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AI Fluency: What Hiring Managers Actually Want to See
In this article:
🤖 AI Fluency in Hiring
💡 Real-World Example
🛠️ 5 Ways to Prepare
🎯 Beyond the Basics
✅ Your Interview Checklist
What Does “AI Fluency” Actually Mean for SE Hiring?
AI is showing up in SE job descriptions everywhere. What employers actually expect is often less clear.
That gap can make interviews tricky. How deeply should you answer “How do you use AI day to day?” What makes AI feel useful in a demo? And how do you demonstrate real skills beyond putting “AI-savvy” on your resume?
Join us Thursday, October 22, 2026, at 12 p.m. ET / 9 a.m. PT, live on LinkedIn, for a conversation with three SE leaders.
We’ll explore what AI fluency looks like on their teams, how it shapes interviews and hiring decisions, and how to discuss your experience with confidence and accuracy. You’ll leave with practical ideas for building and demonstrating your skills.
Bring your questions—we’re saving plenty of time for live Q&A.
How to show AI fluency in your next interview
There's an interview question that's quietly tripping up strong SEs and AEs.
It doesn't sound hard. It usually comes up halfway through, almost as an afterthought: "So, how are you using AI these days?"
Cue the answer we hear all the time in mock interviews: "Oh, I use ChatGPT constantly! Emails, call notes, account research…" (Totally reasonable. Also totally forgettable.)
That answer shows the panel you've tried AI, not that you've used it to drive outcomes. And with AI fluency now a hiring requirement almost everywhere, that difference matters more than ever.
Here's what a stronger answer looks like.
One client we coach, who handles both presales and post-sales work, was recently prepping for a technical SE interview. He had a strong AI story ready.
He'd built a Claude-powered workflow that creates customized demo environments in his company's product. Building one by hand used to take anywhere from 30 minutes to three hours. Now it takes less than a minute, and his colleagues are already using it.
In our prep session, his coach pushed on the details the way a technical interviewer might. Was it really working through the product's API, or just clicking through the browser like a bot?
He had the answer. It started out clicking through the product, learned the calls happening underneath, and now sends them directly. That switch is what made it fast.
His coach's advice: put that evolution into the story itself, and invite the panel to go deeper on the technical side if they want to.
He was also upfront about what he didn't have yet. The workflow was two weeks old, so beyond time saved per build, the hard numbers weren't in.
Why this matters now
AI fluency has moved from a nice-to-have to an expectation, fast. We now tell clients to plan on it: in every interview, someone is going to ask what you've done with AI.
Hiring leaders are saying the same thing. In TestGorilla's State of Hiring for AI Fluency 2026 report, which surveyed nearly 2,000 senior hiring leaders in the US and UK, 95% of organizations said AI fluency is a stated hiring requirement. And 53% of hiring managers said they'd take a candidate with high AI fluency over one with deep domain expertise.
That makes it a screen. A vague answer can put you behind a candidate with less experience who can clearly show how they use AI.
So "I use ChatGPT" isn't going to cut it.
SEs and AEs: if someone on the panel asked how you use AI, what would you show them?
Most of us have used ChatGPT, Claude, or another tool to research an account, summarize a call, or draft a follow-up email. And don't get me wrong, I'm all for anything that makes life easier. But it won't set you apart on its own.
Show how you've used it to improve the way you work.
There's a difference between using AI and driving outcomes with it.
Our consultant above is the SE version. He didn't just ask AI for a demo outline. He built a repeatable process around a task that was eating his week.
The same idea works on the AE side. Instead of asking for a quick company summary before each discovery call, you build a process that combines company research, industry trends, and buyer personas into a discovery brief you use before every call.
And no, you don't need to become a software engineer overnight. (We're not building the next ChatGPT here.) The goal is to spot something you spend way too much time on, figure out where AI helps, and build a process you'll actually use again and again.
5 ways to prepare
1. Pick one workflow, not five tools.
Think about a task you've spent more time on than you'd like, at work or in your personal life. That could be discovery prep, researching an unfamiliar industry, personalizing outreach, or even organizing a personal project.
One thoughtful example you can explain from beginning to end is worth more than a collection of tools you've tried once.
2. Start with the problem, not the technology.
Were you spending 45 minutes prepping for every discovery call? Struggling to turn long technical notes into a clear demo plan? Repeating the same account research across several opportunities?
Being able to explain why you built something matters as much as explaining what it does.
3. Walk through how it works, and expect follow-ups.
This is where you get to show how you think. Explain what information goes in, what AI helps you do, what the output looks like, and what you do with it next.
For example, an SE might explain:
"I built a repeatable workflow that takes discovery notes and organizes them into technical requirements, business priorities, and outstanding questions. From there, I use AI to generate an initial demo outline based on the customer's use cases, which I review and refine before presenting."
Then be ready for the next question. A panel may dig into how it actually works, especially in a technical interview, the same way our consultant's coach did.
Know your answer one level deeper than your summary.
4. Explain how you check its work.
Let's be real, AI has a funny way of sounding VERY confident even when it's completely making things up.
In a dry run for a hiring manager interview, our coach asked another SE how he'd used AI on his take-home, a data analysis assignment. His answer was specific. He cleaned the dataset himself first, trimming about 20,000 orders down to about 17,000. Then he used AI to draft the code for his visualizations, and because he knows Python, he reviewed that code to make sure it matched his own analysis.
That's the kind of detail that builds trust. Be ready to explain where you apply your own judgment, how you verify the output before it reaches a customer, and how you keep sensitive customer information out of tools that shouldn't have it.
5. Show what changed, and be honest about where it stands.
Connect the work back to impact. Did you reduce prep time? Make your discovery more consistent? Make it easier to tailor demos across industries?
If you have measurable results, use them. "Less than a minute instead of up to three hours" is a lot more memorable than "it saves time."
And if it's early, or you haven't used it in a real business environment yet, say so.
Be clear about what you've tested and what you've actually achieved. Initiative and clear thinking still come through.
Take it one step further
Once you've built something useful, think about who else it could help. Could another SE use it for technical discovery? Could an AE team adopt a similar process for account research?
You don't need to have rolled it out across a whole organization. Being able to see where your work could have broader impact is valuable on its own.
And if the interview format allows it, don't just tell the panel what you built.
Show them. Use sample or nonconfidential data, walk through the decisions you made, and show the output. If you're not sure there's room for a quick screen-share, ask your recruiter ahead of time.
Quick checklist
✅ One workflow, and the problem it solves in one sentence
✅ How it works, one level deeper than your summary, including how you check its output and protect customer data
✅ What changed, with numbers if you have them and honesty if you don't
✅ A sample version you can show if the format allows
AI fluency isn't about knowing the most tools or building the most complicated app. It's about spotting where the technology adds value and being able to explain how you used it.
Anyone can say they use AI. Showing how you used it to solve a real problem is a much more interesting conversation.
Go build something.
Want help turning your AI workflow into a strong interview story? Book a call with us →
Thanks for reading, stay tuned!
Yuji Higashi is the Co-Founder of Better Career, where he helps Sales and Solutions professionals build careers they’re excited about.
Previously, Yuji built and led global PreSales teams at Outreach as the company scaled from $2M to $120M ARR and at Apptio as it grew from $10M to $100M ARR and went public. He also co-founded PreSales Collective and PreSales Academy, helping build one of the world’s largest communities for PreSales professionals.
Throughout his career, Yuji has hired, interviewed, and coached thousands of professionals and developed a deep network of GTM leaders across the tech industry.
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