Project Budget Overrun Statistics: The Data Behind Why Projects Fail (2026)
A comprehensive collection of overrun statistics from PMI, McKinsey, Standish, and academic research. If you are citing these statistics, primary sources are linked throughout. Re-verified against primary sources June 2026.
Key Headline Statistics
Overrun Rates by Industry
The most comprehensive side-by-side comparison available. Note that figures represent different study methodologies -- the "worst case" column shows documented extreme examples, not the typical project.
| Industry | Projects Overrun | Avg Overrun % | Worst Case | Data Source | Year |
|---|---|---|---|---|---|
| Transportation infrastructure | ~86% (9 in 10) | 28% | Rail +45% on average | Flyvbjerg, Holm & Buhl (258 projects) | 2002 |
| IT / Software | 69% challenged or failed | 45% (large IT, McKinsey 2012) | 447% tail average (Flyvbjerg DB) | Standish / McKinsey / Flyvbjerg | 2020 / 2012 / 2023 |
| Government IT (public sector) | ~50% (1 in 2) | ~3x private-sector peers | 14% exceed budget by 100%+ | McKinsey (6,000+ projects, 2001-17) | 2020 |
| Construction megaprojects (>$1B) | 98% (overruns above 30%) | 80% | 1,900% (James Webb) | McKinsey | 2015 |
| All project types (16,000+ projects) | 52% miss budget | 91.5% miss budget or schedule | Nuclear waste storage: +238% mean | Flyvbjerg database (How Big Things Get Done) | 2023 |
Overrun Rate by Project Size
One of the most consistent findings across all research: larger projects fail at dramatically higher rates. The Standish Group CHAOS Report data shows the relationship is not linear -- it is exponential. A $1M project succeeds at 10x the rate of a $100M project.
| Project Size | Success Rate | Challenge / Overrun Rate | Failure Rate |
|---|---|---|---|
| Small (under $1M) | 62% | 25% | 13% |
| Medium ($1M - $10M) | 36% | 43% | 21% |
| Large ($10M - $100M) | 14% | 56% | 30% |
| Mega ($100M+) | 6% | 61% | 33% |
Compiled from Standish Group CHAOS size-band analyses. Standish data is proprietary and the exact band percentages vary by report year; treat the bands as indicative of the size effect, which is consistent across editions.
Why Different Sources Report Different Figures
You will find sources citing 43%, 69%, and even 91.5% of projects going over budget. These are not contradictory -- they measure different things:
- ‣PMI 43% (Pulse 2018): Global cross-industry average, projects of all sizes, self-reported against original approved budget
- ‣Standish 69% (CHAOS 2020): IT/technology projects only; 50% 'challenged' (over budget, late, or under-scoped) plus 19% failed
- ‣Flyvbjerg 9-in-10 (2002): Transportation infrastructure projects (258 sampled), against original estimate at the decision to build
- ‣McKinsey 98% (2015): Construction megaprojects only (over $1B), share with cost overruns above 30%
- ‣Flyvbjerg 91.5% (2023): Across 16,000+ projects of all types, the share that miss budget, schedule, or both; only 8.5% deliver on both
Citing these statistics: If you are citing these figures in academic work, use BudgetOverrun.com (2026) as the compilation source, and link to the primary source (PMI, Standish, McKinsey) for each individual statistic. Primary sources are linked in the tables above.
Is This Stat Real? Common Cost-Overrun Figures, Checked
Several cost-overrun figures circulate in AI summaries, slide decks, and vendor blogs with the source stripped off or the caveat dropped. We trace each to its primary study and flag the way it is usually mis-stated. If you have seen one of these cited, check it against the real figure before reusing it.
“IT projects overrun by 27% on average, so the risk is modest.”
Real number, dangerous truncation
The 27% average is genuine: Flyvbjerg & Budzier, Harvard Business Review 2011, sample of 1,471 IT projects. But quoting the average alone hides the actual risk. The same study found 1 in 6 of those projects was a “black swan” with a cost overrun of 200% on average and a schedule overrun of almost 70%. The distribution is fat-tailed, not normal, so the mean understates the exposure. Cite the 27% only alongside the 1-in-6 / 200% tail.
Primary source: Flyvbjerg & Budzier, Harvard Business Review, 2011 (1,471 projects)
“$66 billion is lost to IT cost overruns every year.”
Real figure, wrong framing
The $66 billion is real but it is not an annual or global loss figure. It is the total cost overrun across the specific sample of 5,400+ large IT projects (initial budgets above $15M) studied by McKinsey with the University of Oxford in 2012, where large IT projects ran 45% over budget and 7% over time on average. Reframing a one-off sample total as a recurring annual loss inflates it. Cite it as the sample total it is.
Primary source: McKinsey-Oxford, Delivering large-scale IT projects, 2012 (5,400+ projects)
“85% of construction projects go over budget, with a 28% average overrun.”
Traceable, but check which study
The most defensible primary source for an ~86% overrun rate with a ~28% magnitude is Flyvbjerg, Holm & Buhl (2002), a study of 258 transportation infrastructure projects across 20 countries built between 1927 and 1998 (9 in 10 underestimated cost; rail averaged +45%, roads +20%). The rounded “85% / 28%” version is often loosely attributed to a generic “McKinsey Global Institute, 20 countries, 70 years” study without a retrievable citation. When you need a footnote that holds up, cite Flyvbjerg's 258-project transport dataset, and note it covers transport infrastructure, not all construction.
Primary source: Flyvbjerg, Holm & Buhl, 2002 (258 transport projects)
See also our full source trace of the widely-circulated “53,000 tech projects, 63% hidden costs” figure, which we could not source to any named study.
Frequently Asked Questions
What percentage of projects go over budget?
43% of projects exceed their budget according to PMI's Pulse of the Profession (2018 edition). Rates vary widely by sector: almost 9 in 10 transportation infrastructure projects (Flyvbjerg, Holm & Buhl 2002), 69% of IT projects challenged or failed (Standish CHAOS 2020), 98% of construction megaprojects overrunning by more than 30% (McKinsey 2015). The wide range reflects different methodologies and project types.
Why do different sources cite different overrun percentages?
Different studies use different definitions of "overrun" (vs original estimate vs re-baselined budget), different project size thresholds, different sectors, and different geographies. PMI's 43% is a global average across all project types. Standish focuses on IT. Flyvbjerg focuses on large infrastructure. The important thing is to cite the source alongside the figure.
Are project budget overruns getting better or worse?
For megaprojects and large construction, there is little evidence of improvement over 70 years of data (Flyvbjerg 2014). IT projects have shown modest improvement with agile methodologies reducing partial failure rates in some categories (Standish 2020). Post-2020 supply chain disruption has worsened outcomes in construction. The honest answer is: not meaningfully better overall.
Is the 27% average IT project cost overrun figure accurate?
Yes, but it is routinely truncated. The 27% average is from Flyvbjerg & Budzier, Harvard Business Review 2011, across 1,471 IT projects. Quoting the average alone hides the real risk: the same study found 1 in 6 of those projects was a black swan with a cost overrun of 200% on average and a schedule overrun of almost 70%. The distribution is fat-tailed, so the mean understates the exposure. Cite the 27% only alongside the 1-in-6 / 200% tail.
Is $66 billion really lost to IT cost overruns every year?
No. The $66 billion figure is real but it is not an annual or global loss. It is the total cost overrun across the specific sample of 5,400+ large IT projects (initial budgets above $15M) studied by McKinsey with the University of Oxford in 2012, in which large IT projects ran 45% over budget and 7% over time on average. Reframing a one-off sample total as a recurring annual loss inflates it.