Skip to main content

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

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

APMdigest's Predictions Series continues with 2026 Data Center Predictions — industry experts offer predictions on how data centers will evolve and impact business in 2026 ...

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms ...

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

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

APMdigest's Predictions Series continues with 2026 Data Center Predictions — industry experts offer predictions on how data centers will evolve and impact business in 2026 ...

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms ...