If you had six extra hours in your week, what would you do with them?
For me, it’s innovating, learning, and developing my team in the ways they uniquely deserve. And getting more sleep.
As a leader exploring AI opportunities in my work, I was excited to learn these hours were no longer just hypothetical. In recent studies, professionals and highly skilled workers actively using AI reported time savings of 6-12 hours a week and performance gains of nearly 40% (Writer; MIT Solan). Those same users reported shifting time from administrative and drafting tasks to coaching their teams, thinking strategically, and making data-driven decisions faster, without compromising outcomes (Writer).
I’ll be the first to admit I’ve lost countless hours of sleep since 2023 exploring AI tools. I reinvested time saved using AI into learning strengths, limitations, and discovering the possibilities of early GPTs (followed by Copilot, Claude, and Gemini). However, as I increased my AI use over the years, I found my early curiosity giving way to intensifying feelings of overwhelm, stress, and anxiety about its impact. As I got more comfortable with AI, I also got burned out, overloaded, and drained, a pattern shared by many AI users (HBR).
The unspoken reality of AI: unrealized potential and unchecked anxiety
There’s a version of the story that dominates leadership calls: the confident, forward-looking promise of accelerated adoption, improved productivity, and tangible returns on significant AI investments.
What rarely makes it onto the agenda is the stress and complexity many of us are carrying. Over 60% of executives fear job loss, and 70% of CEOs report significant stress from their company’s AI transformation (Writer).
In my conversations with leaders across industries, I notice the same tensions:
- The pressure to be knowledgeable about tools that evolve daily
- The unsettling moment when AI applies in seconds a skill you spent years building
- The fear of irrelevance, replacement, or being outperformed with every prompt
- The weight of leading a team and sustaining trust when change is the only certainty
There’s a term for what many of us are carrying: it’s AI anxiety, a new flavor of “technostress,” a term coined in the 1980s to describe the feeling of overwhelm and inability to cope through technological disruption. The triggers are the same as they were back then, from a fear of being replaced, skills becoming obsolete, loss of control, and overload to fatigue when the pace of technological change outpaces our ability to cope with that change (International Journal of Human-Computer Interaction; European Journal of Information Systems). What’s new is the ever-intensifying pace of technological change, the breadth of disruption, and that current AI models feel eerily more human in a way ATMs, printers, and computers simply do not (Psychology Today).
The path to better adoption is through AI anxiety, not around it.
The emotional responses AI elicits aren’t inherently positive or negative; they’re a form of insight. Research shows that emotions in the workplace provide us with valuable information about the employee experience. When organizations focus on accelerating AI adoption while ignoring the emotions employees experience throughout it, they erode psychological safety, trust, and the health of the workforce.
Despite most organizations failing to materialize the anticipated value of AI (BCG), individuals are using it at growing rates globally (Microsoft AI for Good Lab). The question isn’t whether your team will use AI, it’s whether you’ll help them do it well.
Five tips for leaders to move to better AI adoption.
1. Start with awareness of your own emotions around AI. What are your reactions to automation? What tasks are you excited to automate? Which ones would you aim to protect? When leaders start by understanding their own emotions and reactions to AI, they build the foundation and credibility needed to model it for their team.
2. Bring that awareness to your team. In your next one-on-one or team meeting, ask your team what they’d do with six extra hours per week, then explore how AI could free up that time together. Encourage open and honest knowledge sharing to build collective, healthy AI practices.
3. Champion wellbeing as part of AI transformation. Share wellbeing practices that alleviate the emotional and cognitive toll of AI use. For leaders, this means modeling recovery and healthy boundaries, advocating for your team’s needs, and making it safe to admit how people are navigating the change without fear.
4. Invest in upskilling and dedicated experimentation time. Create space for your team to build AI skills in the context of their daily work and career goals. This directly addresses AI anxiety rooted in complexity and skills obsolescence and signals that the organization remains invested in career development, even if that path is less linear than before.
5. Connect your team’s work to the organization’s AI strategy. Use the organization’s AI roadmap and responsible guiding principles to help your team understand how they contribute to the AI strategy. That clarity helps build the trust responsible, value-aligned AI use requires, reassuring employees and other key players alike
AI anxiety may never be fully eliminated, but we can leverage that anxiety to propel us forward.
Let’s get started. Interested in learning more about McLean’s research or services to navigate today’s workplace priorities? Reach out to Jon Campbell at jcampbell@mcleanco.com.
Works Cited
Bedard, Julie, et al. “AI Adoption Puzzle: Why Usage Is Up but Impact Is Not.” BCG, 8 Dec. 2025. Accessed May 2026.
Habib, May. “2026 AI Adoption Survey: AI Adoption in the Enterprise.” Writer, 2026. Accessed May 2026.
Kaya, Feridun, et al. “The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes Toward Artificial Intelligence.” International Journal of Human-Computer Interaction, 7 Dec. 2022. Accessed May 2026.
Misra, Amit, et al. “Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage.” Microsoft AI for Good Lab, Oct. 2025. Accessed May 2026.
Nastjuk, Ilja, et al. “Integrating and Synthesising Technostress Research: A Meta-Analysis on Technostress Creators, Outcomes, and IS Usage Contexts.” European Journal of Information Systems, 33(3), 361-382.09. Jan. 2023. Accessed May 2026.
Pang, Damian. “Why Does ChatGPT Feel So Human?” Psychology Today, 14 Jan. 2023 Accessed May 2026.
Ranganathan, Aruna, and Xingqi Maggie Ye. “AI Doesn’t Reduce Work – It Intensifies It.” Harvard Business Review (HBR), 9 Feb. 2026. Accessed May 2026.
Somers, Meredith. “How Generative AI can Boost Highly Skilled Workers’ Productivity.” MIT Solan, 19 Oct. 2023. Accessed May 2026.