The Planning Fallacy was first introduced by psychologists Daniel Kahneman and Amos Tversky in 1979. Their research revealed a consistent human tendency: when predicting the duration of future tasks, individuals often imagine a best-case scenario rather than relying on realistic averages from prior experiences. Even experts and professionals who have managed similar projects tend to fall into this trap. This demonstrates how deeply ingrained the bias is.

One of the most well-known case studies of this bias is the Sydney Opera House project. Originally estimated to take about four years and cost $7 million AUD, the project ballooned into a 14-year construction project costing more than $100 million AUD. Despite clear evidence of delays and rising costs, decision-makers repeatedly relied on overly optimistic projections. This famous example highlights how optimism, group pressure, and political motivations can collectively reinforce the Planning Fallacy on a large scale.

More studies have shown that we don’t fall for the Planning Fallacy simply because we’re overly optimistic. We often fall for the planning fallacy because we don’t consider “outside views.” Kahneman later stressed the importance of what he called reference-class forecasting, which means comparing current projects to historical data from similar reference classes. And yet, even with an awareness of the bias, we tend to continue to fall for it. And that’s often because focusing on potential setbacks feels discouraging or less motivating.

For teams, the Planning Fallacy can have serious consequences.

At a leadership level, the Planning Fallacy can warp strategic decision-making. Executives may greenlight initiatives with aggressive schedules to please the board or investors, knowing deep down that the estimates are entirely unrealistic. Over time, this erodes morale within teams, who end up seeing leadership and their timelines as detached from reality.

Product teams may agree to leadership’s unrealistic goals, thinking it’s possible. Engineers may underestimate complexity, and designers may believe they can finish more research and design iterations than time allows. These overly optimistic estimates not only create stress and burnout, but they can also damage credibility with important stakeholders and customers when deadlines are missed.

For agile teams, the Planning Fallacy often shows up in sprint planning. Teams may load their backlog with more work than they can realistically accomplish, because they’re under pressure to show progress. While the intention is positive—we think it demonstrates ambition and efficiency—the result is often incomplete tasks, stories rolling over to the next sprint, and growing frustration. This ultimately undermines trust in the planning process itself.

The bias also affects cross-functional collaboration. For example, engineers might promise delivery based on their portion of the work, forgetting to account for QA, acceptance testing, or unforeseen integration and deployment hurdles. Marketing teams might plan product launches without considering the inevitable technical delays. When each function underestimates in isolation, the collective outcome becomes even more unrealistic.