Introduction
AI skills are the standout requirement of 2025: they show up in reviews, interviews and job postings across GTM roles. Demand spans engineering, marketing, sales and product. The edge now is knowing how to assess real proficiency, not buzzwords.
Quick definition: AI skills = using tools and agents to deliver measurable impact on workflows and outcomes.
Context
AI fluency is actively evaluated in hiring and inside companies. Job posts mentioning AI skills in GTM roles jumped from 65 (July 2023) to nearly 1,000 (July 2025), per Sumble. Titles range from growth/GTM engineers to social media producers, paid search specialists, BDRs, marketing analysts, content managers, product marketers and CMOs. Capabilities span basics like “working proficiency with AI tools” to advanced use of copilots and agents that personalize messaging, automate outreach and surface CRM insights. The key: AI is context‑dependent. Engineers need to know coding assistants and their limits; recruiters aim to speed screening while reducing bias; support teams must audit documentation for agents and LLMs. A structured method is required to judge competence.
The Challenge
Many candidates mention AI; fewer demonstrate ownership and last‑mile problem solving. The challenge is separating casual prompting from people who redesign processes and produce business impact. You need a process that checks curiosity, creativity, reasoning and end‑to‑end execution, plus cross‑functional enablement.
Six steps to assess AI skills
Action plan: define needed skills, gauge interest/usage, unpack a real build, stress‑test decisions, run a job‑relevant case, and finish with a data/prototype task.
Step 1: decide which AI skills you actually need
Not every role requires multi‑step agents or complex systems. Separate essentials from nice‑to‑have. An adapted rubric (e.g., Zapier) helps score candidates:
- Unacceptable: resists AI tools, reverts to manual playbooks
- Capable: uses popular tools, limited hands‑on, early human‑in‑the‑loop flows
- Adoptive: embeds AI in workflows, prompt tunes, chains models, automates with productivity impact
- Transformative: rewires strategy, builds AI teammates, solves last‑mile issues and drives org‑wide adoption
Step 2: learn about interest level and real usage
Seek innate curiosity and frequent, purposeful use—beyond “do you use ChatGPT?”. Probe mindset, depth of practice and awareness of limitations.
"People think you need a big grand vision for how to adopt AI. The reality is that you want as many people playing with this on a personal productivity level as possible."
Phil Lakin, Enterprise Innovation / Zapier
- What did you rebuild from scratch because AI changed your approach?
- When did AI make a workflow or role obsolete?
- If you had a full‑time AI engineer tomorrow, what would you build?
- What’s the most frustrating AI use case you’ve seen and why?
- What’s the structure of a great prompt, and how does it vary by model/task?
Step 3: unpack a real example they built
Focus on business impact, problem discovery and ownership. Open‑ended prompts let you dig 2–3 layers deeper to see whether they drive the AI or accept outputs at face value.
"Tell me about a recent project or deliverable that you leveraged AI to help with and are proud of. What was it and what made it special?"
Sam Kuehnle, VP Marketing / Loxo
Step 4: probe creativity and problem solving
Shiny stories may not survive scrutiny. Ask how they iterated, where they got stuck, where AI failed, when they overrode outputs and why. Also check whether they ran an AI‑assisted SWOT before finalizing.
Step 5: navigate hard problems via a case
The first prototype feels magical; then comes hours of debugging. Use a real case, like outbound automation comparing a fully automated SDR vs. human‑in‑the‑loop. Weigh pros/cons, current choice and what evidence would flip their answer.
Step 6: add a data/prototype assignment to confirm
Words and execution often diverge. A tailored assignment separates thinkers from builders. Some require shipping prototypes (v0); others check whether candidates choose AI‑first analysis, discuss hallucinations and propose 10x automation of a subsystem.
"The main thing I do is require anyone interviewing for a product or growth role to ship actual prototypes of changes they'd make in v0... I just want to see the actual stuff you produce with it."
Zeb Hermann, General Manager v0 / Vercel
"In our data assignment I ask if they used AI to analyze their data and discuss AI hallucinations. Then I ask how they'd automate or 10x part of the system."
Gaurav Agarwal, COO / ClickUp
Solution / Approach
Run the six steps and tailor checks to each role. Use clear rubrics (unacceptable → transformative), job‑relevant cases and assignments that measure delivery, not narrative. Reward curiosity, ownership and knowledge sharing (workshops, enablement) across teams.
Conclusion
Demand for AI skills is soaring and touches nearly every GTM function. A structured six‑step process separates casual prompt users from AI‑native talent who can rethink strategy, automate safely and deliver measurable business impact.
FAQ
- How can I quickly assess AI skills in interviews?
Use real‑world examples, where they overrode outputs and how they handled blocks; then validate with a short assignment. - Which GTM roles most need AI skills today?
From growth/GTM engineers to marketing, BDR and product marketing; demand is broad and rising. - AI skills “capable” vs. “transformative”: what’s the gap?
Transformative talent rewires strategy, builds AI teammates and drives team‑level impact, not just personal speed. - How do I test prompt quality during hiring?
Ask for structure, model/task variation and prompt‑tuning examples tied to measurable results. - What risks matter when adopting AI in GTM workflows?
Hallucinations, premature automation, missing human review and poor workflow generalization. - Should I include a human‑in‑the‑loop case to judge AI ability?
Yes; it reveals critical thinking, fallback plans and when to stop or adjust automation.