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

Cloud.ru has described five types of boring but high-volume work that large companies already hand off to AI assistants. This isn't about "lazy" employees. It's about tasks where people spend their energy repeating steps rather than making decisions.

Five zones of routine

The article's main point is simple: some tasks only look trivial because people have done them on autopilot for years. Break a process down into micro-steps, and it turns out to be ideal work for an assistant that never tires of monotony, never gets irritated and never puts small things off until tomorrow. The author gives the example of an agent that schedules meetings: what looks like a single action to a manager becomes a chain of more than a hundred checks, approvals and exceptions for the system.

"This task doesn't need you to be you, it just needs to get done."

In large businesses, five categories of this routine were the first to be automated:

• answering repetitive support questions;
• digging through other people's code and running standard tests;
• searching contracts, NDAs and internal documents;
• labeling and prioritizing false positives in information security;
• generating templated advertising content.

Each item already has concrete case studies behind it. At T-Bank, an AI support agent works in the same interface as a live operator, while AFLT-Systems automated ticket registration and saw a 93% efficiency gain. Sber is developing an agent mode for GigaCode; Google, according to the article, generates about half of its new internal code with AI; and Yandex sped up legal work with "Neuro-lawyer." Even marketing has long moved beyond manual copywriting: VkusVill spends up to 7% of its operational advertising budget on AI-generated content.

How it gets rolled out

The approach is similar across all five scenarios. Companies don't start with an autonomous "super-agent." They pick a narrow slice of a process with little room for creativity and a lot of repetition. For support, that means a base of 30–50 frequent questions and careful RAG search instead of open-ended chat. For developers, it means safe scenarios like "explain this file" or "write unit tests for this function," with mandatory human review. For lawyers, it means a limited set of documents, cleaned of junk after PDF parsing, so the model cites specific clauses instead of inventing interpretations.

Pilot speed matters too: almost everywhere, the timeline is measured in days, not quarters. A basic FAQ bot can be stood up in 2–3 days, internal contract search in a day, a first-pass security layer for triaging SAST alerts in 2–4 days, and streaming generation of marketing drafts can be shown on day one if the data has been gathered in advance. In other words, the barrier to entry is dropping: a business first checks whether AI takes over the most tedious part of the work, and only then decides whether to build a more complex agentic system.

Where the weak spots are

Saving time doesn't change the fact that each of these setups breaks quickly on bad data and inflated expectations. If the FAQ base is outdated, the assistant will start lying confidently. If a coding assistant isn't confined to the repository, it may propose a plausible but broken patch. If a legal bot is allowed to draw conclusions without citations, the risk of errors involving money and obligations goes up. In security, the most dangerous idea is automatically closing findings as safe: the model can only suggest a priority, not replace the analyst's final decision.

On top of that come organizational risks:

• data leaks through logs, integrations and external services;
• shadow AI, when employees bypass clunky internal tools;
• team pushback driven by fear of layoffs;
• shifting responsibility onto the assistant instead of properly checking the output.

The author reminds readers that an AI assistant isn't a magic cost-saving button but a new layer of operational responsibility. It needs restricted access, clean data, verification through ordinary review processes, and a clear explanation to employees of where the model's help ends and human decision-making begins.

What it means

The article captures a real shift: businesses are no longer treating AI as a showcase for "smart answers" and are starting to use it as a tool to offload boring, repetitive work. The winners won't be the companies that talk loudest about agents, but those that break routine into steps fastest, give the model a narrow role and leave humans in control of the consequences.