Why AI licenses rarely make a company faster. · VANDAM

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# Why AI licenses rarely make a company faster. · VANDAM

> Adoption counts logins, diffusion changes the work. Why 88% of firms use AI, 5% get value, and what redesigning the work looks like inside one company.

URL: https://vandam.ai/notes/adoption-vs-diffusion  
Contact: hendrik@vibelab.be

# Why AI licenses rarely make a company faster.

Most companies now count AI logins. The few that get value from it changed the work itself first, the way factories once had to rebuild around the electric motor.

Hendrik De Winne 24 October 2026 6 min read

A leader of an operations organization with thousands of people told [Vas (@vasuman)](https://x.com/vasuman/status/2085806422072418632), the CEO of [Varick Agents](https://varickagents.com), that his team had been moving at a decent rate before AI. Then they rolled out Claude Cowork. In Vas’s words, “the team kept moving at the same rate.”

He describes the usage behind that flat line as a barbell. **5‑10%** of the people became power users, with skill files and connectors to Outlook. Of the rest, **20%** used it a couple of times a day, “pretty poorly.” The remaining **70%** did not use it at all. It is one company, told by someone who sells agents for a living, so I read it as an observation and not as a study. I still recognize the shape of it from most companies I walk into.

> The dashboards in his org would indicate that the rollout counts as adoption. But to him, nothing got faster. Both of these were true simultaneously.
>
> Vas, CEO, Varick Agents, August 2026, [Source](https://x.com/vasuman/status/2085806422072418632)

Both of those were true at the same time because they measure different things. A dashboard counts who logged in. The leader was asking whether the work moved faster. Most companies I meet have bought the first and are waiting for the second to follow by itself.

## Every measure shows the same gap

The pattern holds at much larger scale. In March, [Anthropic](https://www.anthropic.com/research/labor-market-impacts) compared what language models could do in theory with what people actually use Claude for. In computer and math jobs, the occupations closest to the technology, models could take on **94%** of the tasks. Claude covered **33%**.

Could do

94%

Actually used

33%

Tasks in computer and math jobs. Source:

[Anthropic](https://www.anthropic.com/research/labor-market-impacts)

, March 2026.

That is the group with the most technical people, the most curiosity and the least fear of the tools. The capability is already there. The space between the two bars is made of habits, handoffs, approval steps and job descriptions written before any of this existed.

The surveys of companies say the same thing from the other end. [McKinsey](https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-ai) found that **88%** of organizations use AI regularly in at least one business function. [BCG](https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap) puts the share of firms getting value from it at scale at **5%**, and says 60% are reaping hardly any material value at all.

- **88%**
  Organizations that use AI, according to [McKinsey](https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-ai), 2025.
- **5%**
  Firms getting value from AI at scale, according to [BCG](https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap), 2025.
- **3×**
  How much more likely high performers are to have fundamentally redesigned their workflows, per [McKinsey](https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-ai).

The third number is the one I care about. When McKinsey looked at what separates the small group of high performers from everyone else, one of the clearest differences was that they were nearly three times as likely to have fundamentally redesigned their workflows. They bought the same models as everyone else and kept going until the work itself looked different.

These are self-reported surveys, and I hold them loosely. But the direction never changes, whichever firm runs the survey and however it defines value: wide use, thin results, and a small group that rebuilt how the work flows.

## Factories took decades to learn this once

The economic historian Paul David described the same gap in the electrification of factories, in [The Dynamo and the Computer](https://gwern.net/doc/economics/automation/1989-david.pdf). When electric motors arrived, many factories took out the steam engine and put an electric one in its place. The line shafts and belts that ran the length of the building stayed where they were, and so did the layout of the floor, the order of the work and the jobs of the people standing next to the machines. Electricity was in the building, and productivity barely moved.

The gains came when factories were redesigned around what electricity made possible: a small motor on each machine, which meant machines could be placed where the flow of work wanted them instead of where the shaft ran. That change was slow, because it meant new buildings, new layouts and new ways of organizing people. The technology had been ready for a long time before the companies were.

