AI In Test Automation: From Costs To Benefits
Today, software depends on end customers much more than it did a couple of years ago. With so many options at hand, users won’t hesitate to switch vendors as soon as they are irritated by system defects. According to PwC, 34% of users would leave a brand if they had a bad experience with the product, 26% if there’s an efficiency drop, and 12% would switch simply because a different company provides a better experience.
This puts teams that don’t invest in software testing in an unfavorable position compared to those that do. Slow platform growth, negative ROI, damaged reputation, and reduced opportunities for expansion are among the top consequences of lack of quality assurance. Businesses that treat software testing as an unnecessary expense commonly get stuck in a limbo state with a churn rate too high to cover it with a steady flow of incoming customers.
Why does this happen? Mainly because of the obsolete bias towards QA. Building software is often a developer-centric process, with all resources concentrated on faster deployment. Teams entrust high-level testing to developers, while the rest is expected to be reported by the users. This approach is seen as a source of additional ROI through resource cutting. However, though professional software testing seems like an unnecessary expense and waste of time for many, it’s actually not.
Test Automation: Pros over Cons
Present QA practices integrate quite well into agile development, causing little to no setbacks. One of the best ways to ensure extensive coverage while maintaining rapid release cycles is to implement test automation. “Manual testing already seems to be a budget eater, test automation would be a devourer”. But software QA, especially automated testing, requires a bit more long-term thinking for all benefits to become visible. Let’s crunch some numbers:
“We don’t need software testing, post-launch fixes are good enough“
The earlier you identify the defect, the less of your budget will be spent on fixing it. According to NIST and IBM, the cost of defect resolution can go up by 30 to 100 times if it’s identified at the maintenance stage rather than earlier in development. You can learn more in this blog post by Functionize. With automation, bugs can be found at the earliest stages of the pipeline.
“We have our developers testing their own work“
While this approach implies efficiency, as developers know their code and can detect issues early in the process, it has limitations that lead to bugs reaching production. Having QA engineers on the team fosters more comprehensive coverage, with the platform being tested as a whole and under unique conditions. Automation is a perfect tool to maximize the QA impact. This way, projects will be protected from blinder effects caused by a bias towards one’s own effort.
“Test automation is too expensive for us, we’ll stick with manual testing“
Even though automated testing is more expensive than manual initially, it becomes highly advantageous over time. Below, you can find the comparison chart that demonstrates the typical budget expense over development cycles. Test automation has a positive effect on ROI over time, as it allows for minimizing the resources involved in repetitive (regression) or more intensive (load) testing.
However, test automation indeed requires certain preparation and expertise to be truly impactful. It should be leveraged for projects that already have a stable core and at least some level of maintenance. As soon as your platform has consistent functionality and is intended for lasting support, check out our brief questionnaire to decide whether it’s time for automation.
AI-Powered Testing: Beyond Efficiency
Recent advancements in AI allow adding extra layers of efficiency to test automation. Adaptive learning, data analysis, and decision-making turn AI into a crucial element for more flexible automation. AI-powered testing can handle dynamic test environments, adapt to application changes, and even analyze code to adjust scripts. Properly implemented AI is beneficial for the software testing lifecycle, with faster execution time, smarter maintenance, and deeper insights.
According to the World Quality Report 2024-2025 by Capgemini, nearly 68% of companies are actively using generative AI to improve decision-making and streamline QA processes. The reason is simple – businesses can achieve significant cost optimization by reducing manual efforts, minimizing errors, and accelerating time to market.
AI-powered testing introduces capabilities for seamless test script creation, more intelligent execution, and less human-driven maintenance, freeing up resources for strategic tasks. Test automation platforms that added AI to their functionality report their users managed to reduce test cycle times by up to 60%, speeding up the SDLC.
