AI has changed the hiring question.
It’s no longer enough to ask whether someone can write, analyze data, code, manage customers, or build a financial model. Increasingly, employers also need to know: Can this person use AI to do the job better?
That goes beyond knowing how to write a prompt. Strong AI skills involve knowing when to use the technology, how to give it the right context, how to evaluate what it produces, and when human judgment should take over. As we’ve seen with high-performing employees in the AI era, access to the same tools doesn’t mean people get the same results.
For hiring teams, that creates a new challenge. Traditional resumes and interview questions don’t always show whether a candidate has genuine AI literacy, can build AI into their workflow, or will simply accept whatever an AI tool gives them. Companies may need to rethink parts of their interview process and candidate evaluation to see how people solve problems when AI is available.
In this guide, we’ll look at what working effectively with AI actually means, which AI competencies matter across different roles, and practical ways to test them during hiring. The goal is simple: find people who can use AI as leverage while still bringing the judgment, expertise, and ownership the job requires.
Why “Can They Work With AI?” Is Becoming a Hiring Question
AI is becoming part of everyday work across marketing, finance, engineering, operations, customer support, and plenty of other functions. That means employers increasingly need to evaluate how candidates work with AI, not just whether they’ve tried a few tools.
A marketer might use AI to speed up research and campaign analysis. A financial analyst might use it to summarize datasets or investigate variances. A developer could use it for debugging and testing, while an executive assistant might use it to organize information, draft communications, and streamline recurring tasks.
The tools are different, but the hiring question is similar: Can this candidate use AI to produce better work while still applying their own expertise and judgment?
That matters because AI literacy can affect productivity, problem-solving, and how quickly someone adapts to new workflows. Employees who know how to delegate the right tasks to AI can spend more time on decisions, relationships, strategy, and other higher-value work.
It’s also changing what employers should prioritize when they evaluate candidates. A polished resume may show experience with certain platforms, but it says much less about whether someone can spot a weak AI answer, improve an automated workflow, or decide when a task needs deeper human involvement.
For many roles, working with AI is becoming another layer of job competency. Hiring teams now have to understand what that competency looks like before they can test for it effectively.
What Does Working Well With AI Actually Mean?
Working well with AI isn’t about knowing the most tools or memorizing clever prompts. It’s about using AI in a way that improves the quality, speed, or consistency of the work.
For hiring teams, that means looking for a mix of AI literacy, judgment, problem-solving, and ownership.
The strongest candidates usually know where AI creates leverage. They can identify repetitive work, speed up research, organize information, generate first drafts, analyze patterns, or automate parts of a workflow while keeping control over the final decision.
They also know when the output needs closer scrutiny. A confident-sounding answer can still contain weak assumptions, outdated information, or errors. That’s why critical thinking and domain expertise remain important alongside AI skills.
For employers, the goal shouldn’t be to hire the person who uses AI the most. It should be to hire someone who uses AI intentionally, evaluates its output, and turns it into useful work.
That distinction matters most when evaluating candidates for roles where AI is already changing how work gets done.
AI Skills Will Look Different Depending on the Role
There isn’t one universal definition of being “good with AI.” The skills that matter depend on what someone is actually being hired to do.
A software engineer and a financial analyst may both use AI every day, but they’ll use it for completely different problems. That’s why hiring teams should define role-specific AI competencies before adding “AI proficiency” to a job description or interview scorecard.
Here’s what that can look like:
The important part is connecting AI use to real responsibilities and outcomes.
For example, when you hire a financial analyst, you might care about whether they can use AI to investigate a variance and then validate the explanation against the underlying data. For a developer, you may care more about whether they can use an AI coding assistant while still reviewing the code for logic, security, and maintainability.
This also helps companies avoid vague requirements such as “must be proficient with AI.” A stronger requirement explains how AI fits into the role and what the employee should be able to accomplish with it.
Once you know what good AI use looks like for the position, you can build an interview process that actually tests for it.
How to Evaluate AI Skills During the Hiring Process
Once you’ve defined what good AI use looks like for the role, the next step is testing it during the hiring process.
A simple “Do you use AI?” won’t tell you much. Most candidates can say yes. The stronger signal is how they use it, what decisions they make along the way, and how they evaluate the final output.
Ask About How They Currently Use AI
Start with a real example.
