AI on the resume means nothing. Here's what does.
A comprehensive AI interview guide for interviewers and interviewees.
I wanted to dedicate a newsletter to AI and the hiring process. Whether you’re interviewing candidates or are being interviewed, how you ask about AI and talk about AI will quickly become vitally important to getting the right people in the right roles.
(At the bottom of this newsletter is a free AI interviewing toolkit. No forms or gimmicks to download; it’s just a direct link to the PDF. )
"What AI tools have you used?" is the most useless interview question in 2026.
I promise. Please don’t ask it.
I’ve been talking to a lot of business leaders lately about this topic:
“We need to hire people who actually know AI.”
Then I ask the obvious follow-up: “How are you evaluating that in interviews?”
There hasn’t been good answer to that yet and for good reason.
Most interview processes were designed for a different era. They were built to assess domain experience, communication skills, and cultural fit. Those things still matter. But they don’t tell you whether the person sitting across from you can actually use AI to move faster, serve customers better, or generate more pipeline.
And right now, that’s the capability that’s separating good hires from great ones.
The number of workers in roles where AI fluency is explicitly required has grown sevenfold in just two years from roughly 1 million in 2023 to around 7 million in 2025.
This isn’t a tech-sector trend anymore. It’s hitting revenue teams, marketing departments, and client-facing organizations hard.
So if you’re hiring for these functions in 2026, here’s how to actually evaluate AI capability and not just collect resumes that mention ChatGPT.
Glad you’re here and thank you for reading Approachable AI by Pat Schaber! In case you missed some recent topics, here’s a recap:
The Core Mindset Shift: Stop Asking About AI Tools
The fastest way to get a useless answer is to open with something like, “What AI tools have you used?” You’ll get a list. ChatGPT, Claude, Copilot, Perplexity, maybe a few automation platforms thrown in.
It tells you nothing.
Tools change every few months. The 2026 playbook is AI literacy with restraint.
The real question you’re trying to answer is this: Does this person know how to rethink work?
Not “do they use AI” but “do they instinctively look for where AI can remove friction, improve quality, or scale output?”
That mindset and not the tool stack is what separates a real AI practitioner from someone who read a few LinkedIn posts and added “AI-proficient” to their resume.
What to Look For: The Three Markers of Real AI Fluency
Before you get into questions, know what you’re evaluating. I look for three things.
Workflow thinking, not task thinking. Strong candidates see in systems. They notice repetitive steps, slow handoffs, and manual research and they immediately start asking whether AI can help.
Problem-first orientation. The best AI users don’t start with a tool. They start with a problem. “I needed to cut prospect research time in half” comes before “so I built a workflow using...” That sequencing tells you a lot about how someone actually operates.
Intellectual honesty about limitations. This is the one most leaders overlook. Skills in AI-exposed jobs are changing 66% faster than in traditional roles, meaning continuous learning and adaptability matter more than static knowledge. The candidates who understand where AI falls short are far more valuable than the ones who think AI can do everything.
The AI Interview Questions That Actually Work
Here’s where most hiring processes need a significant upgrade. Move away from hypothetical questions and toward behavioral and tactical ones.
The Workflow Walkthrough
“Walk me through a specific project where you used AI. What did the workflow look like before? What did you change? How did you know it was working?”
A weak answer sounds like: “I use ChatGPT to write emails and it saves me time.” Surface level. No specifics, no validation, no business outcome.
A strong answer sounds like: “I built a workflow where I pull the prospect’s recent earnings call, run it through a prompt I developed, and get a summary of their top three stated priorities before every discovery call. It cut my prep time by about 45 minutes per call and my first-call conversion went up because I was asking smarter questions.”
The difference isn’t the tool but rather the thinking behind the tool.
The Failure Test
“Tell me about a time AI gave you a bad result or led you in the wrong direction. How did you catch it? What did it teach you?”
This question is gold. Real AI users have good failure stories. They’ve run into hallucinations, outdated data, tone that was off, outputs that needed significant human editing before they were usable.
If a candidate can’t think of a single example where AI didn’t work the way they expected, they’re either not using it deeply or they’re not paying attention to the output.
The Opportunity Question
“What’s a task in your current or last role that you think should be AI-assisted but isn’t yet?”
People who actually use AI have a running list of ideas. They’re constantly noticing friction and mentally filing it as a future experiment. If someone goes quiet here or gives you a generic answer, that’s a signal.
The Role-Specific Scenario
Give them a real problem from the job they’re interviewing for and ask them to walk you through how they’d use AI to approach it.
For sales: “You have a discovery call tomorrow with a prospect in the commercial real estate industry. Show me how you’d use AI to prepare.”
For marketing: “We need to repurpose this blog post into three LinkedIn posts and an email. Walk me through your process.”
