Researcher Warns Generative AI May Reinforce Harmful Leadership Patterns

Novogain founder Marko Kesti says GenAI may reinforce harmful leadership patterns unless AI understands team context and productivity impact

The rapid adoption of generative AI in supervisory and managerial work may unintentionally weaken leadership effectiveness if it is used without understanding team context, workload, and managerial capability, says Marko Kesti, a researcher in human productivity and developer of reinforcement learning-based leadership AI.

As generative AI tools become increasingly common in communication, decision support, and day-to-day management tasks, many organizations assume that AI automatically improves leadership. According to Kesti, this assumption is too simplistic.

“Generative AI can produce useful and convincing answers, but it does not necessarily understand the real situation of a team, the leadership capability of the supervisor, or the productivity impact of specific management actions,” said Kesti. “It may optimize the quality of the response, while failing to optimize the actual outcome.”

Kesti warns that in supervisory work; generative AI may reinforce existing but ineffective management patterns rather than correct them. In practice, a micromanaging supervisor may gain more tools for control, a manager who over-relies on email may become more efficient at remote one-way communication, and an already overloaded supervisor may receive even more suggestions without regard to available time or implementation capacity.

He identifies four underrecognized risks in the use of generative AI for leadership support:

• reinforcement of harmful management habits
• optimization of the wrong communication channel
• increased cognitive overload for supervisors
• mismatch between recommended actions and the supervisor’s actual capability level

According to Kesti, these risks matter because effective leadership is not primarily about producing better wording or more polished communication. It is about choosing actions that improve the productivity, capability, and well-being of a specific team in a measurable way.

“In leadership development, the central question is not whether AI can generate good advice,” Kesti said. “The real question is whether AI can identify which actions are likely to improve this team’s performance, in this context, with this supervisor, and with what expected impact.”

To address this challenge, Kesti has developed Novogain, an AI engine designed specifically for team development and productivity improvement. Unlike generative AI systems, Novogain is based on reinforcement learning, simulation modeling, and a production-function approach that connects leadership decisions with expected productivity outcomes.

The Novogain engine analyzes the team situation, considers the supervisor’s operating environment, and simulates the likely effects of different development actions. Its purpose is to identify the most effective actions for a given team context and estimate their potential impact on productivity and financial performance.

Kesti notes that this kind of AI is fundamentally different from systems designed mainly for text generation.

“Leadership improvement requires more than fluent output,” he said. “It requires modeled decision support that takes into account team dynamics, implementation capability, and the expected effectiveness of different actions.”

The principles behind the Novogain AI engine were presented before the generative AI breakthrough at the IntelliSys Conference in London in 2018. The approach is based on peer-reviewed scientific publications and practical research on human productivity, leadership, and team development.

Kesti believes that as organizations expand their use of AI in management, they should distinguish between tools that improve the smoothness of communication and tools that improve the actual quality and impact of leadership decisions.

“AI does not improve leadership by default,” Kesti said. “Its value depends on what it is designed to optimize. If it optimizes plausible responses, the result may still be weak leadership. If it optimizes effective actions in real team context, it can support sustainable productivity improvement.”

About Marko Kesti

Marko Kesti is a researcher in human productivity and a developer of reinforcement learning-based AI for leadership and team development. His work focuses on the connection between leadership practices, quality of work life, and sustainable productivity.

About Novogain

Novogain is an AI-based leadership development engine designed to support team productivity improvement through contextual analysis, simulation modeling, and reinforcement learning. The system is intended to help supervisors identify the most impactful development actions for their teams.