Why Most Software Projects Run Over Budget — And How to Not Be One of Them

Custom software has a reputation problem, and the data backs it up. According to the long-running Standish Group CHAOS research, only around 31% of software projects succeed on time, on budget, and with full scope, while roughly 19% fail outright and the remainder are "challenged" — late, over budget, or missing features. The picture gets worse with scale: small projects succeed at close to a 90% rate, while large projects succeed less than 10% of the time.

The budget numbers are equally sobering. The median cost overrun on large IT projects sits at around 45% above the original estimate, and for very large projects — those over roughly $15 million — average overruns exceed 66%. A smaller but still meaningful share of projects become what researchers call "black swans" — around one in six IT projects overruns its budget by 200% on average, with the most extreme documented case reaching nearly 700%.

What's genuinely useful in this research isn't the failure rate itself — it's the "why." The most commonly cited causes of software project failure are unclear requirements, scope creep, inadequate planning, and communication breakdowns — not, in most cases, the technology itself. That finding matches almost exactly what we see across our own engagements: the projects that go sideways are rarely undone by a hard technical problem. They're undone by a requirement nobody pinned down at the start, or a change nobody flagged before it hit the codebase.

It's also worth noting that agile delivery approaches consistently overrun less than traditional waterfall methods, across every project size studied — which is precisely why every Via Codos engagement is scoped and delivered in two-week sprints with a working demo at the end of each one, rather than a single, high-risk delivery date six months out. You don't have to accept the industry's failure rate as the price of building custom software. You just have to work with a partner whose process is actually built to avoid the specific mistakes that research keeps finding.