A recent study conducted by researchers from three universities provides a fresh perspective on how managers can effectively incorporate artificial intelligence (AI) into their workflows. The findings explore how managerial attitudes toward risk influence decisions about adopting and delegating tasks to AI systems, offering practical guidance tailored to different management styles.
Magno Queiroz, associate professor of information systems at Florida Atlantic University’s College of Business and the study’s lead author, emphasizes the importance of embracing a shift in decision-making dynamics. “Managers must be willing to relinquish some level of autonomy, as AI systems play an increasingly prominent role in workflow processes,” Queiroz explains. “The extent to which a manager is comfortable delegating tasks to AI has a direct impact on the type of AI systems they choose to implement and the business value those systems generate.”
Published in the Journal of Management Information Systems, the paper, titled “Manager Appraisal of Artificial Intelligence Investments,” was co-authored by Abhijith Anand from the University of Arkansas’s Sam M. Walton College of Business and Aaron Baird from Georgia State University’s Robinson College of Business.
Tailored AI Investment Strategies for Different Managerial Styles
The research categorizes managers into three distinct groups based on their approach to decision-making: projective, iterative, and practical evaluative. Each style shapes how managers assess and invest in AI technologies:
- Projective Managers: These forward-thinking leaders prioritize innovation and experimentation. They are more inclined to adopt cutting-edge AI systems that emphasize autonomous learning and adaptability. For such managers, AI tools capable of both augmenting human decision-making and automating tasks entirely are highly appealing.
- Iterative Managers: Focused on established routines and past successes, iterative managers gravitate toward AI systems that align with proven processes. Their preference lies in predictable technologies that enhance efficiency while maintaining human oversight.
- Practical Evaluative Managers: Positioned between the two extremes, these managers balance immediate business needs with a pragmatic approach to AI adoption. They consider both the risks and opportunities associated with different AI systems, aiming to address current challenges without compromising future flexibility.
Abhijith Anand underscores the importance of understanding AI’s inner workings before making investment decisions. “AI cannot be treated as a mysterious ‘black box,’” Anand asserts. “Managers need to grasp the underlying algorithms and functionalities, including the system’s potential for automation, augmentation, and decision-making autonomy. This thoughtful approach ensures that AI investments are aligned with organizational goals.”
Risk Appetite and Delegation Preferences Shape AI Choices
The study reveals that a manager’s comfort level with risk and delegation plays a pivotal role in determining their approach to AI adoption. Risk-averse managers, for example, may hesitate to invest in advanced AI systems capable of making independent decisions. Instead, they are likely to favor more predictable tools that allow them to retain control over critical processes.
In contrast, managers who are open to taking risks are better positioned to explore innovative AI technologies. These systems, which can autonomously learn and adapt, offer significant potential for driving business innovation but require managers to cede greater control to the technology.
“The choice of AI system reflects a balance between delegation preferences, control, and value creation goals,” Queiroz notes. “For some managers, the idea of handing over significant decision-making authority to an AI system may feel daunting, particularly if they perceive it as a potential threat to their own role.”
Implications for Organizations
By providing a theoretical framework to guide AI investment decisions, the study equips organizations with valuable tools to navigate the complexities of integrating AI into their operations. The findings highlight the importance of aligning AI adoption strategies with managerial styles to maximize business value while mitigating risks.
As AI continues to transform industries, understanding the nuanced dynamics between management styles and technology adoption will be critical for organizations striving to remain competitive. The research underscores the need for thoughtful planning and a clear understanding of how AI systems fit within broader organizational processes.