Hidden Costs of Ignoring AI Testing in Your QA Strategy

At this juncture in economic history, AI has become a transformational force. It can predict possible bottlenecks, streamline processes, speed up operations with precise insights, and find inaccuracies within literal seconds.

The same stands true for Quality Assurance and software testing. Teams can no longer overlook AI’s advantages to their testing frameworks. While CFOs often hesitate at the initial setup and training costs, AI tools inevitably deliver higher ROI in the long run.

Additionally, ignoring AI will cause any team or company to drop in competitive value, as their peers leverage the advanced capabilities of AI engines. They will build better software, find bugs more efficiently, and release updates faster.

This article will expand on this point, diving into some of the hidden costs of ignoring AI testing in your QA strategy.

Missing out on AI testing is a “False Economy”

False economy describes a scenario in which an action/decision with apparent short-term financial savings leads to significant expenditure in the long run. Basically, it is the economic equivalent of “penny wise, pound foolish”.

Overlooking the pivotal role that AI will play in testing is a classic example of a false economy. While initial setup costs can be somewhat intimidating, the inclusion of AI and ML capabilities into test cycles has yielded overwhelmingly positive outcomes. On the other hand, the absence of AI often results in financial losses arising from low product quality, security gaps, and customer dissatisfaction.

What the numbers say

The greatest advantage of introducing AI in QA (or any other industry) is efficiency. A Formstack and Mantis Research report found that many organizations are bleeding to $1.3 million yearly due to inefficient tasks slowing down employees. Many companies have recognized this, especially in their QA processes.

According to Forbes, the global artificial intelligence market is projected to expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. It is projected to reach $1,811.8 billion by 2030.

TestRail’s survey shows that 65% of respondents already leverage AI in their QA processes. The 35% who have yet to do so are missing out on a critical component in modern QA strategies.

Another survey found that 77.7% of organizations are using, or planning to use, AI tools in their workflows. They use AI for:

• test data creation (50.60%)
• test log analysis and reporting (35.70%)
• formulating test cases (46.00%) AI engines are slated to intelligently automate 70% of repetitive testing. The role of software testers is quickly shifting to monitoring AI progress, modeling its workflows, verifying test results, and building test plans and strategies at conceptual and implementation levels.

What AI Testing brings to the table in QA strategy

Faster test cycles

AI can execute test cases at 10x the speed and accuracy of human testers and current non-AI automation tools. It can continuously build and run tests, adjust test code to accommodate UI changes, find bugs, and suggest fixes — all in fractions of seconds. This reduces the time taken between deployments and enables faster software releases.

For example, TestGrid’s CoTester comes pre-trained with advanced software testing fundamentals including comprehension of various tools, architectures, languages, and testing frameworks such as Selenium, Appium, Cypress, Robot, Cucumber, and Webdriver. That means it’s easy for your team to build tests in every language and framework without spending time training or picking up new tools.

The time eliminated at the test-building stage is significant enough to accelerate project releases by days, even weeks.

Wider Test Coverage
As AI can create sophisticated tests faster, it contributes directly to wider test coverage. The AI element can analyze massive datasets to create comprehensive test cases covering thousands of scenarios, including edge cases. With the right ML (machine learning) algorithm in place, these test cases can be designed to pick up on obscure bugs and push out the best possible product.

For example, Jenkins can integrate with AI testing setups to automatically initiate tests for each code commit.

Consider, as another example, an e-commerce platform under test. AI solutions like CoTester can analyze user behavior data, craft test cases for user scenarios matching different shopping patterns, and verify that the app works for users in all these scenarios. It can identify edge cases that the human mind is likely to miss and boost software quality — all at half the time required by humans/current automation solutions.

Faster & better feedback loops
While automation tools have sped up the rate of feedback reception in CI/CD pipelines, AI models can further speed up the process while also expanding on the number of insights.

For instance, AI models (like CoTester) can be specifically trained on data about an organization’s team members, team structure, tech stack, and code repo. Naturally, insights offered by such a tool will be more nuanced, comprehensive, and specific than more generalized insights from code-based automation tools.

By providing instant and better feedback on builds, AI capabilities unlock better test results and insights, which assist devs and QAs with finding and fixing bugs as early as possible in the SDLC.

Improved decision making
Unlike any other tool, AI engines can analyze large datasets of historical data to derive insights — a task too cumbersome for humans to accomplish. ML models, inherent in most AI protocols, can extract patterns and trends from previous bug reports. They can predict likely failure points, and create extra test cases to cover these areas of operation. It can also notify the team about common customer complaints and failure points, based on personal data.

In other words, AI models can assist testers with making better decisions during every stage of testing — planning, creation, execution, and analysis. By doing so within seconds (rather than days, as humans would), AI testing automatically brings greater quality to your QA strategy, while also cutting down on time between deployments.

Intelligent analytics and test prioritization
The mechanics of AI are capable of studying historical data and identifying likely issues and the components they will impact. IBM’s Watson is especially well known for providing insights into software modules that have historically faced bug-related failures.

This helps QA teams prioritize their testing efforts. It can also help rank tests based on preset criteria — code changes bug history, customer preferences, etc.

Improved test quality
One of AI’s core abilities in testing projects is being able to create self-healing tests. In other words, tests are automatically adjusted/updated to align with changes in the UI and source code. Consequently, testers don’t have to spend time updating individual tests with every change.

By taking human intervention out of the picture, AI engines don’t just speed up the process, but also keep tests consistent across the project lifecycle. All tests are automatically updated, which eliminates human oversight or inaccuracy — resulting in better test quality while reducing total time and effort.

Enhanced bug resolution
As bugs are captured via tests, AI engines can analyze logs, app behavior, customer preferences, and even predetermined requirements to identify root causes. Advanced root cause analysis is conducted automatically, and suggestions for bug fixes are presented to testers. Once again, this entire process takes minutes and supplements human testers’ evaluation of errors and their causes.

Dynatrace, a software observability tool, leverages AI to find the source of application performance issues automatically. It suggests possible underlying causes that minimize company costs arising from downtime.

Cost-Effectiveness
There is undoubtedly an upfront expense associated with implementing AI testing into your QA strategy. However, this expense pays for itself in the long run.

The many benefits of AI testing — increased automation efficiency, faster time to market, minimal defect-related costs, reduced test maintenance, improved test coverage, better resource optimization, and decision-making — all translate into higher software quality with less time and effort.

AI can be trained quickly on new technologies and protocols as compared to human testers. It makes fewer mistakes and works without rest. Depending on the tool, testers can follow a no-code or low-code approach to build fully capable tests, which reduces the need to hire many highly skilled QA professionals.

Conclusion

AI-driven testing is no longer optional; it is a necessity for modern QA strategies. The benefits — faster test cycles, wider test coverage, improved feedback loops, and intelligent analytics — far outweigh the initial investment. Companies that adopt AI will see enhanced software quality, reduced costs, and a competitive edge in the market. Ignoring AI in testing is a false economy, leading to higher long-term expenses due to inefficiencies, security risks, and lower product quality.

Source: This blog was originally published at testgrid.io