For client services: “A customer sends a complex support request that touches three different teams. How could AI help your team respond faster without making the interaction feel robotic?”
You’re not looking for the “right” tool. You’re looking for structured thinking. Do they break the problem down, identify where AI adds value, and think about quality control on the back end?
The Red Flags That Are Easy to Miss
Tool listing without outcomes. If the conversation stays at the level of “I’ve used X, Y, and Z tools” without connecting those tools to actual results, that’s a red flag. Tools are means, not ends.
Polished language, no texture. HR teams are admitting they’re having trouble telling who to hire because candidates keep using the same AI-assisted talking points. The tell is usually a lack of specific, messy, real-world detail. Real AI practitioners have specific stories including the ones that didn’t go perfectly. If everything sounds like a highlight reel, probe deeper.
No mention of human judgment. Someone who talks about AI like it’s a magic box where you put in a prompt and trust whatever comes out is going to create problems in a client-facing role. The best practitioners treat AI output as a starting point, not a conclusion. They edit, verify, and apply their own expertise before anything goes to a customer or a prospect.
Privacy blind spots. If a candidate casually mentions feeding sensitive client data into a public AI tool without any mention of data handling or security that’s a red flag.
The AI Skills That Actually Matter for Revenue Teams
Let me get specific about what you should be prioritizing in sales, marketing, and client services.
AI-assisted research. The ability to quickly understand an industry, a prospect, a customer’s business model, or a competitive landscape. This is table stakes for anyone in a client-facing role in 2026.
Workflow design. Not just using AI for one-off tasks, but thinking about how to build repeatable processes. A marketer who can set up a consistent content production workflow. A salesperson who has a systematic approach to account research. A client services rep who has a system for drafting responses to complex inquiries.
Prompting and iteration. This is more nuanced than “prompt engineering.” It’s the ability to work with AI the way you’d work with a smart but junior collaborator. Giving context, reviewing output critically, pushing back when something’s off, and refining until the result is actually useful.
Output validation. Knowing when to trust the output and when to fact-check it. This requires both domain knowledge and an honest understanding of where AI models tend to go wrong.
Critical judgment. Probably the most important skill of all. Knowing when AI helps and when it doesn’t. When a customer interaction needs a human touch. When a data point needs verification. When the AI-generated draft is 80% there and needs real editing versus when it’s off-base and needs to be started over.
The Bottom Line
AI expertise isn’t about knowing the newest tools. It’s about knowing how to rethink work and being honest about what AI can and can’t do.
The candidates worth hiring in 2026 are the ones who ask “could AI help us do this better?” as a habit, not a performance. They have failure stories alongside success stories. They think in workflows, not one-off tasks. And they know that AI output is always a starting point, never a final answer.
If you can identify that mindset in your next interview, you’ll build a team that doesn’t just keep up with how AI is changing your industry. They’ll help you stay ahead of it.
Ways to Work with Me
Quick Win Session
A focused, high-impact working session designed to identify one immediate AI opportunity and give you a practical roadmap you can implement right away.
AI Readiness Assessment
A structured evaluation of your people, processes, data, and tools to determine where AI will create the most value and what needs to happen before you scale.
Workshops & Projects
Hands-on, team-based engagements that turn AI from theory into execution through tailored workshops, use-case design, and guided implementation.
Fractional AI & Marketing Leadership
Ongoing executive-level support that embeds AI strategy, operational rigor, and revenue-focused marketing leadership directly into your business.
AI Tech I’m Currently Using
I get quite a few questions on Substack, LinkedIn, and through my website on what AI tools I use for what use cases. I’ll try to share a few in each newsletter so it will give you some ideas of tools you can try for specific purposes. Here are a few for this week:
Everyday LLM - ChatGPT and Claude: I cover a lot of this above so won’t dive in too far here.
Call and Meeting Transcriptions - Granola: I like Granola for a couple of reasons. It takes the transcription and rolls it up into very good notes and action items but doesn’t need to be added to a meeting as an attendee. Also, it syncs great with my Hubspot CRM which makes it very easy to send notes to the contact record.
Presentations / Branded Documents - Gamma.app: I use this 3-4 times a week for anything I do with client presentations or professional document creation. I cover why I’m a heavy Gamma user in this post:
Video Recording / Editing - Descript: I’m trying to work in time to my weekly routine for video creation. I feel it’s important to give people a more personal connection. Descript is a huge timesaver. Imagine being able to erase or add text from a script and have it be reflected in the actual video in seconds. Unbelievable.
Sales Prospecting - Apollo.io and Clay: I don’t do a ton of outbound prospecting for my business but I’ve been using the free versions of Apollo.io and Clay to find companies that I could get to know through LinkedIn that may be interested in my services. I’m only touching on a sliver of the capabilities of these tools.