Ask candidates to walk you through a task they’ve completed with AI, including what they were trying to accomplish, which parts they delegated, what they changed, and how they checked the result.
You’ll get much more insight from this than from asking which AI tools they know.
Give Them a Realistic Problem to Solve
For roles where AI will be part of the job, consider letting candidates use it during a skills assessment.
Give them a task that resembles something they would actually encounter after being hired. A marketer might analyze campaign performance. A developer could debug a piece of code. A finance candidate might investigate a discrepancy in a report.
Then evaluate the process alongside the finished work.
Did they frame the problem clearly? Did they question the AI output? Did they improve it using their own expertise?
Ask Them to Critique AI-Generated Work
Another useful approach is giving candidates an intentionally imperfect AI-generated output.
It could be a customer email, financial summary, sales proposal, piece of code, or marketing plan. Ask the candidate what they would change, what they would verify, and whether they would feel comfortable using it.
This can quickly reveal critical thinking, attention to detail, and domain knowledge.
Ask Them to Improve a Workflow With AI
You can also describe an inefficient process and ask how the candidate would improve it.
For example, imagine a team manually spends several hours each week compiling customer feedback from different sources. How would the candidate use AI or automation to speed up the process while keeping the output reliable?
The best responses usually go beyond naming a tool. They show workflow thinking: what should be automated, what still requires human review, and how the candidate would measure whether the new process actually works.
These exercises can fit into an existing interview process without adding several extra rounds. In many cases, one well-designed assessment can reveal more about a candidate’s AI skills than a long list of hypothetical questions.
Interview Questions That Reveal How Candidates Work With AI
You don’t need an entire interview dedicated to AI. A handful of well-chosen AI interview questions can reveal how a candidate thinks, experiments, verifies information, and applies technology to real work.
Here are some questions worth adding to your interview process:
The strongest answers are specific. Candidates should be able to explain the problem, how they used AI, what they contributed themselves, and what changed as a result.
Pay attention to how they talk about mistakes, too. Someone who has used AI meaningfully will usually have encountered outputs that were inaccurate, incomplete, or simply unsuitable for the task. Being able to explain how they caught and corrected those issues can tell you more about their AI competency than knowing the latest tools.
You can also tailor these questions to the position. When you hire remote employees, for example, ask candidates how they would use AI within the actual workflows they'll encounter in the role.
The goal isn't to hear the “right” AI terminology. You're looking for evidence that the candidate can think independently while using AI to improve their work.
Should Candidates Be Allowed to Use AI During Hiring Assessments?
In many cases, yes—especially if the employee will use AI once they’re on the job.
If a marketer, developer, analyst, or operations hire will regularly work with AI tools after joining your team, banning those tools during a hiring assessment can make the exercise less realistic. You may end up testing how someone works without the resources they’ll actually have.
A better approach is to design the assessment around how the candidate uses AI, not whether they use it.
Ask candidates to explain:
- Which parts of the task they used AI for
- Why they chose those parts
- What they changed or rejected
- How they verified the output
- What they would do differently with more time
This gives hiring managers a clearer view of the candidate’s decision-making, judgment, and problem-solving process.
It also shifts the focus away from the polished final answer. Two candidates could submit similar work while taking very different paths to get there. One may have carefully directed, reviewed, and improved the AI output. Another may have accepted the first response with minimal scrutiny.
The process is often where the real signal is.
There are still situations where you may want to test a skill without AI assistance. If a role requires someone to understand accounting fundamentals, write production-ready code, communicate clearly, or make high-stakes decisions, you’ll still want evidence that those foundational skills are there.
The most useful assessments often combine both: test the candidate’s core expertise, then see how effectively they can extend that expertise with AI.
This can also help companies keep their interview process focused. Instead of adding another interview round just to discuss AI, you can build AI use directly into a relevant work sample and evaluate both skills at once.
What Hiring Managers Should Look for Beyond AI Skills
Knowing how to use AI can make someone more productive, but it doesn’t replace the skills that make them effective in the first place.
In fact, as AI takes over more routine execution, human judgment becomes even more valuable. Hiring teams still need people who can understand context, make decisions, communicate clearly, and take responsibility for the outcome.
Some of the most important qualities to look for include:
- Domain expertise: They understand the work well enough to recognize when an AI-generated answer is weak or incomplete.
- Critical thinking: They question assumptions, compare options, and evaluate information before acting.
