According to groundbreaking research by Assistant Professor Abhijith Anand and his co-authors, Magno Queiroz (Florida Atlantic University) and Aaron Baird (Georgia State University), it all comes down to how managers relate to time and risk.
Anand's research reveals a crucial insight that could transform how businesses approach AI adoption: successful implementation isn't just about choosing the best technology—it's about matching the right AI system to the right manager's decision-making style.
Working with colleagues at Florida Atlantic University and Georgia State University, the research team identified three distinct managerial approaches to AI investment decisions. Future-focused managers embrace experimentation and innovation, always looking ahead to what's possible. Past-oriented managers prefer established protocols and proven methods, drawing on experience and tradition. Present-focused managers concentrate on immediate business needs and current operational demands.
"AI systems cannot be treated as a 'black box,'" Anand explains. "Managers must understand the specific purposes and capabilities of AI before making investment decisions. Thoughtful consideration of how AI fits within organizational processes is critical."
The research shows that future-oriented managers thrive with advanced AI systems that can learn autonomously and adapt based on incoming data. These systems might augment human decision-making or even automate entire processes. For these managers, the potential for innovation outweighs the risks of giving up some control.
However, risk-averse managers—those who prefer proven approaches—find predictable AI systems more beneficial. They want to maintain oversight and control over AI-based decisions rather than delegating authority to systems that might seem unpredictable.
This research addresses a critical challenge facing businesses today: why some AI investments succeed while others fail. The answer often lies not in the technology itself, but in the mismatch between the system and the manager's comfort level with delegation and risk.
When managers invest in AI that aligns with their decision-making style, they're more likely to use it effectively and see positive returns. When there's a mismatch, even sophisticated AI systems can become expensive mistakes.
The work of the research team, which was published in the prestigious Journal of Management Information Systems, provides organizations with a practical framework for making smarter AI investments. Rather than adopting a one-size-fits-all approach, companies can now tailor their AI strategies to their leadership's strengths and preferences.
This research positions the University of Arkansas at the forefront of understanding how human psychology intersects with artificial intelligence—a critical area as AI becomes increasingly central to business operations. By solving real-world problems through rigorous academic research, Anand's work demonstrates the practical value of university research in shaping the future of business.
The implications extend far beyond individual companies. As AI continues to transform industries, understanding how to match technology with human decision-makers will become increasingly valuable for organizations seeking competitive advantage through intelligent automation. ¬