Full episode: The Ten-Person Shop That Out-Transformed the Banks, with Jessi Szurek, associate partner, Synthesis —
On 6 August 2026, Challenger, Gray & Christmas published its count of announced job cuts in the United States for the first seven months of the year: 477,033, of which 112,713 named artificial intelligence as the reason.1 AI has now led the firm’s table of stated causes for five consecutive months.
A few weeks earlier, in a ten-person auto body shop that had been in one family since 1983, a woman with two decades of banking and insurance transformation behind her switched on an AI receptionist for a few hundred dollars a month. She did it because the previous owners could not open their own voicemail. The mailbox had a password. Nobody knew it. The phone contract was in the name of the seller’s father, who had died years earlier, so there was no way to reset it, and for as long as anyone could remember customers had been leaving messages nobody would ever hear — in a trade where an unanswered call is a customer ringing the next name on the list.
My guest this week is Jessi Szurek, associate partner at Synthesis, who spent the better part of twenty years inside transformation programmes at global financial institutions before her husband acquired the shop and she found herself doing the same job with no budget, no committee, and nobody to hand it to. Tony Stark in the cave, with a box of scraps.
Here is the argument I want to make, and I want it labelled as mine rather than smuggled in as reportage. The ten-person shop did not beat the banks because it had better technology. It beat them because it had less to unlearn, a higher tolerance for being wrong, and — for this one shop, by marriage — free access to the single thing most small businesses cannot afford. Everything else in this piece is evidence for that sentence, and for what it means for the 112,713 people who were told this year that a machine took their job.
The voicemail was never a technology problem
Jessi did not arrive with cameras in the repair bay or barcodes on every part. She and her husband spent six to eight weeks watching how the business ran, and how the family that had run it since 1983 did things and why, before they changed anything. Then they asked the question she says she puts to global banks and to companies of any size: what is the smallest use case I can define? What is the minimum viable product — the smallest version of the change that produces a visible result, so the people who have to live with it can see it work before they are asked to trust it — that can be delivered in days or weeks and show value to the owners, the users, and anyone else with a stake in it?
The answer was the phones. An AI phone service that answers, takes the call, handles the most common enquiry in the building, which is a customer asking where their car is, and does it 24 hours a day. A few hundred dollars a month, against the cost of a person sitting by a phone around the clock. She gave it a woman’s name so that the staff would talk about it as a colleague who picked up for Mr Smith about his Tacoma rather than as the machine. The effect she describes is not a productivity statistic. It is that the office staff stopped being nervous about going to the bathroom, because for the first time there was always someone to answer.
She had, in other words, walked into a business with no procurement function, no project team, and no change budget, and run the same play she runs at a bank. She once inherited a mandate to assess 420 processes at a financial institution and cut it to ten, on the grounds that by the time you have assessed 420 of anything with care the world has moved on. Same instinct, different scale. The difference is what happened next. At the shop the phones were live within days. At a bank, the same idea would still be waiting for a steering committee.
That gap is the mechanism, and I want to be precise about what it is. A small business has less to unlearn, which is the obvious half. The less obvious half is risk. If the shop’s phone system had been wrong, the cost of pivoting was a few hundred dollars and an awkward week. If a bank’s programme is wrong, the cost is a write-off and somebody’s career — neither of which appears on the business case — so the entire organisation optimises for never being visibly wrong, and the value gets stuck in what I have come to think of as proof-of-concept purgatory: pilots that are never allowed to fail and never allowed to escape into a use case that pays. Enterprise AI is full of it. The ten-person shop has no purgatory, because it has no committee to keep things there.
Now the uncomfortable part, which the voicemail story is good at hiding. The shop had Jessi. Most shops do not. Willingness to take a risk is cheap; knowing where the opportunity is and how to reach it without falling into the standard holes is not, and it is precisely the thing a ten-person business cannot buy at a price it can pay. The constraint on small-business transformation is not appetite. It is access to experience and expertise, and the honest reading of this episode is that one shop in the United States got twenty years of it for free.
