By Yael Grushka-Cockayne
The California bullet train. Lockheed Martin’s Joint Strike Fighter program. Berlin’s Brandenburg Airport. Inaccurate forecasts involving durations, costs, resources and benefits are clearly a major source of risk for leaders’ careers and organizations’ growth opportunities. Late or pricey projects can also affect the health of the economy at large.
Forty years ago, psychologist and Nobel prizewinner Daniel Kahneman, along with long-term collaborator Amos Tversky, noted that humans tend to suffer from a “planning fallacy:” They overpromise and underdeliver by offering unrealistic forecasts of projects’ objectives. Today, changing attitudes toward data collection, data-driven prediction and decision-making offer unprecedented opportunities in the field of project planning.
In the UK, the HM Treasury’s Green Book provides guidance on how project proposals should be appraised before significant public funds are committed. The appraisal procedure includes an explicit adjustment to account for systematic optimism, sometimes referred to as “optimism bias,” which is the overstatement of benefits and the understatement of durations and costs.
In a study I conducted for the UK’s Department for Transport, along with researchers from University College London, Erasmus University Rotterdam and Warwick Business School, we found that rail infrastructure projects require anywhere from a 64 percent optimism adjustment (for projects in early definition stages) to a 4 percent adjustment (for projects that have already completed detailed designs).
In the US, the Program Management Improvement Accountability Act was signed into law in 2016. The act, which aims to improve program and project management practices within the federal government, establishes initial guidance for coordinated and government-wide approaches to strengthen project management practices.
In both of these examples, the set of projects that are considered in the reference class is identified by human judgment. What if artificial intelligence could help perform this role?
Using detailed project-plan-level data with information on individual tasks, deep learning and artificial intelligence can identify patterns of similarity among project tasks, hierarchies and precedent relations. A London-based start-up, nPlan, is doing this now. The company uses data from tens of thousands of construction projects involving millions of tasks combined with natural language processing techniques to predict project durations and delays. The combination of rich data and proprietary AI capabilities allows nPlan to generate highly accurate and useful forecasts for project completion dates, including information about the risks of delay. Here, AI algorithms learn which patterns are most useful for predicting delays, relaxing the need to declare a reference class upfront.
Some suggest the availability of data and AI technologies will introduce a “seismic shift” in project planning. Let’s hope this shift will finally enable us to overcome the planning fallacy, too.
Yael Grushka-Cockayne is an associate professor at Harvard Business School.
