45% of new businesses fail within the first 5 years, and 75% of them close within 15 years due to the inability to cover expenses. More than 60% of startups don’t bring a positive return to investors.
To enter the small number of lucky new businesses that survive in the long run and achieve success, it is essential to do everything possible to maximize the ROI. For large companies, it is also vital to boost return on investment for further growth and development.
With AI test automation, ROI can be increased noticeably by automating tasks, improving workflows, and cutting expenses. Learn more about how incorporating AI-driven tools into QA processes helps optimize costs and increase net profits.
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What is AI-powered testing: A comprehensive definition
AI-driven solutions are being actively implemented in all IT processes, and QA is no exception. Such technologies as predictive algorithms, decision trees, generative AI models, and others enable automating some tasks and improving workflows. Let's take a closer look at what role artificial intelligence plays in QA and how exactly it helps boost the ROI of test automation.
How is AI used in testing?
AI-driven tools automate, speed up, and simplify many tasks of QA engineers. To improve their workflows, teams use:
Generative AI and natural language processing models like ChatGPT to write test cases and scenarios.
AI-driven test automation platforms like Katalon Studio or Testim to create test automation scripts and manage test data.
Machine learning and deep learning predictive AI models to prioritize test cases, enhance the quality of the QA process, and find bugs in the early stages.
Self-healing test automation scripts based on ML and AI to ensure ongoing testing of the software that is regularly updated.
These are just a few examples of the usage of AI in QA. You can find more cases in our other article.
How does AI assist in overcoming testing challenges?
Modern applications have complex and multi-layered structures, and user demands in today's highly competitive environment are constantly increasing. All this leads to the fact that QA may be associated with numerous challenges. And AI can become a great assistant in overcoming them. Next in the article, let's take a closer look at how smart technologies help cope with the most common challenges QA professionals face.
Challenge 1: Time limitations
Every business is trying hard to shorten the time to market before the competitors get ahead of them. Therefore, there may not be enough time allocated to the testing phase to perform every task manually.
It is possible to automate up to 70% of repetitive tasks with the help of cutting-edge AI-based solutions. This enables companies to speed up the processes.
Challenge 2: Budget shortage
No company has an unlimited budget. And when it comes to the distribution of finance, most of the resources may be allocated to development, while not enough to QA.
AI-based assistants help reduce IT costs by up to 50-70%. By automating routine tasks and accelerating workflow, it is possible to allocate less money to pay for QA specialists' working hours.
Challenge 3: Incomplete test coverage
Many companies find it critically difficult to sufficiently cover an application with a complex structure with tests. Doing so takes too much time and resources.
AI-driven testing solutions can analyze the code and identify test coverage gaps. And by using test automation tools like Testim specialists improve test coverage faster and with less effort. 97% of companies that have already implemented AI in their QA processes notice that it positively affected test coverage.
Challenge 4: Insufficient bug detection rate
If a lot of work is done manually, it is impossible to avoid the risk of human error. Because of this, some bugs may not be detected timely.
Artificial intelligence is capable of analyzing a huge amount of information, never getting tired. It can find hidden bugs and predict which code sections are the most prone to the appearance of defects.
Challenge 5: New defect appearance during updates
Frequent changes may provoke the emergence of more defects in the code. Besides, after updates, it is necessary to create new test cases to check the quality of the added features and code sections.
Predictive AI can “forecast” the risk of bugs in case of certain changes. Also, with the help of self-healing automated test scripts, it is possible to ensure the ongoing QA process is adjustable to updates.
Challenge 6: Compatibility concerns
Modern users may prefer a variety of devices (both desktop and mobile) and a wide range of different browsers. The product should function correctly, regardless of the type of device, operating system, browser, and so on. It may be too difficult and time-consuming to conduct full-fledged testing under all possible conditions.
AI enables faster and easier compatibility checking. The tools based on artificial intelligence are capable of creating realistic test environments to see how the product performs under different conditions. Also, it can analyze user behavior and other statistical data to identify the most popular configurations under which the software should be tested primarily.
Challenge 7: Prioritization issues
Setting priorities in testing may turn out to be more complex than it initially seemed. Proper selection of criteria for prioritization and keeping the balance between main concern testing and overall test coverage may become a challenge. Moreover, it may be quite difficult to determine the correct order of testing tasks in complex projects, where some test cases may depend on others or overlap with each other.
Tools based on machine learning and artificial intelligence can make prioritization much easier and faster. They are capable of analyzing large data sets, such as code change history, the log of previous tests and their results, user behavior, etc. Based on this information, they highlight the most important points for testing and identify the areas with the highest risk of defects that require primary checking.
Challenge 8: Data management difficulties
Drawing up reports and keeping documentation is quite time-consuming. Also, it is a rather routine job that often contributes to faster employee burnout.
