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.

The fastest way for smaller tech companies to leverage AI is not by building complex proprietary models, but by training employees to master existing consumer-grade tools like Claude and ChatGPT. This treats AI adoption as a skill to be developed through practice and experimentation, yielding immediate productivity gains.

Providing AI licenses isn't enough. Companies must actively manage the transition of employees from basic users (asking simple questions) to advanced users who treat AI as a collaborator for complex, high-value tasks, which is where real ROI is found.

Leaders feeling pressure to deploy AI should focus it internally first. Using AI to enrich and manage product data catalogs is a low-risk, high-reward application that improves efficiency and builds the necessary foundation for future, more complex customer-facing AI features.

Bill Glenn suggests a phased AI rollout for teams. Phase 1 focuses on efficiency and automating repeatable tasks to gain productivity. Phase 2 moves to strategic work, using AI for insights and decision-making assistance. This provides a clear, manageable roadmap for adoption.

To make "AI Ready" tangible, Unum uses a two-pronged approach. "Everyday AI" (e.g., Copilot) is rolled out to the entire company to foster citizen development and reduce fear. "Embedded AI" involves deep, mandatory training for engineers to integrate AI directly into their core workflows and boost productivity.

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.

The path to enterprise AI adoption follows a typical change curve. To bypass initial fear and rejection, organizations should first apply AI to transform familiar, high-friction workflows. This strategy builds momentum and demonstrates value before tackling entirely new, innovative business models.

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.