New research reveals how relying on generative AI for management tasks can erode practical judgment, and how executives can protect independent problem solving.
In September 2026, the University of Bath detailed findings from a new study published in the Academy of Management Review. The research examines how generative AI may alter the way managers develop practical judgment. Scholars from the University of Bath, Ohio State University, the University of Lausanne, and Cardiff University conducted the research. Their study examines a concept called epistemic de-skilling, which describes the gradual loss of knowledge-related capabilities when professionals outsource too much thinking to AI.
The researchers argue that AI processes codified information efficiently. However, relying on it as a substitute for direct inquiry can reduce opportunities to build judgment. This risk increases significantly under time pressure. Managers facing tight deadlines often treat AI as a shortcut rather than a tool for testing ideas. Over time, these daily habits may fundamentally change how operators approach complex problem solving in the workplace.
The research focuses heavily on managerial phronesis. This concept refers to the practical wisdom that operators develop through real-world experience, reflection, and direct human interaction. Dirk Lindebaum, a lead researcher and professor of management and organization at the University of Bath, explains a central limitation of current technology. He notes that generative AI does not experience the world, understand the consequences of decisions, or grasp the social complexities of workplaces.
Because AI lacks this contextual grounding, it cannot replicate the nuanced learning that comes from firsthand experience. The study outlines how frequent AI substitution weakens the foundational skills required to diagnose organizational problems. When a manager uses a chatbot instead of brainstorming with colleagues, they miss a chance to test their ideas against human feedback. Asking an AI for a solution instead of speaking with the people directly involved removes a critical layer of organizational context.
The University of Bath researchers contrast this risk with a model they call epistemic up-skilling. In an up-skilling scenario, managers use AI to challenge assumptions, evaluate alternative scenarios, and test their own reasoning. The tool serves as a reflective mirror rather than a replacement for cognitive effort. When used effectively, AI can highlight blind spots and prompt operators to examine their logic more rigorously.
For founders and operators managing high stress and demanding schedules, convenience often defaults to reliance. Delegating first-pass thinking to a chatbot saves immediate cognitive energy during an exhausting week. Yet, the research suggests that this convenience carries a developmental cost over the long term. Maintaining strong cognitive performance and mental clarity requires continuous engagement with difficult, unstructured problems.
Executives must structure workflows that use AI as a decision support layer, not a default decision maker. Pete Dusché, founder and principal consultant at Hesion Leadership Consulting, describes this as a choice between an erosion path and a better-judgment path. Organizations must decide if they are building enough accountability to push managers toward the latter. Accountability serves as a mechanism to preserve independent judgment in the face of automated convenience.
Dusché argues that managers retain ownership of a decision when they must explain their reasoning to others. When leaders justify their choices without relying solely on AI outputs, they maintain their diagnostic ability. Accountability becomes more than a compliance mechanism in this context. It functions as a necessary friction that protects human reasoning and supports sustainable executive performance.
Leaders can apply this insight by requiring a human-first explanation for consequential decisions. Before reviewing AI output, a responsible manager should state the problem and the relevant context in their own words. They should also articulate preferred options and key risks clearly. This practice preserves a dedicated window for independent reasoning before an algorithm influences the solution.
Furthermore, protecting deliberate practice is crucial for junior operators developing foundational skills. Providing opportunities to attempt selected tasks unaided builds the judgment required for future leadership roles. Teams must also separate stable routines from rapidly changing situations. While AI excels at codified work, situations involving changing conditions, employee relationships, or ethical ambiguity demand direct human inquiry.
The HR coverage of the study also cites related research from Microsoft Research and Carnegie Mellon University. This associated study observed 319 knowledge workers and examined 936 real-world tasks involving generative AI. The findings highlight a specific behavioral pattern among knowledge workers using these tools in their daily routines.
The Microsoft Research study found that greater confidence in AI tools was associated with less critical engagement with the output. When workers trusted the system highly, they often applied weaker verification habits. This highlights that high trust in an automated system requires stronger questioning protocols to prevent errors. Relying blindly on an automated output can introduce unchecked biases or factual inaccuracies into strategic decisions.
The practical implication is that organizations must balance the adoption of new tools with rigorous oversight. High trust in AI should prompt more rigorous human validation, not less. Executives need to monitor whether teams are accepting AI recommendations despite contradictory evidence or skipping necessary probing questions.
To combat this, teams should audit for automation bias regularly. Managers must review whether their departments are reducing the number of human perspectives consulted when AI is involved. Tracking capability alongside speed ensures that operators can still explain decisions, detect errors, and handle unfamiliar cases without digital assistance.
Clear boundaries exist regarding what this research proves. The study published in the Academy of Management Review is a conceptual contribution. The available reporting does not provide a detailed experimental methodology, a causal effect size, or longitudinal measurements of managerial skill loss. Claims about epistemic de-skilling represent a research-backed risk framework, not absolute proof of inevitable cognitive decline.
Furthermore, the Microsoft Research and Carnegie Mellon University findings demonstrate an association between AI confidence and reduced critical thinking. The available coverage does not establish that this confidence directly causes a decline in reasoning skills. It is entirely possible that workers who are already pressed for time or distracted are simply more likely to accept AI outputs without verification.
The researchers do not advocate for banning AI in the workplace. Instead, they recommend redesigning workflows so that employees continue developing human capabilities that automation cannot replicate. AI can surface alternatives and summarize vast amounts of information efficiently. The strongest operational strategy is deliberate human-AI collaboration, recognizing both the utility of automation and the irreplaceable value of human judgment.
As generative AI transitions from experimental use to standard operating procedure, management frameworks will need to adapt. Future organizational research will likely focus on measuring the precise impact of AI on long-term skill retention. Industry leaders should watch for new operational models that formally separate stable, codified routines from ambiguous situations requiring moral judgment.
We expect to see more enterprises implement explicit accountability frameworks for AI-supported decisions. The focus will shift from measuring mere time saved to evaluating the quality of human reasoning. Leaders will need to preserve learning experiences in which managers understand why and how established routines are changing. Transparency will become a core element of these new workflows.
Employees should be able to disclose when they used a chatbot and still explain the reasoning behind the final work. Establishing clear escalation paths will also become standard practice. AI-supported workflows will specify exactly when a matter requires direct human contact, specialist input, or executive approval.
Ultimately, the organizations that thrive will not be those that automate the most thinking. They will be the ones that use AI to enhance, rather than replace, the demanding work of independent problem solving. By preserving deliberate practice and human-first accountability, executives can protect the very judgment that makes their leadership valuable.
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