Overcoming human psychology may be the most insurmountable chore when implementing technology. Anyone who has successfully navigated through an AI initiative can attest to this.

“When AI-driven change succeeds, it is because leaders put people at the heart of every decision, informed by a rich understanding of their emotions, thoughts, and behaviors. BCG has observed that, in successful AI-driven transformations, 70% of the value is derived from people-related action rather than technology-related action.”

Whether it’s a lean start-up or a multi-division corporation, "the core challenge remains constant," says Gleb Tsipursky, PhD., a cognitive neuroscientist and behavioral economist. “Your workforce must engage with gen AI voluntarily and intelligently.”

AI is poised "to do for creativity and strategy what flexible work did for office culture,” he says in his new book, The Psychology of AI Adoption at Work: From Resistance to Results . "While new algorithms grab headlines, widespread adoption demands more than a glitzy demo. Employees want real transparency about how AI might shift their roles and assurance that efficiency gains do not automatically spell job losses."

Use cases vary – from marketing copy and supply chain analytics to product innovation – but the journey follows a similar pattern, he adds. "Staff want confidence that they’re not ceding their worth to a machine, and they want to see how AI’s strengths interlock with their own. Each AI slip or hallucination tests leadership’s ability to build a learning culture rather than assign blame."

During this journey, there are eight distinct reactions one may see as technology is introduced. In his book, Tsipursky outlined the leading attitudes managers will encounter, from most resistant to most enthusiastic.

  • The AI Alarmist (Most Resistant): “AI Alarmists worry about the potential of this new technology to disrupt their jobs; secondarily, that it will lead to errors for which they will be blamed, or more broadly about threats to the company or even society as a whole. Their psychological underpinnings include status quo bias (a preference to keep doing tasks the way they always have) and loss aversion (a fixation on worst-case scenarios).”
  • The Pragmatic Resister: "Pragmatic Resisters often express wariness about how AI will introduce more errors or complexities than it solves, especially in their own roles, and they have a strong preference for proven processes." Their work tends to be cognitive or procedurally complex, such as accounting, legal tasks, or specialized customer support. Training may help allay their concerns, Tsipursky recommends.
  • The Skeptical Observer: They tend to be passive observers, and believe in letting the chips fall where they may. “They are neither strongly opposed to AI nor motivated to explore it on their own." They may even claim they’re too busy to learn new approaches.
  • The Reluctant Adopter: These employees feel they are forced into the new AI paradigm. “Often they believe that not learning AI puts them at risk of being viewed as obsolete. They fear getting left behind more than they fear or dislike the technology itself." They may even “feel self-conscious about whether their reliance on AI might imply they lack traditional skills.”
  • The Cautious Optimist: "They typically have a growth mindset toward technology and positive digital transformation experiences in the past, leading them to believe in AI’s genuine benefits.” These employees “feel comfortable enough to experiment yet prudent enough to reassure skeptics.”
  • The Efficiency Seeker: They pragmatically focus on AI’s potential to streamline tasks." They like it because it makes them more productive. “Once they see a quantifiable time savings, they are sold. They see themselves as simply working smarter."
  • The AI Evangelist (Most Enthusiastic): “An Evangelist among employees invests personal energy in learning the latest tools, discovering new techniques, and encouraging coworkers to adopt them.” They bring "a blend of genuine tech fascination and a desire to be, and be seen as, a forward-thinking go-getter. They primarily come from tech-savvy backgrounds and secondarily from creative fields, such as marketing or design."

To get started with an AI initiative, Tsipursky recommends identifying the evangelists and have them see through "one or two visible, low-risk use cases that remove friction in daily work.”