Why Government Projects Fail: A Behavioral Analysis
It is a well-known fact that governments often fail to deliver the things they set out to achieve. New schemes, programs, and projects are often rolled out over time or over budget—or just don’t quite live up to their original promise.
As the directors at a specialist government advisory firm called PUBLIC, we’ve witnessed these shortcomings firsthand. PUBLIC works with the UK and European governments to deliver large, complex digital, data, and technology projects. From our experience, we know that things aren’t always as smooth as originally anticipated. The real question is: why?
In this article, we’ll look at some of the common failures in government projects through a behavioral lens. We believe that cognitive biases, habits, and heuristics are often the root of the problem and that by being more aware of them, governments can get better at fulfilling their promises. We’ll investigate seven examples of project biases, and offer strategies to overcome them—drawing inspiration from Kahneman’s recommended “decision hygiene practices” to keep biases in check.
As digital practitioners, we’ll focus mainly on digital and technology projects, but we believe our principles are true for any kind of project—including infrastructure, housing, healthcare, or anything that requires careful planning and even more careful roll-out.
About the Authors
Johnny Hugill
Johnny Hugill is a Director at PUBLIC, where he supports public authorities to deliver and evaluate major projects. He has been a member of a number of UK Government advisory boards, including for public procurement. He has previously written for The Decision Lab and other behavioral science outlets about the intersection of behavioral science and government.
Richard Llewellyn
Richard Llewellyn is a Director at PUBLIC, leading its Digital, Data, and Technology projects with public authorities. He joined PUBLIC after leading product development at a HealthTech startup called Eva Health, having previously led product development at a range of tech scale-ups.
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