Most AI Strategies Are Doing Maths On People. Very Few Like The Answer.

Why multiplication, not automation, is the thing worth chasing.

I've been sat in a lot of conversations recently where the AI questions sound perfectly reasonable on the surface.

How do we save time? How do we get ROI? How do we make sure we're not left behind?

All sensible and commercially justifiable. And almost none of them get to the question that actually tells you something useful.

What is your AI strategy doing to your people?

Not what it's doing for them. What it's doing to them.

I started thinking about this differently a couple of months ago when I was speaking to an SME business. They had a clear AI rollout plan, good exec sponsorship, decent adoption numbers. On paper, it was a success story.

But when I asked what had actually changed about how people worked, they couldn't point to a single role that had become genuinely more capable because of AI. Faster, yes. Lighter, maybe. But more capable? No.

That conversation is what eventually became the AI Maths Framework. And the core idea is pretty simple.

Every AI strategy is doing one of four things to its workforce. It's either dividing, subtracting, adding, or multiplying. The maths tells you what leadership actually believes people are for, even if nobody's said it out loud.

Division

This one's blunt.

AI becomes the reason to cut roles, break teams apart, redistribute work to whoever's left standing. The leadership question, whether it's asked explicitly or not, is: where can we cut?

Division can look sharp in a board pack. Margins tighten. Headcount drops. The narrative sounds decisive.

But the hidden cost is usually bigger than anyone admits.

You lose the institutional knowledge that took years to build. You damage the trust in ways that don't show up in an engagement survey for another 12 months. And you teach the remaining workforce something very specific:

AI is something that happens to them, not something that works with them.

The efficiency story lands well with the board. The cultural damage takes longer to surface, and by then, leadership's already moved on to the next priority.

Worth saying clearly: this isn't transformation. It's cost pressure in a more modern outfit.

Subtraction

Subtraction sounds better than division, and often genuinely is.

Here, AI removes low-value tasks. The admin gets automated, reporting speeds up, first drafts appear in seconds and manual processing shrinks. The leadership question shifts to: what can we take off people's plates?

That's an improvement on division because you're not removing the person.

You're removing the task.

But subtraction has a ceiling, and I think a lot of organisations will hit it without recognising what's happening.

If you subtract work without redesigning the role around the space you've created, you don't build capability. You just create a gap. And if there's no plan for that gap, it fills with drift, confusion or a kind of low-level anxiety that's hard to pin down.

People start asking questions they probably won't say out loud in a team meeting:

What's my job now, exactly?

If AI can do more of the task, where's my value?

Is this role growing or quietly disappearing?

Subtraction is often where organisations congratulate themselves too early.

Addition

This is where most firms will believe they're winning.

AI tools get rolled out, licences are bought, training sessions happen, prompting workshops appear in the calendar, usage dashboards get reviewed. The leadership question becomes: how do we give people AI?

The intent is usually good. Genuinely. Leaders want to equip their teams, not just reduce them.

But addition has a trap that's easy to miss.

If you add tools without changing workflows, role expectations, decision rights, management habits, or how you measure performance, you haven't transformed the way work happens. You've bolted a new capability onto an old operating model.

I keep coming back to this because it explains where we see AI programmes that create a lot of activity without creating any real advantage. Or the 95% of AI pilots that get disbanded when they hit the real world.

People use the tools, managers mention AI in their updates and the board sees adoption numbers that look healthy. But the organisation is still doing the same work in the same way, just with slightly more assistance.

I'm not sure that's enough. Actually, I'm fairly sure it isn't.

Addition is better than subtraction. But it's not the prize.

Multiplication

This is the level I think is worth chasing. And it's the one I see least often.

Multiplication is where AI doesn't just help people do existing work faster. It helps them do work they couldn't do before.

The leadership question changes completely. Instead of "how do we give people AI?", it becomes "what could our people do that they couldn't do six months ago?"

That one change in perspective lands differently when you sit with it.

• A finance team stops producing reports and starts interpreting patterns, guiding decisions that wouldn't have surfaced otherwise.
• A client team moves from reactive service to proactive insight because they've got bandwidth and data they didn't have before.
• A specialist who was bottlenecked by volume starts operating with genuine range and speed they didn't have before.

In multiplication, the role itself expands. Work becomes qualitatively different. And this is where something interesting happens commercially, because while every competitor in your sector can buy the same licence, far fewer can redesign work, management, governance, and role expectations well enough to actually multiply their people.

That redesign is where the advantage sits. Not in the tool.

Why this matters more than I originally thought

When I first started framing this, I thought of it mainly as a diagnostic. A way to help leadership teams see where they are.

But the more I've played with it, the more I think the distinction between addition and multiplication is the strategic question in leadership right now.

If your end goal is efficiency, you'll almost certainly end up in a race to the middle. Everyone in your sector will eventually automate admin, generate first drafts, summarise meetings, accelerate routine analysis. Those gains matter but they don't create a gap between you and the competition. They're table stakes within a couple of years.

Multiplication is different because it changes what the organisation can actually do. Not just how fast it does existing things.

It creates teams that solve better problems. It produces roles that are more valuable to clients and customers, not just cheaper to run. It gives your best people a reason to stay because the work gets richer instead of thinner.

Still working this out, honestly, but I think that's the divide forming.

Not between who's adopted AI and who hasn't, but between who's using it to cut costs and who's using it to build capability that compounds.

The bit most organisations skip

The jump from addition to multiplication isn't mainly a technology problem, it's a leadership one.

It means asking whether roles, workflows, and expectations have been redesigned around what humans and AI do well together, not just whether people are logging in and using the tools.

It means managers becoming capability builders rather than adoption chasers.

And it probably means different measures of success. Not just time saved or tasks automated, but new capability created and new kinds of work made possible that simply weren't before.

This is where organisations could stall. Not because the tools aren't good enough. Because the operating model around the tools hasn't changed.

AI doesn't multiply people on its own. That bit takes leadership willing to rethink what great work actually looks like.

Some questions I've been sitting with

Rather than a checklist (there are enough of those), here are the questions I keep coming back to when I'm working through this with clients.

• Are we using AI to reduce people, reduce tasks, add tools, or expand what people can do?
• Are teams doing qualitatively different work, or just the same work at higher speed?
• Would our best people say AI has made their role more interesting and more valuable?
• Can we point to a capability the organisation has today that it genuinely didn't have a year ago?

If the answer to that last one is no, then there might be strong AI adoption happening, but probably not multiplication yet.

I think that distinction is going to matter a lot over the next couple of years.

The organisations that come out ahead probably won't be the ones that installed AI fastest. They'll be the ones that most effectively expanded what their people could do and how far they could reach.

One last thought

I keep coming back to the idea that AI strategy reveals something about belief.

Whether leadership sees people primarily as a cost to be managed, a workload to be lightened, a user base to be equipped, or a capability to be multiplied.

That's probably why I think multiplication is worth treating as the goal. Not because it sounds better in a strategy document (although it does). Because it's the level where AI stops being a tool conversation and starts being a genuine strategy conversation.

And for anyone leading through this right now, I think that's the difference between incremental improvement and something that actually compounds.

The question isn't whether we're using AI. It's what maths we're doing on our people.