Introduction

AI skills are now a must-have for GTM roles. Employers want candidates who not only use AI but also rethink work with it.

What's the problem it solves?

Many job seekers don’t know how to show their AI fluency, while companies struggle to tell apart real AI talent from those who just dabble. This article explains how hiring managers can evaluate AI-native candidates and how applicants can prepare to stand out.

Quick Summary

AI has gone from “nice-to-have” to “required” in just two years. In 2023, fewer than 100 GTM job posts asked for AI skills. By mid-2025, that number was nearly 1,000. Roles now range from growth engineers to content marketers, all needing some level of AI fluency. But fluency looks different for each job.

To address this, Kyle Poyar created the FLUENT framework, based on insights from GTM leaders at companies like Zapier, Clay, Wiz, and Vercel. Employers should start by defining which AI skills matter for the role, then test candidates with curiosity-driven interview questions, real-life examples, creative problem-solving, case studies, and data assignments.

For job seekers, this means you need more than buzzwords. You should be able to talk about AI projects you’ve built, explain how you iterate and debug, and even demonstrate prototypes live. Beyond skills, curiosity and a willingness to tinker with tools are huge signals that set apart transformative hires.

Key Takeaways

• AI is now part of hiring and performance reviews across GTM roles.
• Employers should avoid “AI-resistant” hires and look for capable, adoptive, or transformative candidates.
• Curiosity and creativity matter as much as technical knowledge.
• Real-world projects and problem-solving examples reveal true fluency.
• Case-style interviews and data assignments help separate surface-level users from AI-native talent.
• Candidates should be ready to show, not just tell, how they use AI in their work.

What to do

• For hiring managers:
  • Define which AI skills your role actually needs.
  • Ask curiosity-driven interview questions.
  • Push candidates to share real examples and how they solved challenges.
  • Use case studies and data assignments to confirm skills.
• For job seekers:
  • Go beyond ChatGPT basics - build actual workflows and AI agents.
  • Document real projects where AI saved time or drove results.
  • Be ready to explain how you overcame AI’s limitations.
  • Practice “vibe prototyping” - showing prototypes live.
  • Stay curious, test new tools, and share learnings with others.

‍