Technical Context
I don't see a sensation here. I see a familiar picture: if a process is narrow, repetitive, and well-formalized, AI automation can easily take over a piece of work that used to require several people.
I've seen this kind of thing in production, not in theory. More often than not, it's not "magic AI" but a generator or a combination of an LLM, templates, rules, validation, queues, and proper logging. This setup can handle the very volume that would have otherwise required hiring five more people.
The key point is not the model, but the architecture. If the inputs are messy, the rules are inconsistent, and no one checks the results, the automation falls apart within a week. But if the task has a clear format, quality metrics, and human oversight at the edges, artificial intelligence integration starts to pay off very quickly.
To put it bluntly: it's not "people in general" who are being replaced, but batches of micro-tasks. Generating product cards, initial responses, classifying incoming traffic, preparing drafts, normalizing data, collecting content variations, and routing requests. This is where the effect that looks like a whole team being replaced comes from.
And yes, there's an important time-related caveat here. The idea itself isn't new. By 2024-2025, there were plenty of cases where AI reduced the manual workload, but by 2026, it's no longer a forecast but a working practice for those who know how to build AI architecture, not just buy model subscriptions.
Impact on Business and Automation
The winners are teams with a high volume of repetitive tasks. Content operations, lead generation, first-line support, catalog processing, and internal sales assistants. There, speed increases immediately, and the cost per unit of work drops significantly.
The losers are those who confuse AI solution development with "let's connect a chatbot and replace everyone." Without proper constraints, fact-checking, and escalation routes, such a project quickly becomes an expensive imitation of automation.
I also wouldn't budget for "minus 6 headcount" from the first month. In practice, the routine work goes first, then roles change, and only then does it become clear who you really don't need to hire. This is a much healthier approach than cutting the team based on a vendor's presentation.
If you're already accumulating tasks that employees are doing manually based on a template, this is a good time to reconsider the process. At Nahornyi AI Lab, we work with these exact bottlenecks: we can build an AI solution for business that eliminates routine work, rather than creating new headaches.