No industry is immune to the need for high-quality software. Recently, automaker Ford recalled more than 355,000 trucks due to an instrument panel display issue; a flaw that risked hiding critical information like speed and, in turn, increasing the likelihood of car crashes. While not every software failure has such dramatic consequences, many organizations are feeling the squeeze of poor quality. In fact, over two-thirds (66%) say they’re at risk of a software outage within the year, with 40% of technology leaders and professionals saying poor quality costs them over $1 million annually.
Overly rushed or poorly tested releases can lead to increased failures – as seen with Ford – leading to costly downtime and user frustration. Software quality often slips not because of major flaws, but because of small cracks in the software development lifecycle (SDLC). Weak feedback loops, unclear metrics, and manual bottlenecks can create lasting damage.
About a third of software development teams say poor developer–quality assurance (QA) communication is a major barrier to their software quality, while over a quarter (29%) cite the lack of clear quality metrics. Left unresolved, these challenges embed themselves into organizations, eroding software quality at its core. Software failures aren’t just caused by code, but by culture, which is why stronger, shared testing practices are essential to keep them in check.
Root failures in software testing practices
Unfortunately, communication breakdowns between developer and QA teams are common, and when feedback does arrive, it is often inconsistent or unclear. These weak feedback loops can lead to long clarification cycles, or worse, fragmented testing efforts with duplicated work and rework. While all of these can slow down issue detection, broken feedback loops are only part of the problem.
Oftentimes, different stakeholders define quality in conflicting ways. It’s common for less technical stakeholders to focus on metrics that emphasize speed, for example, while development teams may choose to focus on critical quality indicators like defect rates and user experience to judge their success. Without agreed upon business-wide quality metrics, teams lack clear direction on how to allocate their time and resources most effectively. Such misalignment makes it difficult to allocate testing resources effectively and concentrate on the areas that matter most for the business.
Once teams are aligned on what to measure, execution can often falter. Reliance on manual, ad hoc testing creates inconsistency across teams and makes it nearly impossible to scale effectively. Without standardized processes or automation, results vary from one cycle to the next, slowing delivery and increasing the risk of missed defects. Over time, this lack of structure prevents organizations from achieving the speed, efficiency, and reliability needed in modern software development.
Building a stronger testing process
To set organizations up for success, software quality should be treated as a collective duty, not left to one team or a single phase of development. Instituting a shared responsibility model makes every group accountable for quality at each stage of the SDLC, from design all the way through delivery. This requires clearly defining team roles, setting cross-functional objectives, and ensuring all teams actively participate in reviews and planning.
This shared ownership can be reinforced by instituting a common language for measuring performance. Developing a concise set of key performance indicators (KPIs) can help reveal wins and highlight areas for improvement. Pairing this with recurring cross-functional reviews, which draw in internal teams and even customers, can help surface problems earlier. With timely feedback loops, context is preserved for developers, accelerating fixes and preventing small issues from snowballing. Formalizing these mechanisms allows feedback to become part of the workflow itself, reinforcing accountability and helping teams build empathy for one another’s challenges.
Crucially, the KPIs must extend beyond output-oriented measures like release speed to include outcomes tied to user experience and business goals. When consistently applied, unified metrics can help guide insight-driven decisions and turn quality into a strategic lever.
Reinforcement and scaling
Once these foundational practices are in place, organizations can take the next step by layering in automation and advanced tooling. These capabilities reinforce process discipline, reduce variability, and strengthen consistency across teams. Among the most impactful tools is AI, which can scale quality practices beyond what manual approaches can achieve, helping software development teams move faster without sacrificing reliability. It can act as an accelerator and help maintain high standards even as systems grow more complex.
However, the true benefits of AI will only be realized if process gaps are addressed first. Without a solid structure, automation risks amplifying existing inefficiencies and increasing technical debt. By tackling these core issues upfront, businesses can ensure that AI becomes the next driver of smarter, more resilient delivery, for years to come.
