We've observed decades of tech adoption begin inside large organisations and then slowly trickle down to SMBs and individuals, but I'm beginning to suspect the reverse is happening with AI.
For every tale of woe I hear about inconclusive ROI and fruitless AI pilots within large organisations, I hear about a freelancer or self-employed person — unencumbered by corporate IT and procurement policies — who has lashed together various AI tools, workflows, and cloud services to utterly transform their personal productivity. It seems the secret has been hiding in plain sight all this time: productivity is personal, not organisational.
Previous enterprise shifts — ERP, CRM, Cloud — were built on the process as the unit of value. They required top-down, command-and-control rollouts to prevent left-hand/right-hand chaos and waste. But AI's unit of value is the individual: their specific thinking, creativity, drafting, and judgment. A technology that augments the idiosyncratic way a human mind works will always be adopted more readily by people acting in an individual capacity.
The data bears this out. Among large companies with more than 250 employees, AI adoption has dipped from a peak of 14% to 12%¹. Among freelancers, it stands at 75%².
Why does scale make it worse?
When it comes to rolling out AI, large organisations responded with the only playbook available to them: standardise, govern, procure, roll out, measure. Copilot licences were purchased, lunch-and-learns were scheduled, and 42% of companies abandoned most AI initiatives in 2025³, up from 17% the year before.
The problem contradicts everything large organisations believe about their own advantages: diseconomies of scale. In most enterprise tech deployments, scale amplifies, but with AI, the reverse seems true.
The coordination overhead of deploying a personalised technology stack to hundreds or thousands of diverse workers compounds with every layer of governance. When an organisation forces a single-vendor configuration on its staff — as corporate procurement policies usually dictate — it sacrifices the very thing that makes AI valuable today: the high-fidelity fit between the tool and the individual user's unique cognitive style.
If economies of scale built the enterprise, diseconomies of scale are frustrating its ability to harness AI.
Headcount reduction is not a consequence of AI adoption. It looks more like a precondition.
The current wave of large-scale tech layoffs reshaping global organisations like Oracle, Amazon, and Block is not, in the main, caused by AI replacing workers, as the media usually reports it. Instead, it is being driven by boards who have concluded that organisation-wide AI adoption cannot be coaxed, encouraged or even mandated.
Instead, it will be forced by circumstance.
The re-org, redundancy programme, or RIF (Reduction In Force) is the lever usually yanked when large organisations need to enact a major change in direction or strategy. CEOs can demand that their managers prudently constrain headcount, rationalise and manage costs and encourage the adoption of AI in their teams, but it's more efficient to simply remove the optionality.
So, my sense is these cuts are coming ahead of the AI productivity gains, not because of them. When there are fewer people to absorb the work, necessity will become the mother of invention, and those who remain will be forced to find new ways to do it — with AI tools.
The self-employed are building personal AI operating systems while large organisations are still negotiating procurement terms. How that gap closes is the more interesting question.
Sources: Fortune ¹, 2727 Coworking ², S&P Global Market Intelligence ³.