Chapter 18: When the Work Moves

In April 2025, Shopify’s chief executive, Tobi Lütke, published an internal memo after hearing it was being leaked. Its central instruction, as widely reported, was that before asking for more headcount, teams must demonstrate why they cannot get what they want done using AI. Shopify also said it would add questions about AI use to its performance and peer reviews.^[3] Whatever you think of the policy, it was a decision, made in the open and applied across the company.

The early labour-market evidence suggests the effects are real. Economists at Stanford’s Digital Economy Lab, using payroll records, found that employment of workers aged 22 to 25 in the occupations most exposed to AI "now stands 19% below where it would be had it kept pace with that of their less-exposed peers." The declines were concentrated "in occupations where AI usage primarily substitutes for human tasks"; where AI mainly complements workers, employment was flat or rising.^[4] That distinction, between agents that replace a task and agents that extend a person, will run through the rest of this chapter.

At CorralData the movement is smaller and closer. TAM Bot was asked to research the work of our technical account managers and learn from them. QA reviews moved from one agent to another, and some of what our QA lead used to do by hand is now done by her own bot. Customer emails have been drafted and sent by agents on people’s behalf. Some of this was planned. Some of it happened because someone with an agent and a free afternoon decided to get on with it.

That last kind is the problem. In a small, fast company, the default way work moves is not a decision. It is someone with an agent doing a task that used to belong to someone else, and the someone else finding out afterwards. This chapter argues for replacing that default with a decision.

One episode at CorralData taught me more about this than any policy discussion. An executive, frustrated on our customers' behalf that some follow-up work was overdue, used agents to draft and send the outstanding customer emails on a colleague’s behalf, signed as written jointly by the AI and the executive. At around the same time, that colleague was repairing customer dashboards by hand. In a public channel, the executive asked whether the colleague had tried asking the AI how to speed the work up, and if not, why not.

Every part of that is understandable. The executive could see, correctly, that AI could do much of this work faster. Customers were waiting. The question was genuine, asked by someone who uses AI for almost everything. It is the same instinct, to delegate and move, that makes executives good at working with agents in the first place (Chapter 1).

And yet, from the colleague’s side, the experience was different. Work that was theirs had been done around them: emails had gone to their customers without them. They were doing careful work by hand while being asked, in front of others, why they weren’t using a tool to go faster. Earlier, an automated sweep had reopened customer tickets they had closed, which made it harder to see what genuinely needed their attention.

To the executive’s real credit, when that earlier problem was raised, the response was immediate and generous: "this one’s on me." He also set a rule that fixed the whole class of error: "if you closed it, it stays closed unless the customer replies." That is a Friction Review in two sentences, and exactly the right instinct.

The lesson is structural, not personal. Nobody had decided that this work was moving from the colleague to an agent, so nobody had said who owned the agent’s output, who would check it, who would clean up when it went wrong, or what the colleague’s role was now. The colleague absorbed those costs by default, as individual contributors do unless the structure assigns them somewhere else (Chapter 19).

Lisanne Bainbridge described a version of this in 1983, writing about automated industrial plants. Her "ironies of automation" included the observation that automating a process tends to leave the human operator with the tasks the designers could not automate, which are often the hardest, while removing the routine practice that kept the operator’s skills sharp.^[5] When work moves to an agent without a decision, the person left behind gets the leftovers: the exceptions, the cleanup and the blame, without the authority to change the system that produced them.

The question at the centre of that episode deserves a fair hearing, because in a different setting it is exactly the right one. A leader who never asks whether work could be done faster with agents is not doing their job.

The difference is where and when the question is asked. Asked of one person, in public, while they are under pressure, it is heard as a judgment: you are slow, and it is your fault. Asked of a role, in advance, as part of planning, it is design: which parts of this role could an agent hold, what would it need, who would check its work, and what would the person do instead?

Shopify’s memo is an instructive contrast. Whatever its merits, it set the expectation in advance, for everyone, in writing, and tied it to a specific decision (asking for headcount) rather than to an individual’s performance in a difficult week.^[3] People can disagree with a rule like that and still know where they stand.