The job losses are real. The reason attached to them is a claim
Back to the 112,713. Every one of those reasons was supplied by the employer that made the cut. Challenger counts announcements; it does not audit them. The firm itself notes that naming AI in a layoff announcement can win over investors, which is one explanation for why the messaging has swung from hedging to citing it aggressively.1
Ask an economist to find the effect and the picture changes. In May, the Budget Lab at Yale compared employment in occupations exposed to AI with comparable occupations that are not, controlling for education, gender composition, and how cyclical the work is. Its conclusion: no strong evidence of impacts as of yet, with an employment estimate that is close to zero and cannot be distinguished from it, statistically speaking.2 The same holds for wages. Ask the executives privately and the picture changes again. A National Bureau of Economic Research working paper from March, built on a survey of nearly 750 chief financial officers run by the Atlanta and Richmond Federal Reserve banks — executives answering confidentially, which is where the number tends to shrink — finds little evidence of near-term aggregate employment declines due to AI, records that larger firms expect workforce reductions while smaller firms expect modest gains, and states that the near-term goal of AI investment is productivity rather than headcount.3 Paul Osterman, professor emeritus at MIT Sloan, put it without the hedge in May: AI is a perfect excuse to justify big layoffs. It makes it seem as if it’s not our decision, our fault — it’s the technology. They have, he says, been saying that for twenty years.4
Jessi’s version is shorter. She does not, she said on tape, necessarily agree that AI is going to reduce the number of employees; she thinks it may be a CYA situation because large organisations want to reduce headcount, and a reason to give to the news, the public, and the team you are letting go. That is her reading and I am reporting it as hers.
Mine is close to it, with one boundary I want drawn clearly. This is a claim about corporate behaviour in 2026. It is not a claim about what AI will eventually do to work. Roles will be displaced over the long run as they are redefined around what the technology can do, and anyone telling you otherwise is selling something. But that is not what is happening now. What is happening now is that boards are making cuts on the hope that the productivity arrives later to justify them, and for a good many of them the hope will not be realised, because the transformation that was supposed to fill the gap is sitting in purgatory next to everyone else’s. Businesses will always need people. I would go further: AI is a leveller of productivity and quality across sectors, and levellers drive hiring, because the firm that gets the most throughput from humans and machines working together beats the firm that runs either one alone. Which is exactly what those 750 finance chiefs said the smaller companies expect.3
Ingka proves her right and wrong in the same year
I raised IKEA on the episode from memory, as the case of a large company that kept 8,500 call-centre staff rather than making them redundant, and asked whether there had been a business reason for it. Jessi’s answer was the honest one: I don’t know. That’s a good question. So here is the record, because it is better than either of us made it sound.
Ingka Group, which operates most IKEA stores, introduced an AI assistant called Billie in 2021 and retrained roughly 8,500 contact-centre workers to handle the complex queries the bot could not and to sell interior design remotely. Billie now assists 74 per cent of customers, up from 47 per cent in its first two years. Remote sales reached €1.25 billion last financial year, from €1.08 billion the year before, and are growing at 15 to 20 per cent a year. The in-house customer satisfaction score went from 60 to 89 per cent.5 That is not a cultural gesture. It is one of the clearest documented cases anywhere of a machine absorbing the mundane while the people move to the meaningful, and the meaningful turned out to be a billion-euro sales channel.
Then the part neither of us knew. In March 2026 Ingka announced it would cut around 800 office jobs. In May, Inter IKEA, the franchisor, cut about 850 more.6 Neither touched the remote-sales centres. Neither was attributed to AI. Ingka’s chief digital officer, Parag Parekh, is on the record that any cuts are likely to be the result of macroeconomic factors rather than the technology, and he did not rule out more.5
Which is where Jessi’s sharpest line runs out of road. CYA is right about a great many of the 112,713 and too neat as a general law, because here is a company that made 1,650 people redundant in a single year and declined the excuse that was sitting there for the taking. My reading of Parekh’s statement is that it is an example of good corporate governance — a company told the truth about why it was cutting costs, when the fashionable lie was free — and I want that labelled as opinion, because so is the rest of it. Whether those 1,650 roles were ordinary attrition and economics or something else, I have no facts either way, and nor does anyone writing about this case outside Leiden.
There is a second thing the Ingka record does, and it turns on my thesis rather than hers. Ingka is a megacorp, and it did what the body shop did: one frontline process, a named owner, a machine on the repetitive calls, and the people who had been answering them moved into work the business could sell. The banks in Jessi’s twenty years had more money than the shop and more money than Ingka. What they likely lacked, was an operating model that could start small, be wrong cheaply, and move. Size was never the variable. The operating model is.
