Cloud.ru named five boring tasks that Big Tech is already handing over to AI assistants at scale

Cloud.ru described five types of tedious but widespread work that large companies are already delegating to AI assistants. This is not about employee "laziness," but about tasks where humans expend energy on repetition rather than decision-making.

Five zones of routine

The article's main idea is straightforward: some tasks seem elementary only because people have long been doing them on autopilot. When a process is broken down into micro-steps, it becomes clear that this is ideal work for an assistant that doesn't tire from monotony, doesn't get irritated, and doesn't postpone minor details until tomorrow. The author gives an example of an agent for scheduling meetings: what looks like a single action to a manager becomes a chain of more than a hundred checks, approvals, and exceptions for a system.

"This task doesn't require you to be you; it just needs to be done."

In big business, five categories of such routine were the first to be automated:

• answering recurring support questions;
• reviewing other people's code and running standard tests;
• searching through contracts, NDAs, and internal documents;
• marking and prioritizing false positives in information security;
• generating uniform advertising content.

Each point already has concrete use cases behind it. T-Bank's AI support agent works in the same interface as a live operator, and AFLT-Systems automated ticket registration and achieved a 93% efficiency increase. Sber is developing GigaCode's agent mode, Google generates around half of its new internal code with AI according to the article, and Yandex accelerated legal work with the help of "NeuroJurist." Even marketing has long gone beyond manual copywriting: VkusVilla allocates up to 7% of its operational advertising budget to AI-generated content.

How they launch it

In all five scenarios, the approach is similar. Companies don't start with an autonomous "super-agent," but take a narrow segment of a process where there's little room for creativity and plenty of repetition. For support, that's a database of 30–50 frequent questions and careful RAG-based search instead of free-form chat. For developers—safe scenarios like "explain this file" or "write unit tests for this function" with mandatory human review. For legal teams—a limited set of documents, cleaned of junk after PDF parsing, so the model references specific clauses rather than inventing interpretations.

Separately important is pilot speed: almost everywhere, the timeframe is measured in days, not quarters. A basic FAQ bot can be set up in 2–3 days, internal document search in a day, an initial security layer to sort SAST alerts in 2–4 days, and batch marketing draft generation can be demonstrated on day one if data is prepared in advance. In other words, the barrier to entry drops: first, the business checks whether AI removes the most tedious part of the work, and only then decides whether it's worth building a more complex agent system.

Where the weak points are

Time savings don't negate the fact that each of these systems quickly breaks down on poor data and inflated expectations. If the FAQ base is outdated, the assistant will confidently lie. If a code assistant isn't confined to repository boundaries, it might suggest a plausible but non-functional patch. If a legal bot is allowed to draw conclusions without citations, the risk of errors involving money and obligations grows. In security, the most dangerous idea is automatically closing findings as safe: the model can only suggest a priority, but can't replace the analyst's final decision.

On top of that, organizational risks emerge:

• data leaks through logs, integrations, and external services;
• shadow AI, when employees bypass inconvenient internal tools;
• team resistance due to fear of layoffs;
• shifting responsibility to the assistant instead of proper result control.

The author reminds that an AI assistant is not a magic economizing button, but a new layer of operational responsibility. It needs to be restricted by access rights, fed clean data, checked through regular review processes, and employees need to understand where the model's help ends and human decision-making begins.

What it means

The article well captures a shift: business stops viewing AI as a showcase for "smart answers" and starts using it as a tool to remove tedious, repetitive burden. The winners won't be the companies talking loudest about agents, but those who quickly break down routine into steps, give the model a narrow role, and keep human control over outcomes.