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.

For leaders overwhelmed by AI, a practical first step is to apply a lean startup methodology. Mobilize a bright, cross-functional team, encourage rapid, messy iteration without fear, and systematically document failures to enhance what works. This approach prioritizes learning and adaptability over a perfect initial plan.

Unlike traditional software development, AI-native founders avoid long-term, deterministic roadmaps. They recognize that AI capabilities change so rapidly that the most effective strategy is to maximize what's possible *now* with fast iteration cycles, rather than planning for a speculative future.

The true challenge of AI for many businesses isn't mastering the technology. It's shifting the entire organization from a predictable "delivery" mindset to an "innovation" one that is capable of managing rapid experimentation and uncertainty—a muscle many established companies haven't yet built.

Competing in the AI era requires a fundamental cultural shift towards experimentation and scientific rigor. According to Intercom's CEO, older companies can't just decide to build an AI feature; they need a complete operational reset to match the speed and learning cycles of AI-native disruptors.

The rapid pace of AI makes traditional, static marketing playbooks obsolete. Leaders should instead foster a culture of agile testing and iteration. This requires shifting budget from a 70-20-10 model (core-emerging-experimental) to something like 60-20-20 to fund a higher velocity 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.

In the AI era, the pace of change is so fast that by the time academic studies on "what works" are published, the underlying technology is already outdated. Leaders must therefore rely on conviction and rapid experimentation rather than waiting for validated evidence to act.

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.