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.

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To overcome the fear of new AI technology, block out dedicated, unstructured "playtime" in your calendar. This low-pressure approach encourages experimentation, helping you build the essential skill of quickly learning and applying new tools without being afraid to fail.

Mandating AI usage can backfire by creating a threat. A better approach is to create "safe spaces" for exploration. Atlassian runs "AI builders weeks," blocking off synchronous time for cross-functional teams to tinker together. The celebrated outcome is learning, not a finished product, which removes pressure and encourages genuine experimentation.

To prepare for a future of human-AI collaboration, technology adoption is not enough. Leaders must actively build AI fluency within their teams by personally engaging with the tools. This hands-on approach models curiosity and confidence, creating a culture where it's safe to experiment, learn, and even fail with new technology.

To truly integrate AI, go beyond simply telling your team to "learn more." The founder of Search Atlas advocates for organizing multi-day, in-person hackathons. This focused, collaborative environment, where teams tackle specific problems together, fosters a deeper and faster mastery of practical AI applications than solo, online efforts can achieve.

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 drive AI adoption, CMO Laura Kneebush avoids appointing a single expert and instead makes experimentation "everybody's job." She encourages her team to start by simply playing with AI for personal productivity and hobbies, lowering the barrier to entry and fostering organic learning.

When stuck on product direction, use a simple prompt like "add five new features." The AI acts as a creative partner, generating ideas you may not have considered. Even if most are discarded, this technique can spark inspiration and uncover valuable additions.

To foster genuine AI adoption, introduce it through play. Instead of starting with a hackathon focused on business problems, the speaker built an AI-powered scavenger hunt for her team's off-site. This "dogfooding through play" approach created a positive first interaction, demystified the technology, and set a culture of experimentation.

Organizations fail when they push teams directly into using AI for business outcomes ("architect mode"). Instead, they must first provide dedicated time and resources for unstructured play ("sandbox mode"). This experimentation phase is essential for building the skills and comfort needed to apply AI effectively to strategic goals.

To avoid generic brainstorming outcomes, use AI as a filter for mediocrity. Ask a tool like ChatGPT for the top 10 ideas on a topic, and then explicitly remove those common suggestions from consideration. This forces the team to bypass the obvious and engage in more original, innovative thinking.