The primary barrier to enterprise AI adoption isn't the technology, but the workforce's inability to use it. The tech has far outpaced user capability. Leaders should spend 90% of their AI budget on educating employees on core skills, like prompting, to unlock its full potential.

Instead of relying solely on top-down, consultant-led workflow automation, enterprises should empower individual employees with AI tools. This builds user fluency and intuition, allowing them to pull AI into their own workflows, resulting in greater overall impact and less disempowerment.

For those early in their careers overwhelmed by the pace of AI, the key is focus, not breadth. Instead of chasing every new tool, prioritize becoming an expert in one core AI assistant platform (like ChatGPT or Claude). This deep mastery is enough to transform your work and provides a solid foundation.

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.

AI agent platforms are typically priced by usage, not seats, making initial costs low. Instead of a top-down mandate for one tool, leaders should encourage teams to expense and experiment with several options. The best solution for the team will emerge organically through use.

The fastest way to understand AI's value is by using it for your actual work from day one, not by working through tutorials or sample projects. Applying AI to a genuine need, like analyzing your team's data or drafting a real memo, provides immediate, tangible feedback on its capabilities and limitations.

For large, traditional companies, the most critical first step in AI adoption isn't building tools, but fostering deep understanding. Provide teams sandboxed access to AI models and company data, allowing them to build intuition about capabilities before crafting strategy.

For companies given a broad "AI mandate," the most tactical and immediate starting point is to create a private, internalized version of a large language model like ChatGPT. This provides a quick win by enabling employees to leverage generative AI for productivity without exposing sensitive intellectual property or code to public models.

To ease the transition to AI workflows, begin by encouraging employees to use common tools like ChatGPT with simple, conversational prompts. This builds comfort with generative responses. Only after this foundation is set should you introduce the concept of supervising small, autonomous AI agents, making adoption more natural.