GenAI is now table stakes in every tool. What that changes about learning one.

A year ago "with GenAI" on a course page was a selling point. Today it is the minimum, and the interesting question has moved.

Everyone is using it. That is the baseline.

Emeritus's India study reports that 96% of Indian professionals use AI or GenAI tools, and 93% spend time outside work building those skills ¹. Great Learning's report says 80% use GenAI to learn new skills, 60% use it often at work, and 69% feel AI is disrupting their role ². A newer Emeritus survey of Gen Z (15 to 28, so it includes students) found 82% use AI weekly to learn or build skills ⁴.

Vendor surveys, all of them, and worth the usual caution. But there is no survey pointing the other way. Using an AI assistant is no longer a differentiator any more than using a search engine is.

What employers are actually asking for

LinkedIn's Skills on the Rise 2026 for India names prompt engineering, workflow automation, LLMOps, AutoML and API integration among the fastest-growing skills ³. Read the list carefully: none of those is "knows ChatGPT". They are about putting an AI step inside a piece of work — a report, a pipeline, a campaign — and running it reliably.

That is the shift. The question is no longer "can you use AI?" but "can you build something with an AI step in it, and can you tell me what that step did and when it was wrong?"

The syllabus has already moved

You can see the change in course pages across the industry. Power BI courses aligned to Microsoft's PL-300 exam now list Copilot in the syllabus ⁵. Data analytics programs from national providers teach LLM APIs and agent frameworks alongside SQL and Python. DevOps programs describe themselves as "AI-native". Full-stack programs use an AI chatbot as the capstone.

We reviewed our own curricula against this in September 2026 and rewrote the parts that were behind: Copilot inside Power BI and Fabric, AI steps inside Alteryx and automation workflows, AI-assisted coding in the MERN program, AI in performance-marketing analysis, and a Generative AI module in every data program. The rule we set for ourselves: if a program claims AI, the AI work has to be in a module with a project, not in a brochure line.

How to learn a tool now

Three habits we teach from week one, whatever the tool.

Use the assistant, then check it. Generate the DAX measure, the SQL, the Python cell — then read it, run it on a small case where you know the answer, and fix what it got wrong. The checking is the skill. In a year, the generating will be free and the checking will be your job.

Put one AI step in every project. A classification step in a data-cleaning workflow. A summary of the week's numbers written by a model and reviewed by you. A chatbot over your project's documents. Small, bounded, explainable.

Keep a log of failures. The hallucinated column name. The join it invented. The confident wrong answer. Those notes become your interview answers to "how do you use AI at work?", which is now a standard question.

What to ask a course before you pay

• Which module is the AI work in, and what is the project? "AI-powered" in the title is not an answer.
• Do you teach checking, or only generating?
• Does the mentor use these tools at work? Someone who does can tell you where they fail.

If a course can't answer the first question in one sentence, the AI is on the brochure, not in the classroom.