To free professionals from the repetitive tasks associated with compiling and maintaining documentation, it is a great idea to use tools like generative AI in testing that accelerate and simplify this process.
Challenge 9: Lack of expertise
Hiring and retaining a team of highly skilled QA engineers with extensive experience may be too costly. Not all companies, especially if we talk about small businesses and startups, have an opportunity to allocate enough financial resources for this.
In such cases, it is a good solution to use AI tools for automated testing, such as OwlityAI. Even employees without extensive QA expertise can use it to check the software's functioning. This autonomous AI-based testing system automatically analyzes application functionality, generates test cases, and creates test reports.
How do businesses maximize ROI by implementing AI into QA?
ROI is the ratio of net profit to the investment made. The higher the figure, the better the profitability of a certain business or some endeavors. It is possible to significantly increase ROI in testing with the help of investing in AI implementation. Let’s consider next how exactly it can be achieved.
Saving costs
Introducing AI tools gives an opportunity to notably boost the ROI of test automation efforts thanks to cutting expenses. Some companies report that they have managed to reduce costs by up to 50-70% by implementing AI-based solutions into their IT workflows.
“Smart” solutions help to accelerate task completion and decrease the number of billable QA engineers’ work hours needed to test software. Also, AI-powered automation makes it possible to prevent burnout of specialists due to a large scope of routine tasks. This, in turn, helps to reduce the HR costs associated with the search and adaptation of new members of the testing team.
Enhancing quality
With the help of AI-based tools, it is possible to identify bugs at earlier stages, as well as find various hidden issues that may have negatively impacted application performance. All this improves the quality of the product, which positively affects the user experience and customer satisfaction rate.
It is vital to keep users satisfied with the product so that they want to recommend it to their friends. 93% of consumers trust the opinion of their family and acquaintances when it comes to choosing some brands or services, as marketing research shows. So, enhancing user experience and boosting customer satisfaction contribute to increasing the number of new customers and, consequently, getting higher revenues.
Increasing efficiency
Implementing AI-based solutions helps to increase the productivity and efficiency of QA. The main benefit of using such tools is automating routine tasks. Also, “smart” assistants help to:
prioritize test cases more correctly and effectively;
prevent potential problems even before they emerge;
identify more bugs in shorter periods of time;
analyze the code for test coverage gaps and fill them.
All these opportunities make QA workflows faster and more productive, which, in turn, increases the ROI of test automation efforts.
Gaining competitive advantage
The modern IT market is tremendously competitive. More than 30,000 new IT products are launched every year. Sometimes speed can be the deciding factor in a competitive race. All businesses are striving to shorten the time to market to get ahead of competitors. And AI solutions make great assistants in this. As they significantly accelerate the workflow, they help IT teams complete the testing phase faster, thus, delivering the product to release sooner.
Moreover, good AI testing tools help to eliminate bugs and other issues more effectively. This helps to outperform competitors in terms of quality.
Furthermore, AI-driven tools can be used to get a deeper understanding of the target audience to cater to them better. AI is capable of analyzing data about user behavior and preferences. Based on this information, a company can improve its products to meet the needs of customers more than competitors.
Effective work of QA engineers with AI as the way to success
AI technologies are the future of IT. They are transforming workflows, making them more productive and efficient. When used correctly, they help increase ROI by saving costs, reducing time spent, and enhancing quality.
However, you still can't do without human specialists. They should thoroughly and properly set up and oversee the work of AI to achieve the best possible results.
To optimize the processes and get a competitive advantage, it is essential to encourage professionals to be open to using AI tools and to know how to do it correctly. Good AI-powered testing solutions in the right hands are a powerful tool to improve QA efficiency and increase ROI.
Conclusion
AI-based tools help to increase the ROI of test automation efforts. Numerous companies are actively implementing these technologies into their QA processes. Smart testing assistants help to overcome such QA challenges as:
time constraints;
budget limitations;
test coverage gaps;
insufficient or late bug detection;
the constant emergence of new defects with frequent updates;
compatibility issues;
troubles with prioritization;
data management concerns;
shortage of expertise.
This can be achieved by, for example, AI-driven test case prioritization, automatic test case generation, or using predictive algorithms to identify the code sections with the highest risk of defects. Overcoming challenges and taking QA workflows to the next level enables to:
save costs;
enhance the product quality;
increase testing productivity;
gain competitive advantage.
97% of companies surveyed reported that the implementation of AI had improved the productivity of quality assurance (43% of them noticed “significant” improvements and the other 54% told of “somewhat” positive changes). Such promising statistical data allows us to conclude that good AI tools in the right hands upgrade the QA processes and increase the ROI.
Do you want to take your QA to the next level and boost ROI with cutting-edge technologies? Reach out to DeviQA to get an expert consultation on AI software testing.
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