To find talent capable of managing an AI stack, traditional interviews are insufficient. A better test is to provide candidates with platform credits (e.g., Replit) and challenge them to build a functional agent that automates a real business task, proving their practical skills.

To cut through the hype, ask candidates to screen share during an interview and walk through their personal AI workflows. This provides an immediate, unfiltered view of their actual proficiency and whether they operate beyond simple chatbot usage.

Traditional product sense questions are being replaced. AI PM candidates should expect to solve problems live using AI tools or design complex AI-native systems. This shift assesses a candidate's hands-on "builder" capabilities and deep understanding of modern AI architecture.

To assess a candidate's ability to use AI as a thinking partner, have them solve a problem with an LLM. The key is observing their follow-up prompts and their ability to guide the AI step-by-step, rather than just accepting the initial output.

To discern a true AI-native product manager from a tourist, ask what they have built or automated. The ability to point to specific agents created or workflows automated demonstrates deep, practical expertise, which is far more valuable than just discussing AI concepts.

To unlock the full potential of AI, don't just assign it single tasks. Instead, ask: 'If I had infinite, always-available junior talent, what is the ideal process I'd have them follow for a new ticket?' This framing helps you design more comprehensive, multi-step prompts and automations.

Zapier's hiring process now requires candidates to demonstrate 'AI fluency' through repeatable systems that measurably improve their work. Merely using AI for one-off tasks is insufficient; they must show how AI is deeply embedded into their core workflows, setting a new bar for talent.

While AI tools will become simpler, the core skill for leveraging them is the ability to think in systems and workflows. People who can break down a business process into logical, step-by-step instructions for an agent to follow will have a significant advantage in the age of AI automation.

Glean has updated its interview process to screen for "AI fluency" across all departments. They don't expect expertise. Instead, they test for curiosity and initiative by asking candidates how they've personally used AI, looking for a mindset that embraces new ways of working.