By rethinking how quality is owned, measured, and enabled, companies can build systems that support both speed and resilience in software delivery.
Developers are under mounting pressure to deliver software faster than ever, oftentimes without the resources, support, or time needed to ensure quality. This urgency to deploy can have serious consequences for organizations. According to a recent global survey of 2,750 CIOs, CTOs, developers, and quality assurance (QA) leaders and professionals, 63% of organizations admit to releasing untested code, and 66% say they’re at risk of a software outage within the year. To course correct, leaders must re-evaluate their approach to quality and ensure all employees have access to the proper resources and a clear roadmap to success.
Quality engineering practices should be embedded not just early in the software development lifecycle, but at all stages – testing should never be seen as a “final hurdle.” Making this shift requires robust organization-wide quality metrics, cross-functional feedback loops, a shared responsibility model, and AI-driven approaches. By investing in these areas, teams won’t just move faster; they’ll deliver higher-quality and resilient software. To put these strategies into action and close the quality gap, software development teams must embrace quality that can be owned, measured, and enabled across the organization.
Fostering effective communication
A major barrier to software quality is miscommunication among teams. According to the same report, 28% of respondents note that there is an active disconnect on organizational priorities between leadership and their software delivery teams, causing a major hurdle to their software delivery. Furthermore, 33% point to poor communication between development and QA.
These communication breakdowns are more than just operational inefficiencies; they have a direct and measurable impact on software quality. Nearly one-third of software development teams are under pressure to release code at increased speed, adding urgency to the challenge. Realistic timelines and open dialogue between developers and their leadership can lead to better code and better understanding across the organization. But communication is only the first layer; teams need unified metrics to measure quality across the organization, quantify success, and generate the data needed to drive better decisions. Uniting the team in deciding on what those metrics are is another way to break down communication barriers.
See also: Rethinking DevOps: What’s Next for Software Teams After a Decade of Stagnation
Establishing organization-wide metrics
A lack of clear quality metrics continues to hold teams back, with 29% of software development teams finding this to be a key obstacle in the delivery process. Without a shared understanding of what is considered as “success” across the organization, software development teams are left uncertain and reactive. Establishing organization-wide metrics not only creates a common language to keep developers, QA, and leadership aligned, but it can also uncover gaps across tools and processes.
To implement this, organizations should define a small set of core quality metrics that are tracked consistently across teams and tied to outcomes that matter to both users and the business. The objective is to track the health of software holistically, beyond just its technical performance.
Too often, organizations become hyper-focused on siloed, output-oriented key performance indicators (KPIs) such as deployment speed and frequency or test coverage. While these metrics are certainly useful in measuring things like team efficiency, they don’t necessarily reflect larger business outcomes – like customer satisfaction and retention, for example. An organization’s chosen set of metrics should include outcome-oriented metrics, such as availability/uptime and reduced defect rates.
When tied to both internal performance and larger business outcomes, defined sets of quality metrics reveal the true impact of quality on the organization. This shift empowers teams to see the bigger picture and focus on insights that can guide strategic decisions. Once metric clarity is established, the next step is to ensure teams are equipped to act on KPI reports quickly and collaboratively.
See also: A Key Factor Your DevOps is Missing: Process Discovery
The importance of cross-functional feedback loops
Real-time, cross-functional feedback is essential to turn shared insight into action. One-third of survey participants cite poor feedback loops between developers and QA as their biggest hurdle to quality, leading to delays in issue detection and increased rework. Successful teams create feedback channels that connect development, testing, operations, and even customers, enabling faster resolution and shared ownership.
The first priority should be speed. Developers often lose valuable context when feedback arrives days or even weeks after the work was completed, making it harder to identify what code changes need to be made. Timely responses preserve that context, accelerate fixes, and reduce the risk of compounding issues.
Consistency is equally important. Introducing recurring reviews that involve representatives from every function will ensure issues are uncovered and addressed before they compound. By integrating continuous feedback into workflows, teams can surface problems earlier and respond more effectively. These loops also build empathy by helping teammates understand each other’s challenges and perspectives, and therefore reinforcing alignment and driving iterative improvement.
However, metrics and feedback alone are not enough without the right culture. Every team must be empowered to take responsibility for its role in delivering quality.
Developing a shared responsibility model
To sustain quality, organizations must treat it as a collective commitment. A shared responsibility model ensures that every team is accountable for software quality at every stage of development, not just during testing. This involves clearly defining ownership for each phase of the software lifecycle and embedding quality considerations into daily decision-making.
Building this model starts with defining roles, setting cross-functional quality objectives, and ensuring all teams are part of quality reviews and planning. This approach enables teams to catch issues earlier and reduces finger-pointing when something goes wrong. It also encourages proactive problem-solving and clearer accountability across departments. With this foundation in place, teams are better prepared to leverage tools and technologies that can reinforce and scale their quality strategy. Among these, AI stands out as a catalyst for smarter, faster, and more resilient delivery.
Utilizing AI as a quality enabler
AI is a powerful tool for improving software quality at scale. Four in 5 IT leaders and professionals believe that AI will help software development teams deliver high-quality software faster, and eighty-four percent are confident that AI will help teams deliver under increasingly tight deadlines. But the most effective organizations are not using AI just to move faster. They are also applying it to enhance test coverage, improve precision, and prioritize testing based on risk.
AI makes it easier to scale quality practices across environments and teams as a true collaborator alongside human testers. While it is not a silver bullet, when combined with clear metrics, shared ownership, and strong communication, AI becomes a force multiplier for software quality.
Building a strong foundation for software quality
Teams will always face pressure to move fast, but quality cannot be an afterthought. Organizations that invest in foundational improvements, aligned metrics, open feedback loops, shared accountability, and the smart use of AI are better equipped to deliver reliable software at scale and meet their critical business objectives. These practices reinforce one another, helping teams navigate complexity without compromising standards. By rethinking how quality is owned, measured, and enabled, companies can build systems that support both speed and resilience in software delivery.
