Harnessing AI for your role — not generic prompts
The market is flooded with AI applications. What employers want is someone who can use AI inside a real role — and still ship work they own.
One of the biggest gaps we see in the graduate brain map is structural: AI has changed what “ready for work” means. Recruiters are drowning in generic AI-generated applications. The degree is still the entry ticket — but role-specific AI harness is becoming baseline employability.
Generic chat is not a skill
Knowing how to open a chat window is not what hiring managers are testing for. They want to see whether you can:
- use AI to speed up your craft — marketing, engineering, analysis, design, sales, data;
- review output against acceptance, brand, security, or business rules;
- ship an artifact you can defend in review and show in a pack.
That’s harness — not hype.
How we teach it on a squad
Every pathway runs the same lifecycle. AI shows up in Instrument (tooling and intents for your role) and Build (draft with assist). Then Review runs to a rubric — peer and mentor, not the model grading itself.
You own the diff, the claims, the metric definitions, the design decisions. AI drafts; you ship.
Pathway by pathway
Marketing learns AI for variants and research summaries — with honest-claims review before publish. Engineering pairs on code but owns architecture and security. Business analysts draft maps and edge cases — but own stakeholder truth. Product owners use AI on backlog breakdown — but own prioritisation and the ship call. And so on.
Explore each pathway interactively on the pathways page — click a role to see what you’d do and how AI fits.
Ready to practice it on a real build? Claim a seat.