The Execution Curve

The organizations I've watched adopt AI move through the same stages in the same order, and it's worth being blunt with yourself about which one you're in, because nobody gets to skip one.

Stage one is individual productivity. Employees use AI on their own, prompting models directly to speed up their writing, coding, and analysis. Execution improves, but orchestration is still manual, and every project still depends on people coordinating the work by hand. Most organizations are here today, and while it's worth having, it's the shallow end of the curve.¹

Stage two is coordinated AI workflows. Individuals get proficient, teams settle into repeatable patterns, and work moves faster because everyone knows where AI pays off. Humans still orchestrate the work. They do it far more efficiently, with prompts and standards the whole team shares and a common idea of what the tools are for.

Stage three is harnesses and autonomous execution. Harnesses, agents, and automated orchestration only start to make sense once an organization has mature use cases to point them at. At that point AI is accelerating whole systems of work at once, and the organization's execution starts to multiply.

The order matters. Teams that reach for stage-three tooling before they've built stage-two habits mostly end up automating their own confusion, because an agent will run whatever process it's handed, including the one nobody on the team ever agreed on.

Maturity Changes What Tokens Buy

Budget conversations usually miss that the same dollar of AI spend buys different things at different points on the curve.

Early on, AI spending buys learning. People experiment, and the processes around them start to change. The tokens are tuition, and that's fine, because tuition is how you get to the later stages.

Later, the same spending buys throughput, where each additional dollar funds more projects, more products, and more work shipped to customers, and how mature the organization has become decides how efficiently every one of those tokens turns into a business outcome.²

The CEO's New Growth Dial

That puts leadership somewhere it has never been. In an AI-mature organization, executives get direct control over how fast the organization executes.

If the company has enough AI-proficient employees and mature enough workflows, leadership can set how aggressively to execute by deciding how much AI capacity to fund. Each increase puts more initiatives in motion at the same time, and a cut slows everything down just as directly. That is how you end up in a roadmap review where nobody brings a hiring plan, because the argument is about how fast the company wants to go and what it's willing to spend to get there.

A dial like that belongs on the CEO's desk. It sits badly in an IT budget, next to the software renewals.

The Strategic Constraint

Follow the curve far enough and an organization ends up with more execution capability than it's willing to fund. That's a strange sentence to write after decades of the opposite problem, when companies had more money than capacity and hiring was always the bottleneck.

At that point leadership has to choose. It can put more money into AI and grow faster, accept slower execution and leave more of the work manual, or shrink the organization's capacity to match what it's willing to spend. Those are business strategy calls, and they'll get argued about in board meetings.

I've written before about the catch, which is that faster execution only helps when the system around it keeps up. An organization that funds more execution than it can review and deploy gets a pileup of pull requests nobody has time to read and finished work waiting on a release.³

AI Changes Capital Allocation

Most companies still treat AI as a procurement question and spend their energy on vendors, license tiers, and seat counts. That framing is a bigger obstacle than any model or tool, because it budgets execution capacity the way you'd budget a software license. In a mature organization, AI spending isn't about buying software. It's about buying execution.

The companies that do well with this will decide how fast they want to move and then fund that decision deliberately, the way they'd fund a new plant or an acquisition.⁴

The ones that understand the curve first will be sitting in that planning meeting arguing about speed, while their competitors are still posting job openings for work they could have funded.