The one thing university career centers should get right is keeping a human in charge of judgment while using generative AI only for first-draft support. AI can speed up resume bullets, mock interview questions, outreach email drafts, and internship search summaries, but it should not be the final voice that shapes a student’s decisions, especially when the advice affects confidence, job targeting, or how they present themselves to employers.
From my experience building and operating SaaS products and working hands-on with AI content tools, the biggest failure point is not usually the model itself. It is the workflow around it. Teams get into trouble when they treat AI output as finished instead of assisted. In practice, AI is very good at producing something plausible, but plausible is not the same as accurate, appropriate, or personal. In a university setting, that matters because generic advice can flatten a student’s real strengths or introduce language they would never actually use in an interview.
A practical lesson from product and content workflows is to make review steps explicit. For example, if a student uses AI to draft a cover letter, the system should require a human checkpoint: first, the student confirms the experiences and claims are true; second, an advisor helps refine tone, fit, and authenticity; third, the student leaves with a short note explaining what was AI-assisted. That process protects against fabricated achievements, over-polished language, and overreliance.
The ethical side is just as important as the efficiency side. Career centers should tell students what the tool is for, what it is not for, and what data should never be entered into it. Transparency, consent, and review are what make AI useful without making advising less human.
If career centers get that governance layer right, AI becomes a productivity tool that expands advisor capacity instead of replacing the trust students actually need.