- Communication: They can explain decisions clearly to teammates, customers, and stakeholders.
- Curiosity: They experiment with new tools and look for better ways to get work done.
- Adaptability: They can adjust as tools, workflows, and expectations change.
- Accountability: They take ownership of the final result, regardless of how much AI contributed along the way.
- Business judgment: They understand what matters to the company and can prioritize accordingly.
This matters because AI can amplify whatever a person already brings to the role. Strong judgment paired with AI can create significant leverage. Weak judgment can simply produce mistakes faster.
That’s why companies should avoid treating AI skills as a shortcut around fundamentals. A candidate still needs the underlying expertise required for the job.
The better hiring question is whether someone has the right combination of role-specific skills, judgment, and AI fluency to perform at a higher level.
That same principle applies when hiring remote talent. Technical ability matters, but communication, ownership, adaptability, and the ability to work independently often determine how successful someone will be once they join the team.
Should AI Skills Be Required in Every Job Description?
Probably not.
AI is becoming more common across many roles, but that doesn’t mean every job description needs a generic “AI proficiency required” line. The better approach is to decide how important AI will actually be in the role.
You can think about it in three levels:
For example, if you’re hiring an AI engineer, AI expertise will obviously be a core requirement. For a finance, sales, marketing, or operations hire, the requirement may be more about using AI effectively within the role than having deep technical knowledge.
The wording matters, too.
“Must be proficient with AI” is vague. A stronger job description explains what the person is expected to do, such as using AI to analyze data, automate repetitive tasks, accelerate research, improve documentation, or support decision-making.
That makes the requirement easier for candidates to understand and easier for hiring teams to evaluate.
It also keeps employers from filtering out strong candidates who may not know a specific tool yet but already have the judgment, adaptability, and technical foundation to learn it quickly.
As companies rethink how they hire remote employees, they should treat AI skills like any other job requirement: specific to the role, tied to real work, and important enough to justify evaluating during the hiring process.
Hire for the Person Behind the AI With South
AI can make an employee stronger, faster, and more capable. The real advantage still comes from the person using it.
That’s why hiring teams should look beyond whether a candidate knows ChatGPT, Claude, Copilot, or the latest AI platform. What matters more is whether they can apply the technology to real problems, challenge weak outputs, make sound decisions, and own the final result.
As AI becomes part of more workflows, companies will need people who combine AI literacy with judgment, communication, adaptability, and role-specific expertise. Those candidates can turn new tools into better business outcomes.
If you’re looking for professionals who can bring that combination to your team, South helps U.S. companies find pre-vetted remote talent in Latin America across engineering, finance, marketing, operations, sales, customer support, and more.
Tell us what you need, and we’ll connect you with candidates who have the experience and skills to contribute from day one.
Schedule a call with South and start building a team that knows how to work with AI—and knows what good work looks like without it.
Frequently Asked Questions (FAQs)
What are AI skills in the workplace?
AI skills include using AI tools effectively, framing problems clearly, evaluating outputs, verifying information, improving workflows, and deciding when human judgment is needed. The exact skills depend on the role.
How can you tell if a candidate is good at using AI?
Ask for specific examples of how they’ve used AI at work, give them a realistic task, and pay attention to how they review and improve the output. The process often reveals more than the final answer.
Should candidates be allowed to use AI during interviews?
For roles where employees will regularly use AI on the job, allowing it during a work sample can create a more realistic assessment. Hiring managers can then evaluate how candidates use AI alongside their own expertise and judgment.
Should AI skills be listed in every job description?
Include AI skills when they’re relevant to the role's actual responsibilities. Instead of adding a vague “AI proficiency” requirement, explain how the employee is expected to use AI in their day-to-day work.
Are AI skills more important than experience?
They usually work together. AI can help employees work faster and handle more complex tasks, but domain knowledge and experience help them judge whether the output is actually useful or correct.
What AI skills should employers look for in 2026?
Useful skills include AI literacy, problem framing, critical thinking, verification, workflow automation, adaptability, and the ability to combine AI with role-specific expertise.
Can AI proficiency be taught after hiring?
In many roles, yes. Candidates with strong judgment, curiosity, technical confidence, and adaptability can often learn new AI tools quickly. That’s why employers should evaluate both current AI experience and the ability to learn as the technology changes.
