CEOs of companies in every industry around the world are grappling with a common question: If so many of their employees are using AI, why hasn’t there been an explosion in value creation? A recent BCG survey quantified this failed promise, finding that 60% of companies globally were not generating any material value from AI despite substantial investment. The answer lies in organizations’ focus on AI as a technology deployment, rather than how (or if) employees truly integrate AI into their ways of working.

We’ve observed that even leading organizations are often prioritizing the low-hanging fruit—focusing on achieving efficiencies in peripheral or administrative activities, rather than reimagining work by embedding the technology in core, high-value activities. For most companies, getting to the latter point will be a journey, one that centers on the experiences of their employees. Through our research of adoption patterns, we’ve identified five discrete stages that workers go through. Understanding this big picture view enables leaders to chart their company’s overall progress, but they can’t stop there. They’ll also need to deeply understand employees’ personal adoption journey, including the psychological and organizational factors that can hold them back—or propel them forward.

Adoption Quality Is the Goal, Not Adoption Rate

Most organizations are preoccupied with inputs, such as the number of logins to AI tools or the amount of time spent using them, as measures of adoption. That approach misses the shift in mindset that occurs when AI becomes central to an employee’s core work. This shift activates a virtuous circle—one that creates momentum that drives deeper and more sustained adoption not just for that employee but also across an organization. What really matters, then, is the quality of AI use and how meaningfully work is being reinvented.

To deepen AI adoption and improve its impact, organizations must first understand how employees are using the technology. Through our work with dozens of companies, we’ve identified a discernible AI-adoption pattern that spans five stages, starting with employees using AI much like a search engine and, in most cases, ending with semiautonomous collaboration—or in some rare instances today, fully autonomous orchestration by AI agents.