# AI agents and the future of work: what already changes in operations > AI agents and the future of work: which roles are shifting today, what capabilities to build, and what nobody can predict yet. - Canonical: https://arkatai.com/en/ai-agents-future-of-work/ - Site: Arkatai (https://arkatai.com) — agentic operations as a service - Language: en - Published: 2026-07-19 --- The future of work with AI agents won't be settled by ten-year predictions. It's being settled right now, in the operations already running on agents. What I see isn't a workforce that vanishes overnight. It's a redistribution of the work itself: repetitive execution loses weight, while judgment, supervision and process design gain it. This article describes that shift using what's on the table today, not forecasting, and it marks clearly where the things we don't yet know begin. I write from the practice of operating agent systems, not from speculation. When an executive asks me whether this "will destroy jobs," my answer is that the question is framed wrong: what changes first isn't the headcount, it's the composition of the work inside each role. ## Which work moves first Agents enter through processes with volume, rules and recurring exceptions: reconciling invoices, tracking orders, resolving patterned support cases, feeding systems with data pulled from documents. This is work people do today, but it holds little expert judgment and a lot of execution within a known lane. When an agent runs that lane, the part of the role that was "doing the step" shrinks. What's left for the person is what the agent doesn't cover: the rare cases, the decision when two rules collide, the conversation with the angry customer, the check on a doubtful result. A role that was 80% execution and 20% judgment doesn't disappear. It turns into a role where that 20% becomes the center and grows. I've described the concrete terrain of these processes in the [pillar article on AI agents for business](/en/ai-agents-for-business/). What matters here is the consequence for people: pure execution work isn't the most at risk because it's low-skilled, it's the most at risk because it's the most codifiable, which is exactly why an agent takes it on first. ## The roles that gain value When the cost of execution drops, the relative value of three things rises: deciding, verifying and owning the result. **Deciding.** Someone has to set the agent's limits: what it can do alone, what it escalates, what's off the table. The model doesn't make that call. A person who understands the business and the risk does. It's judgment work, not keyboard work. **Verifying.** An agent can repeat the same mistake at scale. A kind of work that barely existed before appears: reviewing samples of what the system does, catching when it drifts, deciding whether to loosen or tighten the controls. It looks more like supervising an operation than running it. **Owning.** When software runs part of the work, responsibility doesn't dissolve. It concentrates. Someone has to be able to explain why the system did what it did and answer to a customer, an auditor or a board. That demands understanding the process in depth, not just using a tool. This shift in weight already shows in product development, where the split between architecture, building and review has moved. I've written about it in [agentic product development](/en/agentic-product-development/). The same logic is reaching administrative operations. ## The automated-process owner emerges The clearest new role I see is the person accountable for a process that agents now run. It isn't whoever builds the system or maintains it technically. It's whoever answers for that process doing its job: they set the business rules, decide where the line between agent and person sits, review the results and approve changes when the business changes. It's a business role, not a systems one. In many companies that role doesn't yet exist by name, because processes lived split across several people and none owned the whole. When an agent runs the process end to end, that ambiguity stops being sustainable: you need an owner. How ownership splits across process, result, system and data, and how the org chart gets redrawn around this, is what I develop in [the AI operating model](/en/ai-operating-model/). ## What capabilities to build If you manage people and wonder where to invest in training, here's my practical read. | Capability | Why it gains value | |---|---| | Making a process explicit | An agent only runs what's defined; whoever can turn tacit process into written rules becomes central | | Reading results and spotting drift | System supervision replaces manual execution; it needs basic statistical judgment and business knowledge | | Deciding under ambiguity | The cases that reach a person are, by definition, the ones the agent couldn't resolve: the hard ones | | Communicating and negotiating | The part of the work that deals with people (customers, suppliers, teams) is the least codifiable | None of these is a technical skill in building agents. They're capabilities of judgment and process. Building and maintaining the system is a separate job, and for most mid-sized companies it isn't even worth bringing in-house. I cover that in [prepare your company for agents](/en/prepare-your-company-for-agents/). ## What we still don't know Here I hold myself to the discipline I ask of anyone speaking on this topic: separate what I observe from what I imagine. I don't know, and no one knows with serious data, how fast this will move in each sector. I don't know whether the net job balance will be positive or negative in a given company: it depends on management decisions, not on a law of technology. I don't know which new roles will appear that have no name today, just as twenty years ago there was no "head of data" job. And I distrust anyone who hands you a percentage of affected jobs with two decimals and a date: that number is either invented or badly measured. What I do state, because I see it: within each affected role, the mix shifts from execution toward judgment, supervision and responsibility. That movement is already happening where agents genuinely operate. The rest is scenario, not observation, and it's worth treating as such. To place this change inside the full redesign of the company, the frame is in [the AI-first organization](/en/ai-first-organization/), and to gauge where yours stands, in [how to measure AI maturity](/en/ai-maturity-assessment/). ## Frequently Asked Questions ### Will AI agents destroy office jobs? They change the composition of the work before the number of roles. Pure execution roles lose weight and roles of judgment, supervision and responsibility gain it. What each company does with that balance is a management decision, not an automatic consequence, and I don't buy the tidy percentages going around. ### What skills should my team build? The ones an agent doesn't cover: making a process explicit, supervising results and spotting drift, deciding on ambiguous cases, and communicating with customers and suppliers. These are capabilities of judgment and process, not of programming agents. That technical part is a separate job rarely worth internalizing. ### What is an automated-process owner? It's the person accountable for a process run by agents doing its job: they set the business rules, decide the line between agent and person, review results and approve changes. It's a business role, not a systems one, and in many companies it doesn't yet exist by name. ### When will this change reach my sector? I don't know precisely, and I distrust anyone who states it with a date. The speed depends on the type of processes, the quality of the data and management decisions. What I do observe is that the change always starts with high-volume, rule-based processes, not with the work that carries more judgment.