To get teams comfortable with AI, start with playful, interactive exercises that have no business goal, like styling an app to look like MySpace. This low-stakes experimentation makes the technology less intimidating, fosters creative thinking, and helps participants discover novel applications they can later bring to their actual work.

To familiarize engineers with agentic coding workflows, Brex created a new interview process that requires AI tool usage. They then had every current engineer and manager complete the interview, forcing hands-on experience and revealing skill gaps in a practical setting.

Voice mode offers a more natural and effective way to practice for interviews than text-based AI. For best results, provide the AI with your resume and the job description for the role. This allows it to tailor questions, provide more relevant feedback, and simulate a real interview scenario.

For product managers not yet working on AI, the best way to gain experience is to build simple AI tools for personal use cases, like a parenting advisor or a board game timer. Using no-code prototyping tools, they can learn the entire development lifecycle—from ideation to prompting and user feedback—without needing an official AI project at work.

To simulate interview coaching, feed your written answers to case study questions into an LLM. Prompt it to score you on a specific rubric (structured thinking, user focus, etc.), identify exact weak phrases, explain why, and suggest a better approach for structured, actionable feedback.

To assess a product manager's AI skills, integrate AI into your standard hiring process rather than just asking theoretical questions. Expect candidates to use AI tools in take-home case studies and analytical interviews to test for practical application and raise the quality bar.

Rehearse difficult conversations by having an AI adopt the persona of your boss, partner, or employee. This allows you to practice your approach, refine your messaging, and anticipate reactions in a safe environment, increasing your confidence and effectiveness for the real discussion.

To ensure comprehension of AI-generated code, developer Terry Lynn created a "rubber duck" rule in his AI tool. This prompts the AI to explain code sections and even create pop quizzes about specific functions. This turns the development process into an active learning tool, ensuring he deeply understands the code he's shipping.

After receiving feedback that his writing was too long, a PM built a custom GPT to make messages more concise. He fed it newsletters and books on effective writing from experts, creating a personalized coach that helped him apply the feedback in his daily work, leading to better engagement from colleagues.