What AI Skills Do Managers Actually Look for on a Resume?

Writing a resume that effectively represents your AI skills can feel overwhelming. If you’re like many of the job seekers I work with, you may believe you need to be an AI expert to secure an interview, let alone land a new role.

But the data suggests otherwise. In a survey of 1,000 U.S. hiring managers, 78 percent said AI tool proficiency was at least somewhat important, while only 19 percent called it very important, according to Resume Genius’ 2026 Hiring Insights Report. In the same survey, AI and machine learning ranked sixth on the list of the skills hiring managers prioritize, behind communication and collaboration, critical thinking, domain knowledge, project management and data analysis. Most hiring managers factor in AI, but few rank it above the skills they have screened for over decades.

Outside of AI engineering and similar technical fields, employers don’t tend to expect fluency. They just want candidates who can show that they use AI to be more effective in their actual jobs. You need to demonstrate that experience properly on your resume and in interviews to capture a recruiter’s attention.

I’ve coached more than 1,000 tech professionals and executives one-on-one. I also serve as a group coach for leaders in human resources and talent acquisition. This means I hear about what hiring managers screen for and which resume content generates interviews. What candidates think will impress a hiring manager and what actually does are often two different things.

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What AI Skills Do Employers Actually Want to See?

Much of the AI career advice currently circulating pushes workers to build fluency fast. I coach candidates to take a narrower approach and try for proficiency instead because the bar is lower and more specific than many people assume.

The Resume Genius survey also asked hiring managers how they prefer candidates to demonstrate AI skills. Describing AI’s impact on their work or a task they completed in an interview came first at 26 percent. Mentioning it briefly on a resume and reflecting it through work examples tied at 19 percent each. Certifications or courses came in at 15 percent. Notably, 21 percent said they don’t want candidates emphasizing AI skills at all.

Taken together, these numbers point to something specific. Employers respond to AI experience when it shows up in the context of your actual work. That tracks with the broader shift toward skills-first hiring, where employers weigh demonstrated capability more heavily than credentials.

How to Showcase Your AI Experience

In today’s job market, the tool names on your resume matter less than what you did with them. Hiring managers want a specific example of how AI supported a result in your work, including what changed after you applied it.

I recently worked with a product executive whose resume listed AI tools in parentheses at the end of nearly every bullet point. The tools sat there with no explanation of what the executive had done with them. I suspect the advice to include this information came from somewhere online and that the goal was to get past an applicant tracking system.

We decided to start over. Rather than tacking the tools onto the end of each bullet, we rebuilt each one around what the product looked like before this executive arrived and what it looked like after. The tools appeared in that story, though they were just the side dish. My client’s leadership skills and impact were the entree.

This subtle yet important change drove more interviews. Recruiters told them their AI transformation experience helped them show up in LinkedIn searches because it came through clearly on their resume. That story then carried through to their interviews.

Deciding what to include on your own resume is the tricky part. Below are examples modeled on what my clients have put on theirs:

• Deployed AI agents across X global engineers, reducing code review turnaround by X percent and freeing capacity for high-value work that drove $XM in incremental revenue.
• Overhauled customer onboarding workflow around internal LLM tool, slashing setup time from X weeks to X days and enabling team to onboard X percent more customers without additional staff.
• Gained executive buy-in to launch AI-assisted triage system across X-person support organization, redirecting X percent of tier-one tickets and shifting team capacity to complex escalations.

You’ll notice that none of these bullets name using a tool alone as the accomplishment. Naming the tool is table stakes at this point. The impact the tool allowed you to have is what generates interviews.

On LinkedIn, one or two lines in your About section and another bullet or two in your current role are usually enough. Redact any confidential information about your company or your clients. You also want to give recruiters a reason to reach out and request your resume, where you can share additional details.

Why AI-Written Resumes Work Against You

I strongly advise you to bring AI into your resume only at the eleventh hour during the writing process, if at all. Otherwise, you risk sounding like every other candidate applying for the role.

Hiring managers are seeing a flood of AI-written resumes, and they’re responding accordingly. In the Resume Genius survey, 76 percent said AI-written resumes make it harder to understand what a candidate actually did, and 72 percent said heavy reliance on AI makes candidates seem less skilled. 80 percent said they can tell at a glance when a resume was written by AI.

I’ve seen too many talented professionals sound AI-washed because they used AI on their resumes too early. AI doesn’t know what you don’t tell it. Exhaust your own thinking first, then bring it in as a proofreader. Your context and your results live in your head, and AI won’t think to ask about what it can’t see.

How to Back Up Your AI Experience in the Interview

Listing AI experience on your resume is just the beginning of the conversation. 60 percent of hiring managers in the survey said they also want to test, discuss or see proof of a candidate’s AI abilities rather than take their resume claims at face value. Some engineering teams have gone further, restructuring interviews around whether candidates can critique AI-generated work.

The job seekers I speak with often worry that they don’t have a long list of AI wins to share, but from what I hear on the hiring side, employers aren’t expecting you to have applied AI to every part of your role. What they want to know is whether you can speak to the experience you did list when they ask follow-up questions.

For each AI bullet on your resume, be ready to explain:

• What the work looked like before you brought in AI and what problem you were trying to solve
• Which tools or approaches you considered and why you chose the one you did
• Where the output was wrong or incomplete and how you caught it
• What you have learned along the way and how you approach AI differently today

That last point tends to do more work than the others. A leader who can name a limitation comes across as someone who has actually used the technology. Candidates who have only read about AI rarely have that kind of example ready.

How to Close the AI Gap If You’re Starting From Scratch 

If you’re just getting up to speed, you have options for building your AI proficiency that don’t require a lengthy program:

• Use AI on a real work project, ideally one where you can measure the status quo before and results after
• Volunteer for an AI pilot or working group at your current company
• Take a free or low-cost course to build your baseline familiarity

The recruiters and hiring managers I speak with want real-world use cases over theory. A course will get you comfortable with the terminology, but a real project gives you something concrete to put on your resume.

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Landing a Tech Job in the Age of AI

Every few years, the job market decides there’s a new baseline skill, and candidates quickly scramble to prove they have it. Not long ago, it was comfort with computers. More recently, it was data fluency. AI is the current one, and it definitely won’t be the last.

The candidates who navigate these shifts well can point to a real problem they solved and explain how they solved it. And that skill transfers to whatever comes next.

This is something you can learn, and you don’t need a technical background to learn it. Start with one project where you can point to what changed after you used AI, then practice telling that story out loud before your next interview.