Why our AI team
hires from HR and finance.

In September, Box CEO Aaron Levie shared a post on redesigning processes for AI agents. He quoted one paragraph from an article whose author his post does not name, and it is the most accurate description of enterprise AI I read this year: “Nobody in that chain is empowered to walk into finance, for example, and say that their 14 step process should actually only be 5 steps. So nobody does it. Instead, you ‘apply AI’ on what you currently have, and you end up with faster sh*t.”

Levie’s own comment was that the important workflows cross many functions at once, “so someone needs to be able to go in and retool how work happens.” I read that as a staffing problem. Whoever retools a finance process has to understand finance well enough to know which steps can go. Whoever retools a hiring process has to understand what happens to the people inside it. An engineer can build either one, once somebody has decided what it should look like.

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.

Seventy percent of the value sits outside the code

BCG has a rule of thumb for this, the 10-20-70 rule. The firm puts 10% of the value of an AI transformation in the AI application itself, 20% in the data and technology underneath it, and the remaining 70% in “workflow redesign, culture, governance, and human-AI collaboration.” A team made only of engineers is staffed for the 30%.

Annelies Gamble, a partner at Zetta Venture Partners, makes the same point from the deployment side in her essay “The Agent Is Not the Product.” Her split is the clearest I know: “Deterministic automation is for rules. Agents are for judgment under context. Humans are for accountability, ambiguity, and trust.” Sorting work into those three buckets is a process question before it is a technical one. She also quotes Ahmad Kakar, CEO of Euclid, on what happens when you skip that sorting and copy what people do today straight into an agent.

If you just capture it and encode it into the agent, you’ve automated the dysfunction.Ahmad Kakar, CEO of Euclid, quoted by Annelies Gamble

Kakar’s fuller point is that much of what people call tribal knowledge is “workarounds people built for non-existent or broken systems,” and that the real work is “deciding what was real judgment versus scar tissue.” That decision needs someone who knows the function from the inside.

One month-end close, three kinds of people

To make this concrete, take a month-end close at a mid-sized company. This is an illustration built from how closes usually run; no client is behind it. The controller’s team spends the first days of every month reconciling bank accounts, chasing missing invoices, booking accruals, matching intercompany balances, collecting sign-offs and writing the commentary that explains why each cost center landed where it did. A good share of those steps exist because two systems never agreed with each other, and somebody built a spreadsheet to bridge them years ago.

Finance goes first. Before anything is built, someone with real close experience walks the list and labels every step. A second approval on journal entries above a threshold is a control the auditors will ask about, so it stays. A manual reconciliation that exists because the ERP never matched supplier credit notes is scar tissue, so it goes, or gets fixed at the source. The same person sets the baseline: how many days the close takes today, whose hours it uses, how many corrections land after the books are closed. Without that baseline nobody can say later whether the redesign paid for itself.

Engineering builds the smaller process. With the steps sorted, the build gets plain in the right places. Matching bank lines to open invoices follows rules, so it becomes ordinary software. Explaining why marketing overspent in March needs context from the ERP, the contracts folder and last quarter’s forecast, so that is where an agent earns a place. The engineers connect to the systems the company already runs, log every action the agent takes, and test it against a set of past cases that finance has labeled as right or wrong.

HR redesigns the jobs, human and agent. The two accountants who used to spend the first week ticking boxes now review exceptions and sign off on the agent’s commentary. Their role has changed, so someone has to write the new description, talk to them before the change lands, rethink how their work is evaluated, and decide what a junior learns now that the junior tasks are gone. In much of Europe, employee representatives have to hear about it too, early and in writing.

Agents need job descriptions too

The agent in that close needs the same treatment as the accountants. People running agent teams have started writing it down. Vox, a builder who posts AI experiments as @Voxyz_ai, gave every agent “a role card. what they own, what they deliver, what they can’t touch, when to escalate. went from vibes to actual job descriptions.” Kevin Simback, COO of Delphi Labs, runs his agents through hiring, onboarding and a four-level ladder: Observer, Advisor, Operator, Autonomous. They move up through performance reviews, and down too: “I had a content agent at L3 who started rushing work. Quality dropped so I bumped it back to L2 for a week.”

When you hire a new employee IRL, you don’t just give them root keys on day 1 - you loop them in as you gain trust over time. You should do the same with your agents.

Writing a role card, setting the criteria for a promotion and running a fair review are HR skills. In the close, the variance agent starts as an observer: it drafts commentary that a controller rewrites. After two clean closes it becomes an advisor whose drafts go out once approved. Who decides that it has earned the step, and what counts as clean, is a people question with a finance threshold attached. Engineers can enforce the permissions. They should not be the ones setting them.

After go-live, finance keeps score

Sahil Bloom, author of The 5 Types of Wealth, wrote in February that the biggest adoption challenge for companies outside tech comes later: “actually getting value out of the agents after they’re deployed.” Without that, he warned, you end up with “a bunch of fancy looking AI agents gathering dust on the shelves.” His proposed fix was to hire people from the industry itself, ex-finance people in his example, to train the agents and keep improving them.

The buyers feel the same gap. In a March 2026 survey of 204 finance leaders, Gartner found that 63% of finance organizations said AI implementation was slower than expected in 2025. Marco Steecker, a director analyst in its finance practice, told CFOs that counts of pilots and tools “do not prove that AI is delivering the value boards now expect,” and that they “must not mistake activity for impact.” On the people side, Gartner asked 426 CHROs about their priorities for 2026, and AI transformation came out on top, as HRD reported.

70%

Share of AI transformation value in workflow redesign, culture, governance and collaboration, per BCG.

6156

Technical departments as a percentage of headcount at Anthropic, over the last year.

The last number comes from the company furthest along. In its October report on its own workforce, Anthropic writes that technical hiring kept growing, but “intensive hiring in sales, finance, and recruiting has outpaced it.” The authors say part of that is a scaling company adding business functions, so I would not read it as proof of anything on its own. It does match what I see from the outside: once agents do more of the building, the scarce people are the ones who know what should be built and who can tell whether it worked.

The strongest objection to all of this is that HR and finance are where change goes to slow down, and that a redesign team should keep them at arm’s length. Sometimes that is fair. A finance team that defends every step because it has always been there, or an HR team that treats role changes as a paperwork exercise, will produce exactly the faster version of the old process. That is why I want people from those functions who have changed a process themselves, and who want to do it again on a larger scale.

VANDAM is hiring for this

I am building VANDAM as a mixed team, and right now I am looking for three kinds of people to sit next to our engineers:

• Finance. You have run or rebuilt a close, a planning cycle or a procure-to-pay process, and you can tell a control from a habit by reading the steps.
• HR. You have redesigned roles, run performance cycles or led an organization through a restructuring, and you are curious what a job description for an agent should say.
• Product. You can take what we learn across client redesigns and turn the repeatable parts into software we ship.

If that is you, write to me at hendrik@vibelab.be with one process you would redesign and the first step you would cut.