• And change the narrative: at every level. Framing AI as a tool that removes low-value work and creates capacity for what matters more is a message people can actually move toward.

None of this requires certainty about outcomes. It requires a decision to keep having the conversation, and to keep it honest.

Starting with people

Most leadership teams are actively thinking about how to implement AI solutions in their organisations. Few are thinking about what needs to change or stop to make it possible. After all, ROI remains the driving force. And unfortunately the human dimension of the change sits even further back in the queue, with fewer making plans on how to accommodate a future ecosystem of human and machine intelligence.

This is what the Human One wheel is designed to support. The wheel exists for a single executive conversation: which of these have we actually planned for?

It starts with knowing how your people feel, what they fear, and what they need. That question is not a stage that follows once the technology is deployed. If your organisation is already giving employees access to AI tools, or building its AI strategy, and your stated values are people-centric, this is the starting point.

No one has all the answers here, and I am not suggesting otherwise. What matters is whether the conversation is happening at all, and whether the people leading it are willing to be honest about the uncertainty. The Human One wheel brings structure to that conversation, and ensures the full picture of what AI is doing to people in your organisation gets onto the table.

What would your organisation need to stop doing to make room for the AI change it says it wants to make?