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Stating the obvious in 2025 – Test Automation is a must in any modern organisation.
Where before it was a differentiator, it is an absolute non-negotiable in any modern CI/CD or DevOps organisation. It is the cornerstone of modern QA, enabling faster feedback, standardized quality, and more reliable releases. However, automation engineers are a scarce resource, and maintaining automation frameworks can be time-consuming.
So how exactly does AI-augmentation help your automation team?
1. Self-Healing Scripts
AI-augmented automators have tools at their disposal that can detect changes in the UI and automatically update test scripts to reflect those changes. Tools such as Tricentis Tosca Vision AI can reduce tedious maintenance work and minimize disruptions caused by minor UI updates. As an added perk, it is good for team morale as well, as very few automation engineers enjoy that part of the job.
2. Intelligent Element Recognition
In line with the advantage above, instead of relying only on static identifiers, AI can use contextual understanding to locate and interact with UI elements. This improves test stability and reduces false negatives in automated runs, in turn boosting confidence in the automation solution.
3. Adaptive Test Execution
AI can help your automation engineer analyze execution history to optimize the sequence and scope of the automated tests. Business critical or error-prone application areas can be tested first to ensure fast feedback. Over time, AI could also suggest test scope reduction if certain scripts are not business critical, or never yield any defects. The automation engineer can then use their business expertise to confirm these proposed changes.
Additionally, if the relevant data is available, AI could also optimize test execution by evaluating system performance and test environment conditions. It could propose smarter scheduling, improved resource allocation, or (if possible) parallelization.
4. Predictive Failure Analysis
To help your team in being proactive vs reactive, AI tools can detect patterns in test failures and system behavior to predict where future issues are most likely to occur. This information could help the team proactively update flaky tests, identify root cause failures faster, and reduce time spent on debugging. This proactive approach not only reduces maintenance time, but also helps assure the reliability of the automation suite. As a result of all of the above, AI-augmented automation teams can work faster and more efficiently. Their AI toolset allows them to focus on strategic automation improvements and utilize their business knowledge, maximize their impact on the team. And let’s face it, being able to outsource some boring or tedious tasks to AI is a nice bonus as well.