A chatbot added to an unchanged workflow is the dynamo bolted to the old line shaft. Everyone has access, the invoice still passes through the same inboxes, and the dashboard reports adoption.

> The best way to get real efficiency gains is you have to actually reengineer the process to take advantage of what agents are actually good at and can do differently and faster than people.
>
> Aaron Levie, CEO, Box, September 2026, [Source](https://x.com/levie/status/2096279756925026733)

## Historians and central bankers call it diffusion

Jeffrey Ding, a political scientist at George Washington University, made the national version of this argument in [Technology and the Rise of Great Powers](https://press.princeton.edu/node/51524). He looks at Britain in the first industrial revolution, America and Germany overtaking Britain in the second, and Japan’s challenge to America in the third. The countries that pulled ahead were the ones better at adapting new technologies at scale, and his theory stresses “institutional adaptations oriented around diffusing technological advances throughout the entire economy.” Invention mattered less than spreading it.

The European Central Bank now says something close to that about AI. In a speech in March, [Philip Lane](https://www.ecb.europa.eu/press/key/date/2026/html/ecb.sp260323_1~1e06784a89.en.html), a member of the ECB’s Executive Board, said the aggregate productivity impact of AI “may vary widely, primarily driven by the speed of diffusion,” and that “firm adoption and organisational restructuring are historically the main channels” through which technologies like this have paid off.

When a central banker and a historian of great powers land on the same variable, I take it seriously. Both point at how fast ordinary companies change the way they work, whoever ends up with the best model.

That is the distinction I use with every client. Adoption is people having access to a tool, and you can buy it in a quarter. Diffusion is the work itself changing shape around the tool: who does which step, what gets checked by whom, which approvals disappear, which roles grow. That part only the company itself can do, one workflow at a time.

## Inside one company: map, redesign, then build

Vas makes a point in the same post that I agree with: most people in an organization will never become power users, and a plan that depends on them doing so will fail. Training still matters, mostly to find out who belongs in the top slice. For everyone else the work has to change around them, with agents doing defined parts of it inside the systems they already use, and people approving, correcting and deciding.

Getting there takes three steps, and the order matters.

First we map the work. We follow one workflow end to end, step by step, with the people who do it: where the information comes from, who touches it, where it waits, which steps are judgment and which are workarounds for a system that never worked. This is slow and unglamorous, and it is where most of the value is found. A lot of what looks like expertise in a process turns out to be a habit built around an old tool.

Then we redesign the roles. Once the steps are on the wall, the questions get concrete. Which steps go to an agent, which stay human, and who reviews the agent’s output in what time. Who owns the workflow when it spans finance and operations. What a job looks like when someone stops typing invoices into an ERP and starts checking the exceptions. This is the part most companies skip, because it touches job descriptions, incentives and sometimes the org chart, and it needs HR and finance in the room as much as engineers.

Only then do we build the agents, against a redesigned process with a clear owner and a baseline measured before anything goes live. Built in that order, an agent has an owner and a number to move. Built first, it tends to end up as one more tool on the adoption dashboard.

Report what share of the work today is manual vs hybrid vs fully automated. ([Vas](https://x.com/vasuman/status/2085806422072418632), Varick Agents)

That is the number I would put in front of a board instead of logins. It moves slowly. It moves one workflow at a time, and every move is visible in the work itself. The first quarter of a redesign can look worse than the quarter before it, because people are learning a new way of working while the old numbers are still being reported.

The companies in McKinsey’s small group did not get there with better licenses. They picked the work that mattered, took it apart, and rebuilt it with the tools in mind. For most companies, that work has not started yet, and the licenses are already paid for.

## More notes

29 October 2026 6 min read

## Who owns what your AI learns.

Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections.

26 October 2026 9 min read

## Delete the step first and give the agent whatever survives.

Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go.

## Your agents need a critic and so does the plan they carry out.

Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.