InEight (online), September 24, 2025
News Summary
Construction projects often start ‘awash in optimism,’ but unchecked optimism bias can undermine budgets and schedules. The article recommends treating optimism as a managed risk by validating assumptions with detailed, real-time data, reference-class forecasting, benchmarking and performance metrics like earned value. Accurate quantity management and linking estimates, budgets and schedules to short-interval planning improve forecast reliability. Tracking change orders, RFIs, contract deliverables, timesheets and payments supports realistic assessments. Software platforms that centralize historical and as-built data enable better comparisons and timely adjustments. Bottom line: back optimism with data to protect outcomes and improve project performance.
Bias busting for construction project excellence
How optimism can hurt budgets and timelines — and what to do about it
4 min read | By InEight | 09/23/25
Categories: Infrastructure, Construction | Image credit: Vilkasss/Pixabay
Lead
Construction projects commonly launch “awash in optimism.” That optimism can become a project’s undoing when it becomes a source of unconsciously biased decisions (optimism bias). The most direct way to limit that damage is to check hopeful assumptions against detailed, real-time data, reference-class forecasting and performance metrics, and to treat optimism as a managed risk rather than an unexamined disposition.
Key points up front
Contractors are selected with care, roles are assigned, and goals are clear and incentivized. Even so, projects face optimism-bias blind spots that commonly include potential labor disputes, site-specific challenges, and regulatory complications. The prescribed remedy is simple in concept: optimism in construction requires realism. Biases of any kind can be problematic, especially when unacknowledged, and it is impossible to entirely remove bias. The first step to mitigation is to raise awareness; the second step is to challenge the bias. The easiest practical method is to check assumptions against measurable information.
Why data matters
The article frames managing optimism bias as less about changing personality and more about managing risk. The article asserts the best construction insights are data-driven, and that shared, real-time data forms the foundation for trusted performance monitoring. Diverse project stakeholders are more likely to make decisions based on insights when they have a data platform. The most relevant data for supporting or correcting optimism ties directly to either the project schedule or budget.
How to make the data useful
Detail and accuracy are necessary counterparts to the potential distortion of unwarranted optimism. Starting with highly detailed data results in more accurate analysis, forecasting and progress tracking. Accuracy starts with project setup and data collection practices. As one industry leader put it, fundamentally, if your quantities in scope are wrong, your forecast is inherently going to be wrong as well. That organization standardizes processes around controlled management of pure quantities, ensuring they are entered into the system at the right time and that everything else is a by-product of quantity management.
Forecasting controls and practices
Reference class forecasting is identified as a useful control mechanism. It involves comparing historical, similar project data with current project data to make forecasting and scheduling more accurate and to help if budgets or schedules must shift. Software systems that include benchmarking, quick access to historical and as-built data and native support for sophisticated work demands can help manage optimism bias. Advanced performance management practices that support realistic project assessments include earned value management and schedule performance index.
Another practitioner noted that as a general rule of thumb, 75 to 80% of the effort of forecasting really happens in the system, and then based on project knowledge, the team tweaks and fine-tunes by applying context. For example, if the remaining work is more complex than previous projects, teams will further scrutinize the previous forecast.
What to track
The article lists items helpful to track when addressing optimism bias: change orders, RFIs (requests for information), contract deliverables, and quantity claims. It also lists budgetary items to track: timesheets, payments and billings. A thorough assessment of data needs should be followed by a clear-eyed look at what it takes to collect accurate and detailed information.
One project services leader advises that teams must find ways to connect the estimate, the budget, and the schedule to short interval planning and controls effort, and to collect data as close as possible to the workplace, because that’s where the work gets done. Excellent data sets serve multiple downstream use cases, including integrated forecasting, ERT scheduling, managing contract life cycles, estimating financial impacts and collaborating among stakeholders.
Bottom line
The right data makes for great project outcomes. If optimistic budget assumptions are proven by the numbers, then data-supported optimism may be warranted. If optimistic assumptions are not supported by data, there may still be time to adjust the budget. Optimism may be warranted, but it never hurts to back it up with a quick check on the data.
