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.

The conventional wisdom that enterprises are blocked by a lack of clean, accessible data is wrong. The true bottleneck is people and change management. Scrappy teams can derive significant value from existing, imperfect internal and public data; the real challenge is organizational inertia and process redesign.

AI is a 'hands-on revolution,' not a technological shift like the cloud that can be delegated to an IT department. To lead effectively, executives (including non-technical ones) must personally use AI tools. This direct experience is essential for understanding AI's potential and guiding teams through transformation.

When transitioning Box to be "AI first," CEO Aaron Levie explicitly communicated that the goal was not to reduce headcount or cut costs. Instead, he framed AI as a tool to increase company output, speed, and customer service, which successfully aligned employees with the new strategy by removing fear.

The primary focus for leaders should be fostering a culture of safe, ethical, and collaborative AI use. This involves mandatory training and creating shared learning spaces, like Slack channels for prompt sharing, rather than just focusing on tool procurement.

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.

When driving major organizational change, a data-driven approach from the start is crucial for overcoming emotional resistance to established ways of working. Building a strong business case based on financial and market metrics can depersonalize the discussion and align stakeholders more quickly than relying on vision alone.

To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.

To win over skeptical team members, high-level mandates are ineffective. Instead, demonstrate AI's value by building a tool that solves a personal, tedious part of their job, such as automating a weekly report they despise. This tangible, personal benefit is the fastest path to adoption.