The Effort Dividend

Suits You, Sir

There is a scene that plays out on Savile Row every day. A customer walks in, selects a cloth, and a cutter takes measurements that no off-the-rack garment has ever accounted for. The suit that emerges weeks later fits precisely because it was built for one person — their posture, their shoulders, their preference for how a cuff should break.

This is a caricature, of course — a deliberate simplification. But it captures where we find ourselves with technology today. The tailored version of a tool — shaped around how your organisation actually works, rather than how a vendor wants it to — is no longer reserved for the largest budgets. It is reserved for effort.

Not talent. Not access.

Effort.

That distinction will only become more pronounced.

When Capability Was Scarce

For most of the history of enterprise technology, the binding constraint was capability. Organisations asked: "Can we build this?" The answer depended on whether you had engineers with the right specialisms, whether you had the budget to hire them, and whether you had the patience to wait for the result.

This created a natural hierarchy. Large organisations built. Smaller ones bought. The smallest made do. The Build, Buy, Partner framework emerged as a way of thinking about this — a rational response to a world where technical capability was expensive, scarce, and slow to deploy.

The framework was sound. But the assumption beneath it — that capability is the scarce resource — has shifted.

The New Scarcity

AI-supported tooling did not make capability free. It lowered the barrier to execution for a significant range of tasks — drafting, analysing, prototyping, synthesising — to the point where effort, not expertise, becomes the binding constraint.

There is evidence for this. Studies consistently show that people with greater domain expertise extract more value from AI-assisted tooling. But there is a compounding effect at work too: AI can teach you whilst you use it. Your capability grows through the act of applying it — if you use it correctly. The gap between novice and competent narrows faster than it ever has.

You no longer need to be a specialist to produce a competent first draft of a regulatory summary. You do not need a development team to prototype a workflow. You need someone willing to sit with the problem, iterate, and refine.

The question is no longer "Can we do this?" It is "Do we have the capacity to see this through?"

That is a different question entirely. And it rewards a different kind of organisation.

What Effort Actually Means

Effort is not about working longer hours or pushing harder (he says, at 4am, after eighteen hours with AI today). It is about the willingness to engage deeply with a problem — to describe what you actually need, then work through the experience of clarification and remediation. You learn a lot about a problem when you look to solve rather than tolerate.

When capability was scarce, you hired it. When access was scarce, you paid for it. Effort can be purchased (a job), but it must be deployed with persistence, not merely allocated. It is not time spent doing something. It is time focused on solving the same problem, or the small set of connected problems, over an extended period.

This is why smaller, focused organisations are increasingly capable of producing work that previously required teams of specialists. Not because AI has made those specialists redundant, but because AI has expanded the effective reach of people who are willing to apply sustained effort to a problem.

The specialist remains essential. The difference is that the specialist's work now has more surface area to act upon.

The Question Inverts

There is a subtle but important shift in how you approach technology when effort becomes the constraint rather than capability.

The old orientation was: "How do I fit my process around this tool?" You selected a platform, adapted your workflows to accommodate its assumptions, and accepted the gaps. The tool was the fixed point. Your organisation moved around it.

The emerging orientation is different. It asks: "What would I build that would give me more capacity?" The tool becomes the raw material. Your organisation's needs become the fixed point.

This is not a claim that everyone should build everything. It is an observation that the threshold at which building becomes rational has moved — significantly — and most organisations have not adjusted their thinking accordingly.

The Calculus Has Changed

The Build, Buy, Partner framework remains useful. But the weights in the equation have shifted.

Build now costs less in capability terms. AI-assisted development, prototyping, and iteration reduce the expertise required to produce useful internal tools. The risk is lower. The timeframes are shorter.

Buy retains its advantages for commodity needs — where the problem is well-understood and the market offers mature solutions. But the edge cases, the integration gaps, the places where a vendor's product does not quite fit your regulatory context or your operational reality — these are now addressable in ways they previously were not.

Partner becomes more interesting. When your organisation can prototype and validate ideas quickly, partnerships can be scoped more tightly and evaluated more rigorously. You spend less time negotiating scope and more time testing outcomes.

For regulated industries — financial services, defence, legal, education — this shift carries particular weight. These are sectors where off-the-rack solutions rarely fit well, and where the cost of adaptation is amplified by compliance requirements, governance obligations, and the sheer complexity of legacy environments.

The organisation that can afford to build precisely what it needs — not because it has an unlimited budget, but because it has people willing to apply effort to the problem — has a meaningful advantage.

The Real Constraint

Effort alone is insufficient. All things being equal, if you work harder, you will get further. But as Angela Duckworth observed in Grit, if someone like Michael Phelps is in the pool, he only has to work as hard as you do — because he has a physiological advantage. Expertise, governance, and good judgement remain essential, particularly in regulated environments where the cost of error is high. AI does not remove the need for these things. If anything, it increases it.

So the balance has shifted. The return on deliberately applied effort — focused, persistent, directed at the right problems — has never been higher.

The organisations that recognise this — and allocate their effort deliberately rather than defaulting to old assumptions about what is and is not possible — will find that the "suits you, sir" version of their tools is closer than they thought.

It was always a question of budget, then capacity. The weights have changed.