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# Notes · VANDAM

> Notes on AI diffusion, org design and how companies change, with every number linked to its source.

URL: https://vandam.ai/notes  
Contact: hendrik@vibelab.be

# Notes on how companies change.

What we read, measure and learn while redesigning companies around AI. Every number links to its source.

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.

## Fix a mistake once by turning every correction into a rule the agents read.

Anthropic's data team took Claude from 21% to above 95% accuracy with written skills, and now turns every correction a colleague makes into a rule and a test for the person who owns that domain. I set out that loop for a mid-sized company, with where the rules live, who owns them, and the one number a COO should track: how often the same mistake comes back.

## Plan for the week your AI vendor goes away, before someone else picks the date.

In February 2026 defense contractors started moving off Claude within days, for reasons unrelated to the model, and every provider now publishes retirement dates for the models you depend on. Why the switching cost sits in prompts, evals, traces and data access, and the supplier-risk checklist a CFO and COO can run before the next renewal.

## Most of your AI list needs software and only a few items need an agent.

Agents still finish a minority of office tasks and give different answers on repeated runs, while a written rule does the same thing every time for almost nothing. I set out four questions that sort every request into software, agent or person, and apply them to ten typical ones.

## Your outside firms got faster with AI and you are still paying for the old hours.

Law firms, auditors, agencies and outsourcers are putting AI into the work they bill you for, and most contracts still price the old hours. KPMG showed how to ask for the saving back, and I set out the questions a CFO should send each supplier before the next renewal.

## Check every invoice, not one in twenty, now that a judgment costs a tenth of a cent.

Sampling exists because reading every document used to cost a person’s time, and a small model now reads an invoice with its PO and receipt for less than a tenth of a cent. I work through the arithmetic, what the PCAOB and the ACFE say about whole populations, and a monthly plan and budget for a 500-person company.

26 October 2026 8 min read

## The work you never did because it never paid is where the largest AI return sits.

Most AI business cases count the hours of work a company already does, which caps the return at today's labor cost. The evidence from Jevons to bank tellers says cheaper work expands where demand was rationed, and I set out how a leadership team lists, prices and picks the expert work it skips.

## Ask what your agents can reach because any text they read can give them orders.

Prompt injection tops OWASP’s list of LLM risks, and every frontier model in a NIST-run red-teaming contest was hijacked at least once, so the limit has to sit in what an agent can touch. I lay out the one-page reach review we use and how to give employees who bring their own agents a sandbox instead of a ban.

25 October 2026 8 min read

## Why most AI pilots stall and how to choose one that scales.

MIT's famous 95% figure measures less than the headline says, but larger surveys from S&P Global, Gartner and McKinsey point the same way. The pilots that reach production share five conditions, and a CEO can check for them before the first demo.

## Measure the work before you let AI change it.

Eight in ten survey respondents say AI makes them more productive, yet only about a third of companies can find it in EBIT, and few measured the work before it changed. Why the companies with credible AI numbers all name a starting point, and a three-week baseline method a CFO can run before the next AI purchase.

## Before you automate anything, find out where the hours actually go.

Most AI shortlists are built from the visible work, while the hours sit in coordination, waiting and exceptions that no process chart shows. The evidence from 1990 to 2026, and the two-week work map we use to rank tasks by hours times feasibility.

25 October 2026 9 min read

## What managers do now that part of every team is agents.

Amazon, Microsoft and Meta cut management layers while agents took on coordination work, and the managers who remain carry wider spans and a second team to supervise. What Gallup and Anthropic's data say about that job, and a manager role redesigned around it.

25 October 2026 7 min read

## Once agents do the middle of the work, your people should own both ends.

Balaji Srinivasan’s line that AI works middle-to-middle is the most useful frame I know for redesigning a role. This note covers what sits at the start and the end of a task, what 1,500 workers told Stanford they want, and how I would rewrite one production manager’s morning.

## How to tell your people what AI will change about their jobs.

Most employees can see AI arriving, and only a quarter say their employer has explained a plan. What silence and shifting explanations cost, a communication sequence for the CEO and CHRO, and what never to promise.

## The AI line in your budget should be priced per finished task.

AI spend is moving from seats and headcount to tokens and outcomes, while most finance teams still count licenses. Intercom, Box, Podium and Varick show what the cost side of AI looks like, and I name the three numbers I would ask a CFO to own.

## AI spreads through a few people who build for their own work.

Shopify and Duolingo both told every employee to use AI, and Duolingo dropped its usage metric a year later. The people who actually spread AI build tools for their own work, and a company can give them time, status, budget and a way to share what works.

## What changes in your controls when an agent can post to the ledger.

Once an AI agent posts entries, approves invoices or touches vendor records, it needs a place in the access matrix, a change process and a human who signs for it. What COSO, the PCAOB and the audit firms actually say, and the checklist we use for an agent in accounts payable.

## What a firm should keep inside once agents make coordination cheap.

In 1937 Ronald Coase explained that firms exist because using the market costs money. Agents now make buying, building and internal coordination cheaper all at once, and I list the five comparisons I would rerun at a 300-person company.

## AI transformation has ten steps and only one of them is AI.

Alex Lieberman of Tenex listed the ten steps it takes to make a process AI-native. I walked them through one quote process at a parts distributor to show where the weeks go, and why the map pays off before any model does.

24 October 2026 7 min read

## Europe can lose the model race and still win on AI.

At a keynote this week I heard the case that Europe should stop chasing frontier models and put everything into getting AI used in every company. I checked the evidence behind it, read the strongest objection, and worked out what it means for a single firm.

## Who should do your AI work, now that the labs sell it too.

In 2026 Anthropic and OpenAI set up services companies, Sequoia and Y Combinator told founders to sell the work, and Accenture bought an AI firm. A mid-sized company now chooses between a product, a big consultancy, a lab-backed team and a small senior team.

24 October 2026 6 min read

## Who trains your next senior once agents do the junior work.

Four studies from the past year point the same way: AI is thinning the entry-level rung, mostly by slowing hiring. The judgment employers now look for was learned on that rung, so I set out what I would change as an employer.

## What your company knows becomes the asset once agents do the work.

Agents only know what someone wrote down, and most companies record the outcome of a decision while losing the reason, the exception and the person who said yes. This note explains why that record compounds and lists the four things I would start capturing this month.

## 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.

24 October 2026 8 min read

## What Anthropic found when its agents outworked its people.

Anthropic measured its own company after agent hours passed human hours six to one, and the findings are about attention, management, hiring and people moving. I read the report as a story and draw out what it means for a company of 300.

## The new org chart has written rules.

Block, Coinbase and Fiverr published how they are reorganizing around AI. The rules are concrete, most of them came with layoffs, and the people side decides whether they work.

## Why our AI team hires from HR and finance.

Most AI projects speed up the process a company already has, and redesigning it takes people who know where the money and the jobs sit. One month-end close, walked step by step, shows why they belong next to the engineers and why VANDAM is hiring them.