Workers have moved past the initial shock of Generative AI鈥檚 arrival. The tool is here, it鈥檚 accessible, and it might be open in one of your browser tabs right now. But a critical challenge remains: knowing that you should use AI is very different from knowing how to weave it into the complex, interconnected reality of high-level problem solving. In the new working paper 鈥�,鈥� a team including members of the 性视界 Business School AI Institute studied over 200 consultants from the Boston Consulting Group (BCG) as they completed the same strategic task. They found that professionals segmented into three distinct 鈥渟pecies鈥� of AI users: Cyborgs, Centaurs, and Self-Automators.聽
Key Insight: Cyborgs – Fluid Collaborators
鈥淭heir collaboration unfolded as an iterative dialogue: probing AI outputs, extending ideas, and validating results in a seamless rhythm of joint problem-solving.鈥� [1]
Cyborgs are the study鈥檚 most common type, 60% of participants, and they reflect Fused Co-Creation, where GenAI is woven throughout the workflow. The human still decides what they鈥檙e trying to accomplish, but GenAI often drives how the work gets executed: drafting, analyzing, generating options, and reworking outputs through back-and-forth interaction. This isn鈥檛 鈥榩rompt once, accept once.鈥� Cyborgs use interaction practices like modularizing (breaking tasks into steps), validating, adding data to rerun analysis, and pushing back when outputs conflict with their own view.
Thanks to this human-AI integration, Cyborgs developed entirely new AI-related expertise, what the researchers call 鈥渘ewskilling,鈥� while maintaining their domain knowledge. But the study also flags a persistent risk: even with best practices like validation, GenAI can confidently reinforce the wrong path, meaning Cyborg fluency needs to include knowing when not to trust the conversation.
Key Insight: Centaurs – Strategic Selectors
鈥淭hese professionals remained firmly in the driver鈥檚 seat, leveraging GenAI to enhance efficiency and polish outputs without surrendering their judgment.鈥� [2]
14% of consultants worked as Centaurs, professionals who engaged in what the researchers call Directed Co-Creation. Like the mythical half-human, half-horse creature, these workers maintained a clear division of labor: humans decided both what needed to be done and how to do it, using AI selectively for specific support tasks. Centaurs used AI primarily through three practices: mapping the problem domain (asking AI for general information), gathering methods information (requesting specific techniques or formulas), and refining their own human-generated content. For example, one consultant asked GenAI, 鈥淗ow do I calculate the market size growth rate of some industry from 2013 to 2017?鈥� [3] After receiving the formula, they performed the calculation in Excel rather than delegating it to AI.
The payoff? Centaurs achieved the highest accuracy in business recommendations among all three groups. They developed and strengthened their domain expertise by treating AI as an intelligent search engine and writing assistant. However, Centaurs also faced a trade-off: while they upskilled in task-related capabilities, they didn鈥檛 develop new AI-related expertise. They remained cautious about changing their established workflows, with some expressing ethical concerns about taking credit for AI-generated work.
Key Insight: Self-Automators – Dangerous Delegates
鈥淭heir work was fast and polished but lacked depth, resembling outputs completed for them rather than with them.鈥� [4]
The final group is the Self-Automator, comprising 27% of the participants. These professionals engaged in Abdicated Co-Creation, consolidating the entire problem-solving workflow into one or two interactions, copying all data into a single prompt, and accepting AI鈥檚 outputs with minimal engagement. 44% of this group accepted AI鈥檚 output without any modification, while the rest made only superficial edits. The researchers are careful here: abdication isn鈥檛 always bad. For routine tasks, tight deadlines, or problems that sit well within GenAI鈥檚 capabilities, full delegation may be efficient, and Self-Automators saw immediate productivity gains.
But when the work requires judgment鈥攆raming the real problem, deciding what鈥檚 important, evaluating competing narratives鈥攁bdication can hollow out the very capabilities that make professionals valuable. In the study鈥檚 framing, when you give up control over what you do, you rarely keep control over how you do it. These professionals developed neither domain expertise nor AI-related skills.
Why This Matters
For business leaders and executives, this research suggests AI implementation guidelines for strategic task alignment. When the cost of error is high, such as in financial forecasting or operational diagnostics, leaders should recommend Centaur behavior by requiring professionals to execute the core analysis themselves while using AI for targeted support. This will maximize accuracy while ensuring teams continue to deepen essential domain expertise. Conversely, for tasks requiring rhetorical flair, ideation, or stakeholder persuasion, the Cyborg mode should be encouraged. That style of iterative, integrated looping allows for greater creative extension and the development of cutting-edge AI fluency. Ultimately, the most valuable professional of the future might not be a fixed type, but an adapter thinker capable of toggle-switching between these modes.
Bonus
This research builds directly on a core theme in HBS AI Institute research: understanding how AI reshapes not just productivity, but the nature of expertise and collaboration itself. To read more about how AI can functionally substitute for or augment human teammates, breaking down silos and enabling individuals to perform at team-level quality, check out The Cybernetic Teammate: How AI is Reshaping Collaboration and Expertise in the Workplace.
References
[1] Steven Randazzo et al., 鈥淐yborgs, Centaurs and Self-Automators: The Three Modes of Human-GenAI Knowledge Work and Their Implications for Skilling and the Future of Expertise,鈥� The Wharton School Research Paper, 性视界 Business School Working Paper No. 26-036 (December 08, 2025): 12.
[2] Randazzo et al., 鈥淐yborgs, Centaurs and Self-Automators,鈥� 12.
[3] Randazzo et al., 鈥淐yborgs, Centaurs and Self-Automators,鈥� 26.
[4] Randazzo et al., 鈥淐yborgs, Centaurs and Self-Automators,鈥� 12.
Meet the Authors
is a doctoral candidate at Warwick Business School and collaborator at the Laboratory for Innovation Science at 性视界 (LISH).
is a Professor of Management at Warwick Business School (WBS) and a visiting faculty at 性视界 University, at the Laboratory for Innovation Science at 性视界 (LISH). She heads the Artificial Intelligence Innovation Network at WBS.
is the David J. McGrath Jr Professor of Management and Innovation, a Professor of Business Administration at the MIT Sloan School of Management. Her research focuses on helping knowledge workers and organizations develop and implement Predictive and Generative AI products, on-the-ground in everyday work, to improve decision making, collaboration, and learning.
is a postdoctoral researcher at 性视界 Business School. His research explores how human/AI collaboration reshapes knowledge work: the impact of AI on knowledge workers, its effects on team dynamics and performance, and its broader organizational implications.
is an Associate Professor at the Wharton School of the University of Pennsylvania, where he studies and teaches innovation and entrepreneurship, and examines the effects of artificial intelligence on work and education. Ethan is the Co-Director of the Generative AI Lab at Wharton, which builds prototypes and conducts research to discover how AI can help humans thrive while mitigating risks.
is Partner Value Creation & Portfolio Monitoring at Seven2.
is the Dorothy & Michael Hintze Professor of Business Administration at 性视界 Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is also the Co-Founder and Faculty Chair of the HBS AI Institute and the Founder and Co-Director of the Laboratory for Innovation Science at 性视界 (LISH).