To move beyond 'vibe-based' AI usage, create an automated weekly report that scores your performance on key dimensions like automation and learning. This provides objective feedback, grounds your sense of progress in data, and highlights specific areas for improvement.

Avoid vague, company-wide AI mandates. Instead, apply a maturity framework to individual processes (e.g., account research). This approach builds a practical roadmap, moving specific use cases up the maturity ladder as needed and preventing costly over-engineering.

Formal AI competency frameworks are still emerging. In their place, innovative companies are assessing employee AI skills with concrete, activity-based targets like "build three custom GPTs for your role" or completing specific certifications, directly linking these achievements to performance reviews.

AI curiosity involves individuals testing tools in isolation. AI fluency is a collective capability where teams share a common language, integrated workflows, and a foundational understanding of how AI drives strategy. This fluency is built through consistent, shared learning and processes.

If you're unsure where to start with AI, begin with self-diagnosis. Tell the AI your role, describe your daily calendar and tasks, and ask it to identify where it can help. LLMs excel at pattern matching and can reflect back opportunities for automation you might have missed.

To make AI adoption tangible, Zapier built rubrics defining "AI fluency" for different roles and seniority levels. By making these skills a measurable part of performance reviews and rewards, you create clear incentives for employees to invest their time in developing them, as behavior follows what gets measured.

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.

Instead of guessing where AI can help, use AI itself as a consultant. Detail your daily workflows, tasks, and existing tools in a prompt, and ask it to generate an "opportunity map." This meta-approach lets AI identify the highest-impact areas for its own implementation.