Page and organizational notes
The article includes a commercial section labeled Construction By InEight and states that InEight is described as a leader in construction project controls software. According to the page, InEight empowers over 850 companies taking on challenging projects in industries including construction and engineering; transportation infrastructure; mining; water; power and renewables; and oil, gas and chemical. The page also states that the software is uniquely suited to capital construction and other complex work, manages projects worth over $1 trillion globally, takes control of project information management, costs, schedules, contracts, and construction operations, and delivers insights with advanced analytics and AI. The page states that its solutions adapt and scale to meet the dynamic needs of modern construction, driving operational excellence and successful project outcomes. For more information, follow InEight on LinkedIn or visit InEight.com.
Hashtags: #budget #construction #data #forecast #optimism bias #project management #schedule #timeline
FAQ
Q: What is the first step to mitigate bias?
A: First step to mitigate bias, according to the article: raising awareness of the bias.
Q: What is the second step to mitigate bias?
A: Second step to mitigate bias: challenging the bias.
Q: What is the easiest way to manage project bias?
A: The easiest way to manage project bias is to check it against measurable information.
Q: What data forms the foundation for trusted performance monitoring?
A: The article states shared, real-time data forms the foundation for trusted performance monitoring.
Q: What types of biases most likely affect projects?
A: The types of biases most likely to affect projects are organizational, leadership, group and human biases.
Q: What are some specific items helpful to track?
A: The article lists specific items helpful to track when addressing optimism bias: change orders, RFIs (requests for information), contract deliverables, and quantity claims. The article also lists budgetary items to track: timesheets, payments and billings.
Q: What is reference class forecasting?
A: Reference class forecasting involves comparing historical, similar project data with current project data to make forecasting and scheduling more accurate and to help if budgets or schedules must shift.
Q: What did the industry leader say about quantities and forecasts?
A: Fundamentally, if your quantities in scope are wrong, your forecast is inherently going to be wrong as well.
Q: What operational connection was recommended by a project services leader?
A: You have to find ways to connect your estimate, your budget, and your schedule to your short interval planning and controls effort. Collect your data as close as possible to the workplace, because that’s where the work gets done.
Q: What is the concluding practical statement?
A: Optimism may be warranted, but it never hurts to back it up with a quick check on the data.
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Key features table
| Feature | Why it matters | How it helps address optimism bias |
|---|---|---|
| Detailed, real-time data | Forms the foundation for trusted performance monitoring | Allows teams to verify optimistic assumptions against current facts |
| Reference class forecasting | Uses historical, similar project data | Makes forecasting and scheduling more accurate and helps when budgets or schedules must shift |
| Performance metrics | Earned value management; schedule performance index | Supports realistic project assessments and highlights deviation from plan |
| Benchmarking and as-built access | Quick access to historical and as-built data | Supports comparison and validation of current assumptions |
| Quantity management | Controlled management of pure quantities | Ensures forecasts are not built on incorrect scope quantities |
| Short interval planning connection | Links estimate, budget and schedule to day-to-day work | Keeps teams focused on required work and improves data collection at the workplace |
Additional page navigation items visible on the page include byline lines for other pieces such as By Sean McMahon with dates and categories: 10/31/24 InfrastructureRenewable Energy, 12/21/22 InfrastructureRenewable Energy, 12/18/22 FinanceModern Money, and 04/12/21 Renewable Energy.
Deeper Dive: News & Info About This Topic
Additional Resources
- InEight Blog
- Wikipedia: Optimism bias
- Engineering News-Record (ENR)
- Google Search: reference class forecasting construction
- Construction Dive
- Google Scholar: earned value management construction
- American Society of Civil Engineers (ASCE)
- Encyclopedia Britannica: forecasting
- McKinsey — Capital Projects & Infrastructure
- Google News: construction project data optimism bias
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