How AI Resolves a Major Problem in Managing Projects

 

Cognitive bias is the human tendency to reach decisions not based on logic. In project management, optimism bias is a significant problem when determining the accuracy of a project budget and schedule. Research by Nobel prize winner Daniel Kahneman challenges the belief that humans make rational economic decisions. He proposed the term planning fallacy to describe how we underestimate task completion time and cost regardless of available historical estimates for those tasks. Optimism bias is a significant and pervasive issue in project management.

Professor Bent Flyvbjerg expands on Kahneman's work, describing ten behavioral biases common to project managers. Common project estimating techniques include analogous, parametric, template, simulation, and function points. Despite the systematic methods and probability calculations, the ongoing evidence of budget overspending and schedule delays suggest that traditional methods are ineffective at eliminating optimism bias.

Academic studies reveal that machine learning algorithms are more accurate at forecasting project budgets and project duration. AI has the potential to eliminate or dramatically reduce the bias that human project managers include in the estimating process. Machine learning algorithms can develop a bias based on collected data, but the bias can be detected and removed. Human bias tends to be more ingrained. For project performance to improve, organizations must move beyond existing methods and adopt AI technology.   

For further insight, here are a few references.

Flyvbjerg, B. (2022). Top ten behavioral biases in project management: An overview. Project Management Journal 52(6). doi.org/10.1177/87569728211049046

Ghimire, P., Pokharel, S., Kim, K., & Barutha, P. (2023). Machine learning-based prediction models for budget forecast in capital construction, CEES 2023 2nd International Conference on Construction, Energy, Environment & Sustainability.

Kahneman, D., Rosenfield, A. M., Gandhi, L., & Blaser, T. (2016). Noise: How to overcome the high, hidden cost of inconsistent decision making. Harvard Business Review, 94(10), 38–46.

Min, A. (2023). Artificial intelligence and bias: Challenges, implications, and remedies. Journal of Social Research, 2(11), 3808–3817. https://doi.org/10.55324/josr.v2i11.1477