The AI expert you’re looking for might not exist. At least, not with the experience your job description requires.
New AI models and capabilities are advancing faster than professionals can accumulate the experience needed to master them. Yet, US jobs requiring AI literacy have grown 70% year over year. And across industries, hiring managers face the same challenge: how do you evaluate expertise in tools that have only just emerged and change every day?
Whether you’re hiring for a creative, marketing, or tech team, finding AI-skilled talent takes more than identifying buzzwords on a resume. It requires a closer look at a candidate’s judgment, their underlying workflow, and ability to adapt. These hiring tips will help you set realistic requirements, evaluate professionals effectively, and bring the right expertise to your company.
- Define the Work Before the Role
- Make Sure You Can Evaluate the Expertise
- Search Beyond AI Titles and Job Boards
- Evaluate Judgment and Creativity, Not Just AI Proficiency
- Look for a Track Record of Learning
- Ask Candidates to Walk Through Their Work
- Assign Paid Assessments as Needed
- Consider Evidence, Not Just Confidence
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1. Define the Work Before the Role
Where does your team need help? Perhaps campaigns take too long to produce, research gets repeated across projects, or new tools are being adopted without a consistent process. Define the problem before deciding which professional can solve it.
An AI-skilled designer might use image-generation tools to explore concepts. A marketing operations specialist might connect tools to improve reporting. An agent developer might build systems that research, analyze, or take action across platforms. Some professionals can do all of the above, but the requirements of every role are different.
If you expect a hire to build or direct agents, say so. The same goes for redesigning workflows or training colleagues. Clear responsibilities will help candidates determine whether the role fits and give your hiring team a standard for evaluating them.
2. Make Sure You Can Evaluate the Expertise
Can your interviewers tell the difference between someone who uses an AI tool and someone who can make it useful to your business? Before speaking with candidates, identify who can evaluate the work you’re asking them to do.
That person might be a creative director who has incorporated AI into production, a technical lead familiar with agent development, or a specialist outside your organization. They should be able to recognize strong work, ask meaningful follow-up questions, and identify gaps in a candidate’s approach.
A recruiting partner with relevant expertise can also help define requirements and evaluate talent. Whoever takes that role, involve them early enough to shape the search, rather than bringing them in only after finalists have been selected.
3. Search Beyond AI Titles and Job Boards
The professional you need may not call themselves an “AI expert.” They might be a designer, strategist, writer, or marketing operations specialist who has incorporated AI into their work. Search for the discipline and the problems you need solved, then investigate their AI experience.
Beyond job boards, explore professional networks, referrals, creative communities, and online portfolios. Events such as Onward Search’s AI Portfolio Shows can introduce you to creative professionals and the processes behind their projects. For technical roles, public repositories and AI communities may offer another place to start.
Consider people already on your team, too. Someone who has improved a recurring process may be ready for a broader role. And if they’re not, lean on specialized AI enablement and staffing partners for support as needed. A contractor, consultant, or service provider who can share their practices may also help develop your existing staff.
4. Evaluate Judgment and Creativity, Not Just AI Proficiency
Knowing how to generate an image doesn’t tell you whether a candidate can recognize a strong concept. A creative professional still needs to understand the audience, the brand, and the purpose behind the work.
Look for the expertise the role has always required. A designer should understand composition and visual hierarchy. A copywriter should know how to shape a message. A marketing leader should be able to connect creative choices to business objectives.
Then examine how they apply that expertise alongside AI. What did they reject or refine? Where did they take over manually? Their answers can reveal how they protect quality, solve problems, and decide when a tool has taken the work as far as it can.
5. Look for a Track Record of Learning
The tools on your list today may not be the ones your team relies on next year. That doesn’t mean platform experience is irrelevant. It means you also need people who can learn new capabilities and adjust their approach.
Ask candidates about something they taught themselves, a tool they adopted, or a process they changed after the first attempt failed. What prompted the change? How did they decide the new approach was worth keeping? These examples reveal more than a general claim that they enjoy experimenting.
PwC’s 2026 research finds that skills requirements are changing more than twice as fast in the most AI-exposed jobs. A professional who can evaluate new tools and share what they learn can help your team keep pace.
6. Ask Candidates to Walk Through Their Work
A portfolio often shows the finished product. A conversation can reveal how it came together. Invite candidates to walk through a relevant project from the initial brief to the final result.
What did they personally contribute? Why did they choose those tools? What went wrong, and what did they change? A designer might describe correcting inconsistent product details across campaign images. A marketer might explain how they caught inaccurate information in AI-assisted research.
For workflow and agent roles, go further. Could a colleague run the process? What happens when a connected tool fails? Discuss how the system was tested, which actions require approval, and how the candidate knows it works beyond a single demonstration.
7. Assign Paid Assessments as Needed
A practical exercise can help answer an important question that a portfolio and interview leave unresolved. It should reflect the position and provide enough information to evaluate a skill the person will need on the job.
For example, a copywriter might critique a flawed AI-generated draft. An art director could evaluate concepts against a brief. An agent builder might diagnose a workflow failure. HR best practices recommend balancing role-relevant tasks with a candidate-friendly experience and a speedy selection process.
Tell candidates what you’re assessing, how long the exercise should take, which tools they can use, and what accommodations are available. Pay for substantial take-home work. And know that if you already have the evidence needed to make a decision, another assignment may do more to delay the hire than improve it.
8. Consider Evidence, Not Just Confidence
A candidate may speak confidently about AI and name every new platform. That can make for an engaging interview, but it doesn’t tell you what they’ve accomplished. Look for examples that connect their decisions to the work and its results.
If someone says AI reduced production time, ask what the comparison included. Did they account for review and revisions? If they claim a campaign performed better, what was the baseline, and what else changed? Not every creative project has a clean revenue metric, but candidates should be able to explain how they judged success.
Evidence can take different forms. Personal projects and freelance work may reveal an early-career candidate’s ability. For senior professionals, explore strategic decisions, team leadership, and recent work. Allow redacted or recreated examples when confidentiality prevents someone from sharing client materials.
Final Thoughts
Hiring AI-skilled talent requires a clear understanding of the position and credible evidence that someone can perform it. Tool familiarity has value, but professional judgment determines whether the work meets your standards.
Ensure your process matches the role. A portfolio discussion may reveal what you need to know about one candidate, while another position warrants technical evaluation. Apply consistent criteria and make room for professionals whose experience follows a less conventional path.
Your next hire won’t arrive with years of experience in every new capability. But they should arrive with strong fundamentals, demonstrated AI skills, and the ability to keep learning. Look for professionals who can explain how they’ve adapted and help your team do the same, and together, you can win in this increasingly AI-powered world.
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