To encourage AI adoption, Bitly's marketing team holds a weekly, low-preparation "How I AI" meeting. Team members share personal AI use cases, fostering a safe learning environment, spreading practical knowledge across roles, and helping overcome the common feeling of imposter syndrome around AI.

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 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.

Companies once hired siloed 'digital experts,' a role that became obsolete as digital skills became universal. To avoid repeating this with AI, integrate technologists into current teams and upskill existing members rather than creating an isolated AI function that will fail to scale.

When employees are 'too busy' to learn AI, don't just schedule more training. Instead, identify their most time-consuming task and build a specific AI tool (like a custom GPT) to solve it. This proves AI's value by giving them back time, creating the bandwidth and motivation needed for deeper learning.

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 effectively learn AI, one must make a conscious mindset shift. This involves consistently attempting to solve problems with AI first, even small ones. This discipline integrates the tool into daily workflows and builds practical expertise faster than sporadic, large-scale projects.

Moving beyond casual experimentation with AI requires a cultural mandate for frequent, deep integration. Employees should engage with generative AI tools multiple times every hour to ideate, create, or validate work, treating it as an ever-present collaborator rather than an occasional tool.