W. Edwards Deming made "drive out fear" one of his fourteen points for management, arguing that people cannot work effectively, or report problems honestly, when they are afraid.^[6] Fear is the natural response to work moving without warning. A person who suspects their job is being quietly automated will not tell you where the agent is failing; they will hide the failures or fix them by hand. Google’s reliability engineers build their post-mortems on the opposite assumption, that "everyone involved in an incident had good intentions," precisely so that people will report what really happened.^[7] Hidden failures are disastrous for an organisation trying to learn from its agents (Chapter 16).

So keep the question, and move it. Put it in the role’s charter review, not in the team channel. Apply it to every role, including the leader’s own. And give people time, examples and support to answer it before you treat AI use as a standard they are judged against.

If work is going to move to agents, someone has to sponsor those agents (Chapter 7). The tempting choice is whoever built the agent, often a leader or the most enthusiastic AI user. The better choice, nearly always, is the person who did the work.

The clearest example at CorralData is QA. In November, responsibility moved from the bot that first did it to QA Reviewer, which belongs to our QA lead (Chapter 17 tells the handover from the agents' side). What mattered on the people side was that the person who understood QA best now owned the agent doing it. She insisted that the knowledge the old bot had gathered not be lost, and that both run in parallel before anything was switched off. Those are the instincts of someone who knows what a missed defect costs.

There are three reasons the domain expert makes the best sponsor.

They know the failure modes. Someone who has done a job for a year knows where it goes wrong: the customer who always phrases things ambiguously, the data source that is stale on Mondays, the edge case the manual never mentions. That knowledge is exactly what an agent’s playbooks need. Anthropic compares writing an agent skill to "putting together an onboarding guide for a new hire,"^[8] and the best onboarding guides are written by the people who have done the job.

They can tell when the output is wrong. An executive can tell whether an email sounds professional. Only the person who handles the account can tell whether it says the right thing.

They bear the consequences. When an agent in their domain fails, they are the ones who hear about it from the customer. Giving them ownership aligns the authority to change the agent with the cost of its mistakes.

Microsoft’s guidance for its agent identity platform reaches the same conclusion from a governance angle: once an agent is in use, its sponsors "typically come from teams using the agent."^[9] Team Topologies makes the parallel argument about human teams: a team aligned to a stream of work should own it end to end, without depending on others to make changes on its behalf.^[10] And Deming, again, argued that management should remove the barriers that rob workers of their pride of workmanship.^[6] Making someone the owner of the system that does their old work is one of the most direct ways to do that.

People often claim this ownership themselves if you let them. When our CEO once offered to "just knock this out now" using agents, one of our technical account managers replied simply: "then leave it to me." That is the healthiest exchange in this chapter: a leader offering help, and the person closest to the work claiming it.

This is why CorralData’s emerging model makes one person in each function responsible for the bots that share their function. It is not a rule about bots. It is a rule about who understands the work.

Honest work moves have three qualities: they are open, fair and firm. Open comes first, because without it the other two are invisible.

Being open means the move is announced before it happens, in a place everyone affected can see, and that the person currently doing the work hears it first, privately, from their manager. It means naming the role and the work, not the person, and saying why. "Because the agent is cheaper" is an honest reason, and better than a vague one. "Because the work will be more consistent and you’ll have time for the accounts that need judgment" is better, if it is true.

A simple work-move notice, a short post in a team channel, makes this routine.

The notice also protects the leader. It forces the questions that the overdue-emails episode skipped: who owns the output, who checks it, who cleans up. If you cannot fill in the table, the work is not ready to move.

Fairness in work moves comes down to a simple default: the person whose work is moving has first claim on the agent that takes it on. They sponsor it, write or approve its playbooks, check its output during the shadow period and decide when it is ready. If they don’t want that role, or are leaving, someone else takes it, but the offer comes to them first. And when a sponsor moves on, the sponsorship must move too; Microsoft’s guidance calls for sponsorship to be maintained "to ensure succession when an employee who’s a sponsor moves or leaves."^[9]

This default turns what feels like a loss into a promotion: from doing the task to owning the system that does it. It also keeps the knowledge in the organisation: the person who understands the work teaches it to the agent, rather than being quietly replaced by an agent that never learnt what they knew.