Along with faster deployment and more seamless QA processes, AI-powered testing is highly productive when it comes to coverage, reducing the number of bugs that reach production. Better software quality converts into enhanced customer satisfaction and less funds spent on bug fixes. As we have already covered, post-release debugging can be as much as 100 times more costly compared to early detection, which highlights the impact of AI on project performance.
The combination of test automation and AI has huge potential, enabling faster test runs with broader coverage. All the benefits combined lead to a significant boost in ROI and month-over-month profitability growth. But despite all the benefits, automated testing is a tough nut to crack, which is why many teams fail to convert it into revenue.
Why Test Automation Flops?
Thorough planning and proper execution are the factors that have the most significant impact on test automation outcomes. It’s rather common for QA teams to underestimate the capabilities of automated testing, thus not fully embracing the potential. It’s recommended to consider the following factors:
- Pick the fitting toolset for maximum efficiency, you can find some recommendations in our article on AI-powered testing and RPA;
- Proper coverage is critical for efficient bug detection, so keep in mind all core functions while AI can help you achieve that faster;
- Identify the high-risk and critical areas to put them in focus, ensuring potential bugs are caught early;
- With software often growing more complex over time, parallel testing and data optimization are crucial for test scalability;
- Improve maintainability through practices like modular coding and design patterns, which will simplify further updating of test suites;
- Integrate your test automation into the CI/CD pipeline, enabling faster defect resolution.
Documentation also plays a crucial role in test automation execution. Strategy, test scripts, reporting, and more should all be detailed and well-structured to ensure a robust process. You can use a sample of test scripts from our Knowledge Center as a reference for your own documentation. AI will help you streamline documentation through automated analysis of requirements, existing test cases, and application behaviors.
While these should help you set up a robust basis for efficient AI-powered automation, there are still many typical mistakes to avoid. We have a whole article on this, so in this one, we’ll share the most widespread:
- Lack of strategic planning. Without a clear automation roadmap, teams often automate the wrong tests or miss critical areas, leading to inefficiency and increased maintenance costs.
- No root cause analysis. Flakiness is quite common for automation, with tests passing and failing inconsistently without any changes to the code. Make sure to troubleshoot such cases to maintain reliability.
- Trying to automate unstable features. Some teams tend to prematurely automate features under active development, resulting in frequent test failures and overcomplicated maintenance.
- Creating the interdependent scripts. Some may rely on interdependent scripts to speed up the process and make it easier to navigate, but this may cause cascading failures where one test not passing leads to the related tests not starting.
All of these hinder test automation performance, negatively affecting ROI. For a more detailed overview, be sure to check out the full article.
Pushing QA to the Next Level with AI
Advancements like test automation and AI are already changing the way software is developed and maintained. They allow for better, more efficient operations that are faster than 5 years ago (which is not so far). Even the risk of error is actively reduced with more tailored platforms being introduced, though one could expect that a “faster + better” combination cannot be a thing.
AI-powered automation also introduces significant financial benefits. Qodex recently modelled detailed calculations on the ROI of combining automated testing with AI. By optimizing repetitive tasks, organizations can significantly decrease the number of manual testing hours required. For instance, if AI reduces manual hours from 100 to 20 per sprint, and the average tester’s rate is $50/hour, the savings would amount to $4,000 per sprint. The full material can be found at this link.
So, to summarize, what is AI-powered test automation? It is a combination of two advanced technologies aimed at minimizing manual effort in QA while increasing test coverage and speeding up issue detection and resolution. By adapting to changes, analyzing data, and optimizing repetitive tasks it improves software quality and reduces costs.
Gathering an in-house team of people competent in AI and test automation is challenging, but that’s where QATestLab services can become a game changer. Whenever you’re looking for ways to improve your project’s ROI, we provide highly flexible and easy-to-integrate teams with the required skill set. With no need to handle hiring, onboarding, and replacements, your team would be able to leverage all the benefits of AI-powered testing while redirecting saved resources to other processes. Contact us to convert technical challenges into profit.
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