Which AI tools stay useful once the hype dies down

A new AI tool that "changes everything" launches almost every week. The right question is never "is it impressive in a demo?" but "will I still be using it in six months, on my actual work?"

For writing and document summarising, conversational models (GPT, Claude, Gemini-type) hold up because the task is stable: give them a text, an output format and an audience, and the result is directly usable. The trap is not the tool, it is a vague instruction — "summarise this report" always produces a generic answer, whichever model you use.

For data analysis, AI excels at exploring and suggesting leads, but human verification remains unavoidable: a model that invents a plausible-looking number is a bigger risk than simply lacking time. The tool saves time on the first draft, not on the checking.

For code, editor-integrated assistants (autocomplete, coding agents) are now reliable enough for repetitive code or supervised refactoring. They lose their value the moment they are handed an architecture decision with no human review: the initial speed gain turns into technical debt that has to be paid later.

For generated image and video, the value depends entirely on brand consistency over time — a single visual impresses, an inconsistent series over three months damages a visual identity. It is as much an art-direction job as a tool.

The common thread: an AI tool lasts when it replaces a repetitive, verifiable task — never when it replaces judgment. That distinction, applied tool by tool and task by task, is what structures each applied-AI track in the CoursTech catalogue — not a list of trendy tools, but a method to evaluate the next one yourself when it launches.