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Quality assurance is a leading priority for technology companies, and it’s about more than ensuring that products destined for market are built and perform as intended. QA is also vital to ensuring internal processes run as smoothly, swiftly and accurately as possible in service of the team and customers.
Leveraging artificial intelligence can not only help tech firms test and monitor the software and products they build, but also streamline, verify and enhance the quality of a variety of essential company processes. Below, 18 members of Forbes Technology Council share practical, impactful ways tech companies can leverage AI to support QA not only in product development, but also for essential internal functions.
1. Predicting Potential Issues
Technology companies can use AI and machine learning algorithms to improve product quality through automated testing tools, which analyze vast amounts of data in real time to identify patterns, anomalies and areas of weakness and predict potential issues before they happen. AI testing tools can save manufacturers money by minimizing the resources and time needed to perform manual testing. - Atal Bansal, Chetu
2. Automating Complex Test Scenarios
AI elevates QA by automating complex test scenarios. Take, for instance, its ability to analyze code for bugs. AI can swiftly scan through thousands of lines of code, identifying anomalies and potential errors that human testers might overlook. This focused, efficient approach speeds up the debugging process, enhancing both product development speed and end-product quality. - Wen Shaw, Cooby
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3. Creating User Personas
Generative AI can speed up the creation of user personas from product descriptions or manuals, uncovering unique profiles that humans might miss. Generating testing scenarios around these personas can help tech companies assess how well their products support end users in achieving their goals, identifying areas where the user experience can be improved. - Victor Shilo, EastBanc Technologies
4. Complying With FDA Regulations
There are new companies that support tech firms in the process of complying with FDA regulations. We’re a company in the software as a medical device space, and the tools these companies provide have enabled us to spend less time ensuring we’re meeting regulatory requirements and instead focus on building safe, reliable software that hospitals and patients can trust. - Salim Afshar, MD, DMD, Reveal HealthTech
5. Detecting Anomalous Patterns
AI can be leveraged as a data observability tool to support consistent, high-quality data in an organization’s information architecture. AI can automate always-on, unsupervised anomalous pattern detection at scale and alert users to an unexpected deviation. A human can intervene to contain the issue and minimize the downstream impact to applications and processes that rely on the impacted data. - Michael Meucci, Arcadia
6. Monitoring And Analyzing Production Processes
Technology companies can leverage AI in their quality assurance efforts by utilizing machine learning to monitor and analyze production processes. AI can identify patterns and anomalies in the manufacturing process, allowing for real-time adjustments and improvements. It can also predict potential defects or malfunctions, enabling proactive measures to maintain high quality standards. - Christine Boles, Intel
7. Finding And Prioritizing Software Bugs
AI can automate the testing of technologies by analyzing the data, generating various test cases and finding bugs. It can predict the bugs that are likely to have the most severe risks and focus testing on those with the highest priority. AI helps provide better-quality software. - Jared Thau, Gameverse Interactive Corp.
8. Analyzing Customer Service Phone Calls
AI can assist with the automatic analysis of support phone calls. Before, companies had to pay someone to listen to randomly sampled calls to ensure agents were following compliance scripts and resolving customer issues, as well as to check for nuances such as politeness. Now, AI can transcribe and analyze all calls for these criteria in seconds and can even provide automated coaching and practice sessions for underperforming agents. - Miles Ward, SADA
9. Mimicking Real User Interactions
Deploy AI to create dynamic, personalized test scenarios that mimic real user interactions. This approach doesn’t just validate code; it delves into understanding user experiences, ensuring software not only works flawlessly, but also resonates deeply with its audience. It’s a leap from traditional testing to crafting user-centric digital masterpieces. - Sandro Shubladze, Datamam
10. Detecting Product Defects
In hardware manufacturing, computer vision technologies can instantly and seamlessly detect product defects before they go to market. Integrating AI into the supply chain has invaluable benefits in terms of efficiency, maintaining brand reputation and reducing product waste. - AJ Abdallat, Beyond Limits
11. Anticipating User Behavior And System Stress Points
AI can be leveraged in quality assurance by utilizing predictive analytics to anticipate user behavior and potential system stress points. By analyzing historical data and usage patterns, AI can forecast where issues might arise, guiding teams to proactively optimize performance and user experience. This forward-looking approach ensures smoother operations and enhances customer satisfaction. - Shelli Brunswick, SB Global LLC
12. Monitoring Customer Sentiment And Content
One of the best places to leverage AI is to identify negative customer sentiment, which can suggest product issues, bad service and/or misleading ads. Doing so helps a tech company prioritize concerns to address and track its brand reputation. Additionally, AI can be used to moderate user content: It can filter out spam, abusive language and potentially fake reviews, improving platform trust and safety. - Jason (Jiehui) Zeng, SHOPPEDANCE INC.
13. Identifying Edge And Corner Cases
AI can help identify edge and corner cases. By analyzing code, user behavior and past defects, it can generate test cases for extreme inputs, unusual combinations or unintended interactions. For example, when testing a shopping app, manual testers check adding items and checking out. In contrast, AI might discover an app crash when a specific combination of items or discounts is applied. - Konstantin Klyagin, Redwerk
14. Powering Self-Healing Test Scripts
Technology companies can leverage AI in quality assurance through self-healing test scripts, which automatically adapt to application changes, reducing manual maintenance and downtime. This approach enhances testing efficiency, contributes to predictive maintenance and ensures robustness in the face of rapid development cycles, significantly improving product reliability and the user experience. - Amitkumar Shrivastava, Fujitsu
15. Generating Synthetic Data
The generation of synthetic data is a perfect use case for LLMs. Try a prompt such as, “Create a csv of 50 fictitious retail transactions,” and you’ll get very reasonable data. The better the prompt, the higher the data quality. This technique can be leveraged for images, user profiles, customer service interactions or just about any other data required for seeding a database or performing automated or manual testing. - Elliott Cordo, Data Futures
16. Performing Root Cause Analyses
One way tech companies can use AI in their QA processes is to perform root cause analyses. Basically, this means AI analyzes crash dumps and pinpoints the cause so that developers can fix it. I think this is a very good use for AI if you want to fine-tune your product while saving time and money. - Thomas Griffin, OptinMonster
17. Reviewing Documentation For Inconsistencies, Gaps And Outdated Information
AI-powered solutions can rapidly comb through complex documentation, such as design specifications, regulatory filings and product manuals, to check for inconsistencies, gaps and outdated information. This allows organizations to more efficiently validate both internal and external-facing documentation, ensuring that it is accurate and complete and that it reflects the current state of the product. - Marc Fischer, Dogtown Media LLC
18. Supporting And Enhancing UX Testing
AI can enhance QA by improving user experience testing. AI can monitor user interactions and identify patterns that suggest usability issues or areas for improvement, allowing the system to deduce user needs and preferences from digital footprints. This ensures products are bug-free and finely tailored to each user’s expectations. - Gergo Vari, Lensa, Inc.
