Are We Overly Optimistic In Our Early-Stage Estimates?

The effects of optimism on project estimates are hard to measure. We can recognize how likely it is that sentiment would affect a project but struggle to record and quantify that impact. A leaked study from a large energy player highlighted the results of a recent internal review comparing early-stage estimates to actuals: in an audit of 110 projects, they found massive overspend compared to early-stage estimates. They attributed two causes to the discrepancy: one, the complexity of large-scale developments making it difficult to form early-stage estimates and two, “human biases that resulted in overoptimistic plans designed to win approval.”  

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When a team is bullish about the prospect or outcome of a project, they may make overly optimistic estimates about the cost, schedule, and resources required for the project. This can lead to underestimating the true costs and time required for the project, which can result in cost overruns and delays. These effects show up in multiple ways, including:

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1. ‍Imprecise  cost budgets: Optimism can lead to inaccurate estimates, which can result in unrealistic budgets and schedules that are difficult to achieve.

1. Limited contingency planning: Optimism can lead to incomplete, uncritical contingency planning, which can make the project team unprepared for unexpected challenges or delays.
2. Unrealistic expectations: Optimism can create misaligned expectations between the project team and project owners, which can lead to disappointment and dissatisfaction.
3. Confirmation bias: Contributors may be more likely to rely on information that confirms their existing beliefs about the project, rather than objectively evaluating all available information.

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It's important for project teams to maintain a balance between optimism and realism when making estimates. This can be achieved with access to historical project data that allows teams to objectively compare past actuals to budgets to refine their processes. Additionally, access to clean, structured historical data allows a company to benchmark past similar projects to create quick conceptual estimates and to flag discrepancies between current plans compared to the benchmark.

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