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Software Failures Cost the Enterprise Software Market $61 Billion Annually

While the adoption of continuous integration (CI) is on the rise, software engineering teams are unable to take a zero-tolerance approach to software failures, costing enterprise organizations billions annually, according to a quantitative study conducted by Undo and a Cambridge Judge Business School MBA project.

"Every company is a software company. The ability for engineering teams to deliver high quality software at velocity is the difference between companies that gain a competitive edge versus those that fall behind," said Undo CEO Barry Morris. "The next phase of CI will be about making defect resolution bounded, efficient and less skills-dependent. Organizations that evolve with CI will be able to resolve bugs faster, accelerate software delivery and reduce engineering costs."

The research concluded three key findings:

1. Adoption of CI best practices is on the rise

88% of enterprise software companies say they have adopted CI practices, compared to 70% in 2015.

More than 50% of businesses surveyed report deploying new code changes & updates at least daily, with 35% reporting hourly deployments

2. Reproducing software failures is impeding delivery speed

41% of respondents say getting the bug to reproduce is the biggest barrier to finding and fixing bugs faster; and 56% say they could release software 1-2 days faster if reproducing failures wasn’t an issue.

Software engineers spend an average of 13 hours to fix a single software failure in their backlog.

3. Failing tests cost the enterprise software market $61 billion annually

This equals 620 million developer hours a year wasted on debugging software failures.

Although CI adoption is becoming ubiquitous, test suites are still plagued by a growing backlog of failing tests. Failures in integration and automated tests cause bottlenecks in the development pipeline, and substantially increase engineering costs.

The study further suggests that reproducibility of failures is also a major blocker, finding that not being able to reproduce issues slows engineering teams down and prevents them from releasing software changes at pace.

To fully realize the benefits of CI, software failure replay offers a way out by enabling engineering teams to reproduce and fix software bugs faster. By eliminating the guesswork in defect diagnosis, development teams are able to accelerate Mean-Time-to-Resolution (MTTR) — resulting in considerable cost savings.

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Software Failures Cost the Enterprise Software Market $61 Billion Annually

While the adoption of continuous integration (CI) is on the rise, software engineering teams are unable to take a zero-tolerance approach to software failures, costing enterprise organizations billions annually, according to a quantitative study conducted by Undo and a Cambridge Judge Business School MBA project.

"Every company is a software company. The ability for engineering teams to deliver high quality software at velocity is the difference between companies that gain a competitive edge versus those that fall behind," said Undo CEO Barry Morris. "The next phase of CI will be about making defect resolution bounded, efficient and less skills-dependent. Organizations that evolve with CI will be able to resolve bugs faster, accelerate software delivery and reduce engineering costs."

The research concluded three key findings:

1. Adoption of CI best practices is on the rise

88% of enterprise software companies say they have adopted CI practices, compared to 70% in 2015.

More than 50% of businesses surveyed report deploying new code changes & updates at least daily, with 35% reporting hourly deployments

2. Reproducing software failures is impeding delivery speed

41% of respondents say getting the bug to reproduce is the biggest barrier to finding and fixing bugs faster; and 56% say they could release software 1-2 days faster if reproducing failures wasn’t an issue.

Software engineers spend an average of 13 hours to fix a single software failure in their backlog.

3. Failing tests cost the enterprise software market $61 billion annually

This equals 620 million developer hours a year wasted on debugging software failures.

Although CI adoption is becoming ubiquitous, test suites are still plagued by a growing backlog of failing tests. Failures in integration and automated tests cause bottlenecks in the development pipeline, and substantially increase engineering costs.

The study further suggests that reproducibility of failures is also a major blocker, finding that not being able to reproduce issues slows engineering teams down and prevents them from releasing software changes at pace.

To fully realize the benefits of CI, software failure replay offers a way out by enabling engineering teams to reproduce and fix software bugs faster. By eliminating the guesswork in defect diagnosis, development teams are able to accelerate Mean-Time-to-Resolution (MTTR) — resulting in considerable cost savings.

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...