About This Project

Managers increasingly rely on AI tools to support decision-making, but it’s unclear when they trust, override, or adapt these recommendations. This project will conduct a systematic review to answer: How do managers use intuition and cognition when responding to AI-generated outputs? The findings will help us understand how managers use intuitive and cognitive reasoning to make real decisions—especially under uncertainty.

Ask the Scientists

What is the context of this research?

Artificial intelligence is changing how managers make decisions. From forecasting to strategy, AI tools now support complex business choices by offering data-driven insights. But decision-making is not purely analytical—especially in fast-moving, uncertain, or high-stakes environments. Managers often rely on intuition: rapid, experience-based judgment that isn’t easily replaced by algorithms. While we know AI helps with analysis, we know far less about how human judgment interacts with AI outputs. Do managers follow AI advice? Do they override it? What happens when instinct and data conflict? Despite the rise of AI, these human elements are underexplored. This project investigates that gap by focusing on the role of intuition and cognition in managerial responses to AI, asking when and how these mental processes shape real-world decisions.

What is the significance of this project?

Understanding how managers engage with AI isn’t just an academic question—it’s a real-world challenge. Poor, biased decisions can occur when people blindly follow AI advice, or when they reject valuable input due to distrust. Striking the right balance between trusting AI and applying human judgment is critical, especially in complex or uncertain situations. Yet, current research largely treats AI as an enhancement to logic and rationality, ignoring how managers actually think—often intuitively. This project will offer the first comprehensive synthesis of how intuition and cognition interact with AI recommendations. The findings will support better training for leaders, improve the design of AI decision-support systems, and reduce the risks of automation bias or human overconfidence. In short, it’s about making human-AI decision-making smarter, safer, and more aligned with how decisions really get made.

What are the goals of the project?

The goal of this project is to build a research-based framework that maps how managers engage with AI recommendations—specifically, how they switch between or combine intuitive and analytical thinking in decision-making. I will conduct a systematic literature review (SLR) of peer-reviewed studies published in the last 10 years. These may be empirical, conceptual, or theoretical papers related to managerial decision-making, AI-supported systems, intuition, and cognition.

All studies will be managed using Zotero for reference management and Covidence for screening and data extraction. I will extract data on decision context, use of AI, reliance on intuition or cognition, and reported outcomes. Studies will be appraised for quality using tailored criteria for quantitative, qualitative, and conceptual work. This framework will help you understand when to trust, override, or adapt AI outputs, supporting more confident and context-aware decision-making.