Being made to check an agent you did not choose, built by someone else, on work you used to own, is the worst version of the change; Bainbridge noted long ago that monitoring automation is itself demanding, draining work.^[5] Owning the agent is the best version. At CorralData that renegotiated deal is still more proposal than practice; Chapter 19 takes it up from the individual’s side.

Fairness also means credit. When an agent in someone’s domain does well, the sponsor should hear about it in public, just as they would for a person on their team. Andy Grove’s point that a manager’s output is the output of the organisation they run applies here: the sponsor’s output now includes the agent’s.^[11]

Openness and fairness are not the same as promising that nothing will change. Some work will move to agents and not come back. Some roles will shrink. Some organisations will hire fewer people than they otherwise would have. Pretending otherwise leaves people guessing, and guessing is worse than knowing.

Klarna’s reversal is sometimes read as proof that AI can’t really do the work.^[2] That is the wrong lesson. The company’s own first-month figures showed the agent could carry an enormous volume.^[1] The right lesson is that the decision about what moves should be made on quality as well as cost, and revisited when the evidence comes in. Firmness means making the decision clearly and holding to it until the evidence says otherwise, not drifting in response to whoever complains loudest.

Firmness applies upwards too. When work moves to a leader’s agent, that agent faces the same gates as anyone else’s: what-and-why briefs (Chapter 8), human approval at the irreversible points (Chapter 14), and the agreement of the person whose work it is. If anything, a senior person’s agent needs firmer gates, because colleagues find it harder to push back.

Being firm is also how you protect the people whose work is not moving. A clear statement that "these tasks are moving; these are not" removes the background anxiety that every job is quietly under review.

The hardest question in this chapter is what happens to careers, particularly at the start of them.

The Stanford findings on young workers matter here.^[4] Many of the tasks agents do best (first drafts, routine research, standard responses, simple checks) are the tasks that used to teach junior people how the work is done. Another of Bainbridge’s ironies applies: when the routine practice disappears, so does the skill it built, and the person is left responsible for the hard exceptions without the experience that would have prepared them.^[5] If juniors never do the routine work, how do they become the seniors who can tell when an agent is wrong?

There is no settled answer, but there are some promising paths, and they all run through the agents rather than around them.

Sponsoring small agents under supervision. A junior person can sponsor an agent for a narrow role, with a senior person reviewing. They learn the work by teaching it and checking it, and the senior reviewer can loosen supervision task by task as the junior’s maturity on each task grows, as Grove recommended for people.^[11]

Running Friction Reviews. The best post-mortem I saw at CorralData this year came from an implementation manager, not an engineer (Chapter 16). Analysing why an agent failed teaches a great deal about the work, and organisations will need more of the skill.

Writing and maintaining playbooks. The person who keeps a role’s Role Pack accurate (Chapter 12) becomes the organisation’s expert on how that work is done.

Agentic Resources. The function that manages the agent workforce (Chapter 17) is a new kind of job, drawing on HR, operations and domain knowledge. Commentators have been calling for such a function since at least 2024;^[12] Soofi Safavi’s list of its duties, from keeping a registry of agents to retiring stale ones, reads like a job description.^[13]

None of these paths is automatic. They have to be designed, offered and supported, in the same way a good company designs a graduate programme. Michael Gerber argued that a business should not depend on any one extraordinary person: the systems hold the standard, and people, well trained, run the systems.^[14] The same design discipline that lets agents fill roles can give people a path from one role to the next.

Work moving from people to agents is not a crisis to be managed or a secret to be kept. It is a change to the organisation’s design, and it can be done well or badly. Done badly, it happens by accident: someone with an agent takes over someone else’s work, and the person who did it finds out afterwards, cleans up and wonders whether they are next. Done well, it is a decision made in the open, owned by the person closest to the work, with a clear evidence bar, a named owner for the cleanup, and a new role for the person whose task has moved.

The leader’s job is to make it the second kind. That starts with you: with what you delegate, how you delegate it and to whom. But it also depends on the people on the receiving end making their own hardest shift, from doing the work to delegating it. Chapter 19 turns to them.