Students often start thinking seriously about AI careers in their final year, when the roadmap should really start much earlier. Here's a more realistic timeline.
First year — exposure
Use AI tools regularly for coursework, understand the landscape, and don't worry yet about specializing. Broad exposure now pays off later.
Second year — first projects
Build one or two small AI-adjacent projects, even simple ones. This is where you start discovering which parts of the field genuinely interest you.
Third year — specialization and depth
Pick a lane — technical building, product/strategy, or applied use in your primary field — and go deeper. Internships, if available, are highest-value here.
Final year — portfolio and positioning
Consolidate your strongest project into a clear portfolio piece, tailor your resume and LinkedIn to the specific roles you're targeting, and start networking intentionally.
The biggest mistake isn't starting late — it's treating this as a final-year sprint instead of a multi-year build.