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Experience untethered from outcomes is an opinion, and opinions are now free
I asked Jessi whether consulting is solving the problem of organisations that cannot see their own processes, or living off it. Her answer was that it depends: on whether the client wants an answer or a validation, on whether the board wants an independent view, on whether the firm is there to create value or is focused on the spreadsheet and on margin and billable hours. All of which is true, and all of which is the answer Sir Humphrey Appleby (Yes, Minister) would give if you asked him whether the department was solving the problem or living off it.
I will be less diplomatic, and this is a position rather than a finding. Any consultancy that is not tied to outcome-based delivery is part of the problem. Experience and expertise are valuable, and I would not have spent an hour and a half with Jessi if I thought otherwise. But experience untethered from an outcome is an opinion, and an opinion is now available from your favourite chatbot for twenty dollars a month. What a client cannot get from the chatbot is somebody who has agreed to be measured on whether the change happened.
That is also the point at which I should declare an interest, because Arkava sells agentic automation and an episode arguing that AI is being used as cover for cuts is, commercially, an odd thing for me to publish. The way we keep ourselves honest is the order in which we do things. Purpose and controls first: what does this organisation exist to do, and what must it never do. Then every activity rated against that as either meaningful or mundane. Then, and only then, the smallest use case that moves something mundane onto a machine and something meaningful onto a person. The minimum viable product is the starting point of delivery. It is not the strategy, and a great deal of what passes for AI transformation is an MVP with no purpose above it — which is how you end up with a mandate to assess 420 processes.
Jessi’s advice to the employee who receives the email saying AI is coming is worth repeating. If it arrives sounding like doom and gloom, her first instinct — delivered as a joke and meant, I think, more than half seriously — was that she would start looking for a new job. Her second was that an announcement is at least a sign the organisation is willing to talk, so ask what it means for you, and if you have an idea that would make your job or your business ten times more effective, this is the week to say so. Do not treat a programme called Doctor Doom as though it were weather. Her warning to the people running it was the mirror image: you can build the perfect AI agent army with no employees at all, and if your customers will not interact with it, you have built a failure.
Predictive judgement — Jessi’s, on the record
This show asks every guest to call a prediction, and she did. Her words, from the recording of 26 August 2026:
I think that it’s likely in the next one to two years that we might see that cut in resources. But I think that organisations will quickly realise, that was not the answer, and then we’ll sort of see a rebound. So maybe there will be a bit of a recession, because the unemployment rate is quite high, and then people will have to pivot and we’ll find a new way of working. And I think that we’ll be more efficiently and effectively using technology. We will also need people, and the unemployment rate will decrease again.
I am holding her to the far edge of her own window: 26 August 2028. Her prediction stands if, by that date, the share of announced US job cuts attributing AI as the cause has fallen from the roughly 24 per cent Challenger recorded for January to July 2026, and hiring announcements in the same series have risen from the 107,500 recorded over the same seven months.1 Two further signals worth watching: the next round of the Atlanta and Richmond Fed survey of chief financial officers, and whether the larger firms in it have moved towards the smaller firms’ expectation of modest gains;3 and the Budget Lab’s continuing series on AI-exposed occupations, which is the closest thing anyone has to an audited answer.2
What would falsify it. If by that date the Budget Lab or an equivalent study finds a clear, statistically distinguishable fall in employment across AI-exposed occupations, and the AI-attributed share of announced cuts has held or risen, then the cuts were what they said they were, the rebound did not come, and she was wrong. I will say so here, and I will have been wrong with her.
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The bottom line
A bank has more money than a body shop. It has a technology function, a procurement function, a change budget, and a transformation office. It has, in most cases, a multi-year programme with a name on it — occasionally a supervillain’s — and it has spent the year explaining to the market that the redundancies were the machine’s idea.
The shop had a voicemail nobody could open, one experienced person, and a few hundred dollars a month. Its phones have been answered around the clock since the summer.
You do not have to be a megacorp to be an AI-optimised business. Bring in the experience you lack, start with the smallest thing that can be seen to work, and be willing to be wrong cheaply. Do that and a small organisation goes further and faster than the largest ones can hope to manage, because the money was never the constraint. Fear was, and the operating model built to contain it.
Ten people beat the banks. They will not be the last.
References
Amer Altaf is founder and chief executive of Arkava® and managing editor of The Control Layer. Views expressed by guests are their own. The full episode is on YouTube at
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Decision-grade analysis on AI, cybersecurity, technology sovereignty, and the geopolitics of the technology stack — written for the board paper, not the timeline. By Amer Altaf, Founder & CEO of Arkava and Managing Editor of The Control Layer.