How Your Team Can Achieve Sustainable Test Growth: Balancing Speed, Cost, and Quality in the AI Era

The promise of AI-driven development is undeniable – faster code, quicker releases, and unprecedented innovation. But here’s the catch: AI isn’t perfect, and the code it generates could be riddled with hidden flaws. In fact, within three years, over a third of all code will be AI-generated, and much of it may introduce more bugs into production than ever before.

Digital transformation isn’t just a buzzword anymore – it’s a $3.9 trillion race to stay competitive, with 85% of organizations adopting cloud-first strategies.As release cycles accelerate and budgets tighten, how do you ensure quality doesn’t fall by the wayside?

For years, the rule of thumb has been “pick two – speed, cost, or quality.” Now, that luxury is gone. In this blog, we’ll dive into the growing pressure to balance all three, and why outdated testing processes could make or break your transformation efforts.

Testing Bottlenecks in the Era of Digital Transformation

Despite advancements in test automation, testing remains one of the biggest bottlenecks to digital transformation. Surprisingly, 80% of tests are still conducted manually across the industry. While automation promises greater efficiency, many test automation projects are started but never completed, and their ROI often falls short.

We recently surveyed  SmartBear customers who do not use automation tools. The three most common barriers to automation adoption were:

1. Lack of Time – Teams prioritize releasing the next version, leaving little time to develop automated tests. Automation efforts consistently lag, typically falling two sprints behind development.
2. Lack of Expertise – Automation tools often require technical skills that teams may not possess. Record-and-playback solutions have failed to meet expectations, leading many teams to abandon automation altogether.
3. Tool Overload – With hundreds of automation tools available, selecting the right one is overwhelming. Many teams revert to manual testing simply because it’s easier than navigating the complex tool landscape.

These challenges create friction and prevent teams from scaling their testing processes, slowing down release cycles and increasing the risk of bugs in production.

The High Cost of Delayed Bug Detection

The cost of bugs discovered in production far exceeds those caught earlier in development. A striking example is the recent CrowdStrike issue, which resulted in $5.4 billion in losses due to widespread system failures. The actual fix took only an hour and a half, but the repercussions were far-reaching.

On a broader scale, the numbers are staggering. Each year, 100 billion lines of code are added to software systems, with an estimated 25 bugs per thousand lines. This results in roughly 2.5 billion bugs leaking into production annually. The cost to fix these issues post-release is exponentially higher than addressing them during development.

Strategies for Sustainable Test Growth

To address these challenges, organizations must adopt a sustainable approach to testing – one that pushes defect detection earlier in the process (shift left) while improving monitoring and feedback in production environments (shift right).

Shift Left – Catching Bugs Early

The earlier a bug is found, the cheaper it is to fix. Shift left practices encourage testing earlier in the development lifecycle, reducing the risk of costly production issues. However, developers cannot be expected to take on all testing responsibilities. While developers are doing more testing than ever, end-to-end and UI testing require specialized skills. Overburdening developers with testing tasks detracts from their primary focus – writing application code.

Shift Right – Monitoring Production for Faster Feedback

By extending testing into production, teams can monitor for errors, track performance, and gather valuable insights to refine pre-production testing. Effective shift-right strategies rely on robust production monitoring systems that capture issues in real time and relay information back to development teams. This feedback loop ensures continuous improvement, reducing the cost and complexity of addressing bugs discovered in the field.

Tying It All Together

Combining these strategies creates a continuous quality loop that not only reduces the number of bugs slipping into production but also significantly lowers the cost of fixing them. By catching defects earlier and refining tests through production insights, businesses can avoid the ballooning costs associated with late-stage bug fixes. This holistic approach improves release velocity, enhances software reliability, and ultimately delivers a higher return on investment (ROI) by preventing revenue loss caused by critical failures.

Sustainable test growth isn’t just about preventing issues – it’s about driving long-term savings and maximizing the value of every development hour spent.

The SmartBear Approach to Testing

At SmartBear, we understand the delicate balance between speed, cost, and quality. Our holistic testing strategy focuses on continuous quality at every stage of development. By leveraging SmartBear API Hub, Test Hub, and Insight Hub, teams gain end-to-end visibility across the software development lifecycle, ensuring they can build, test, and release with confidence.

The Test Hub allows teams to manage, automate, and execute a variety of tests – from functional and UI tests to API and load tests – all within a single platform. This centralized approach streamlines workflows and reduces the overhead associated with managing multiple testing tools.

AI-Powered Enhancements for Modern Testing

SmartBear’s roadmap is filled with AI-driven features designed to accelerate test growth and simplify automation. Some of the latest innovations include:

• Natural Language-Based UI Test Automation – Convert manual tests into automated scripts for web and mobile apps using simple natural language prompts, reducing the need for technical expertise.
• Test Case Generation from Requirements – Instantly generate manual test cases directly from user stories and requirements, speeding up test creation and ensuring coverage aligns with business needs.
• Test Data Generation – Create synthetic test data on demand through contextual prompts, eliminating the delays associated with test environment setup.
• Visual Testing – Detect visual defects across web applications at scale, ensuring consistent performance across browsers and devices.
• Contract Test Generation – Produce contract tests directly from OpenAPI specs, client code, or HTTP request/response pairs, ensuring robust API coverage.

By embedding AI throughout the testing process, SmartBear empowers teams to automate faster, identify defects earlier, and minimize production risks without overburdening development teams. These AI-driven capabilities are already delivering tangible results for organizations:

• “Previously, locator-based plug-ins required painful updates as programs evolved. Zephyr Scale’s AI automation eliminates that issue, interpreting commands like ‘click on magnifying glass,’ cutting regression time from 90 to 20 minutes, improving consistency, increasing coverage, and saving time and money.” — Test Analyst at a Leading Automotive Services Provider
• “Adopting no-code automation cut our manual regression time by about 60%, allowing QA to focus on complex scenarios. Non-technical team members now create tests aligned with business goals, increasing coverage, enhancing collaboration, reducing post-release defects, and fostering greater ownership.” — Quality Assurance Analyst at a Global Software Company

Future-Proofing Software Quality in the AI Era

As AI continues to reshape the software development landscape, organizations stand at a critical crossroads. The potential for faster development is undeniable, but without the right testing strategies in place, the influx of AI-generated code could unravel hard-won gains. Sustainable test growth isn’t just a technical goal – it’s a business necessity for navigating the complexities of digital transformation.

Shifting left to catch bugs early, embedding robust production monitoring, and integrating AI-driven automation can help businesses break free from the outdated “pick two” mentality. The organizations that succeed in balancing speed, cost, and quality will lead the next wave of innovation. Those that don’t risk falling behind grappling with costly production bugs, delayed releases, and customer dissatisfaction.

SmartBear Hubs provide the framework to streamline testing across the entire development lifecycle, enabling teams to release with confidence, minimize risk, and scale at the pace digital transformation demands. But the time to act is now.

If you’re ready to stop firefighting production issues and start building a proactive, AI-empowered testing strategy, SmartBear can help. Get in touch today and discover how our end-to-end solutions can future-proof your development pipeline and deliver sustainable test growth.