AI has amazing capabilities, and it’s one of the best technologies for the future. It’s helping to change the world and bringing productivity enhancements across industries with its exceptional use cases. Quality assurance isn’t left out, either. AI is highly useful in any product development process, and many companies are already harnessing it.

Here, we’ll explore how AI is reshaping quality assurance across industries and helping organizations deliver better products to their customers in a shorter time frame. So, let’s start by understanding the benefits of AI in quality assurance, and then we’ll go over some examples of its current use.

What are the benefits of AI in quality assurance?

There are only a few technologies that provide as many benefits as AI. There are infinite AI use cases, with each delivering a different set of benefits. In this section, let’s explore the benefits that AI can offer in the quality assurance process.

Test consistency

Having AI systems in your quality assurance process will ensure that your tests are always consistent and free from any bias or oversight. With larger systems, the number of testers and test cases increases significantly. But AI models can help you maintain test consistency by giving accurate suggestions, enforcing testing standards, and creating transparent reports.

Better defect tracking

AI tools can keep an eye on your tests from end to end, and this ensures that defects are tracked and reported accurately. These models will evaluate the entire testing process, compare it with the product’s normal use flow, and quickly identify where the tests are failing. This will save time and effort spent on troubleshooting errors, and will also help you fix bugs faster.

Wider test coverage

With AI tools and models, you can increase your test coverage quickly and cover edge cases for the product that are harder to imagine and test. Because AI models understand your product’s functioning and intended use from different aspects, it can uncover hidden areas that might be overlooked in manual or rule-based testing efforts.

How is AI reshaping quality assurance across industries?

AI-driven test automation 

Test automation includes writing tests that can be programmatic and easy to replicate across environments and conditions. Though many testing teams love this, they also face issues when the product becomes large, and there are multiple moving parts that must be considered before automating the tests.

Today’s powerful AI systems are capable of writing concise test scripts that are focused on testing specific parts of the system, reducing failure points, and ensuring that tests can be executed successfully across environments.

Predictive analytics

Predictive analytics is the need of today’s systems, and it should be built into every step of your process. With predictive analytics, you can predict issues before they happen by analyzing performance, historical data, and other aspects of your systems.

Moreover, this predictive approach helps QA teams focus on writing and executing better tests without worrying about things breaking.

AI-powered reporting

You can write the best tests for your product and even test it correctly, but if the reporting isn’t good it won’t make sense. Many QA teams struggle with reporting and sometimes even omit things from their reports when business demands a quicker release. This can result in hurried delivery with incomplete testing.

AI models are changing this one step at a time by helping teams create awesome reports. These reports can include detailed bug reports, videos, and steps to re-create the bugs for other team members to study.

Test environments

Maintaining test environments and re-creating them for every test can be a cumbersome task. With manual processes, there’s always a chance that the test environment gets changed, and the entire test results can become different.

This is where AI tools and models are helping QA teams. They manage test environments, understand use patterns, and create resources as and when required to perform testing across test cases in a similar environment. Moreover, AI models with simulation capabilities can also perform real-world scenarios accurately under their managed test environments.

AI-based test execution

In many cases, test execution can take a significant time when the tests are performed on a large number of products. AI systems can perform test execution across the test sample by running parallel tests and ensuring faster completion of testing, along with perfect results.

By executing tests in parallel, AI models can help you save time in testing your products and even ensure that nothing gets missed in larger test batches.

Test data management 

When it comes to managing and organizing test data, humans may find it hard to manage every document for their testing efforts at a large scale. This is where AI shines. Well-designed AI models can manage test data much better, categorize them, and even make them searchable.

Managing test data better will provide you with visibility into your testing process, and also help in keeping an audit trail for your project’s testing.

AI-powered test generation 

If your AI tools have access to the code base, and they can understand it and its functionality, they can help you with test case generation. During this, they can look at your user stories, analyze requirements for the feature, understand historical data around the feature, and then write test cases that evaluate the feature against all criteria and ensure there are no gaps between expectations and completed features.

AI-powered test generation isn’t just for software projects; you can also leverage it for manufacturing or other industries, and it can work with project data files to come up with accurate test cases.

At this point, you know the benefits of AI in QA and how it’s reshaping QA across industries through its applications. So now is a good time to understand some of the challenges that come with adopting AI for QA.

What are the challenges in adopting AI for QA?

Adopting a technology like AI is never easy, even though it offers amazing benefits. So let’s take a look at the challenges.

Skill gaps

QA teams are often less technical compared to development teams, and they have a higher skill gap in adopting or creating their AI models. Skill gaps can become a big challenge in AI adoption because the teams aren’t able to cope with the complex development process. Moreover, many teams are resistant to change, and they don’t want to upskill, either, which becomes a serious problem when you want to introduce productivity-boosting AI tools to your process.

Data-related challenges

Data are the backbone of every AI system, and without enough training for fine-tuning data, the results aren’t good. Most teams that create their own AI models run into data-related challenges where they don’t have enough high-quality data, or the data are unstructured or labeled incorrectly.

All data-related challenges can affect your model’s performance. This is the sole reason to use enterprise-grade, ready-to-use models that can be fine-tuned for your use. When companies develop their own datasets they must comply with data privacy laws, only to discover they can’t use their datasets for model training in any form.

Maintenance 

Maintenance is one of the biggest challenges for AI in the QA process. Because most QA teams aren’t well-versed in software maintenance or support, the quality of AI models will degrade unless they’re tested, actively managed, and improved. This will require additional training and fine-tuning tasks that need to be done regularly to keep the models at their best performance levels.

Limitations in test coverage

Although most AI systems look advanced, they can sometimes limit your test coverage when you’re building something different. Most AI models are experts at pattern recognition, but they can struggle with testing that involves business logic, user experience, or complex integrations with many moving parts.

Unclear AI strategy 

Most companies adopt AI just for the hype, and they do so without a clear strategy. When you don’t have a clear adoption, and you don’t use strategy for your AI products within the QA process, it’s bound to fail, even with extreme efforts. Moreover, with an unclear strategy, you can’t have a proper road map for your AI adoption, and measuring success also becomes tough.

Insufficient budgets

Insufficient budgets are another big challenge. Many companies have already invested heavily in AI research, but they’ve received negligible benefits from their investments. Due to such outcomes, companies are cautious with AI spending. Also, not every company has enough budget to adopt AI for its QA process.

Organizational resistance

Many organizations love to work the old way, and there’s a lot of resistance to using new technologies or automating processes with AI. Such situations also pose bigger challenges in AI adoption, because employees fear job insecurity. Moreover, automated decision-making also poses serious threats to many management employees, and this becomes an additional hurdle to AI adoption.

Conclusion

Unlike other technologies, AI is here to stay. It’s going to reshape the entire product development process across industries. It will not only speed up quality assurance but also bring stability and reproducibility to QA programs. If your organization wants to improve its abilities and make the best products, now is the right time to adopt AI in your QA processes.