Harvard Business Review recently published research that should give every digital transformation leader reason to reflect. In four experiments involving over 3,500 professionals, the authors measured the effects of using generative AI (Gen AI) on everyday knowledge work tasks such as drafting performance reviews, brainstorming social media content, and writing emails.
The main finding is not surprising: when humans collaborate with Gen AI, the work is completed faster and is rated as higher quality by independent evaluators. Performance reviews became richer and more analytical, while emails were perceived as warmer and more empathetic. For managers focused on quarterly efficiency targets, this enhancement seems irresistible.
However, the study also revealed a more concerning effect. After completing an AI-assisted task, participants moved on to another task without AI support, and their intrinsic motivation declined by an average of 11 percent, while their reported boredom increased by 20 percent. The technology that boosts one aspect of the workflow can diminish the motivation people bring to subsequent tasks.
Why Motivation Dips When AI Steps In
The researchers attributed this decline in motivation to a loss of perceived control. Generative AI handles the cognitively demanding parts of a task—those moments when individuals exercise judgment and creativity. While there is immediate relief, it often leads to disengagement because people feel less like the authors of their own work. When they return to solo tasks, the restored control makes those tasks feel flat in comparison. Over time, this dynamic could erode overall job satisfaction and even lead to burnout.
Strengths and Shortcomings of the Study
What stands out is the human-centric perspective. Too often, discussions about AI revolve around model size and GPU counts; this research focuses on how technology affects the actual experience of work. Equally valuable is its comprehensive view. Productivity gains in one step do not necessarily translate into overall value for the entire workflow.
The authors offer tactical suggestions: rotate employees between AI-assisted and non-assisted tasks; combine machine output with human refinement; and clearly communicate that AI is there to augment, not replace, human contributions.
While this guidance is sensible, a key concern arises about what will happen five years from now when large-scale Gen AI initiatives integrate more deeply into workflows. At that point, the primary challenge will be to reposition today’s workforce into roles we have only begun to envision.
Connection to Day-to-Day Practice
In my experience, blended human-AI collaboration is becoming standard rather than an exception. Mastery of prompt engineering and critical evaluation is now as essential as a driver’s license once was. Additionally, the importance of data strategy is elevated; without high-quality, well-governed data, no enterprise model or employee can realize full value.
Implications for Digital-Transformation Leaders
1. Design for engagement, not just throughput. When introducing AI, monitor employee sentiment as carefully as you track cycle time.
2. Invest in universal AI literacy. Everyone—from frontline staff to the C-suite—needs to understand when to rely on the model and when to trust their judgment.
3. Treat data as a first-class asset. The effectiveness of AI and its users hinges on the organization’s ability to collect, clean, and govern information effectively.
4. Plan for the long game on reskilling. While rotating tasks may help in the short term, sustained competitiveness will depend on creating pathways into the new roles that large-scale AI will necessitate.
The discussion about generative AI is no longer centered on whether it can enhance productivity; it clearly can. The pressing question is whether organizations can harness those gains while preserving (or even enriching) the motivation that drives people to learn, innovate, and find meaning in their work. Striking this balance, more than any algorithmic breakthrough, will define the future of work.
Written by:
CEO, Managing Partner
