It’s 2025 and software testing AI can no longer be ignored. The integration of artificial intelligence into QA has passed the tipping point, with recent advancements addressing longstanding bottlenecks in test maintenance, resource allocation, risk management, and other areas.
As you will know if you’re a regular reader, I’ve followed the advancements in software testing AI over the last few years. While I’ve been impressed by the pace of change, I’ve been disappointed by the low adoption rates among many of the companies I speak to.
This hesitancy was potentially due to general concerns about AI’s reliability, issues around integration challenges, and the perceived high initial investment. Plus, some tools providers jumped on the AI bandwagon when their offering was little more than clever coding.
However, as we’ve entered 2025, these barriers have significantly diminished, and AI is no longer just an option.
AI innovations in software testing can deliver unprecedented efficiency gains and bridge the gap between manual and automated workflows. Below are four software testing AI breakthroughs you can’t ignore in 2025.
1. Self-Healing AI Scripts
I’ve been involved in test automation for decades, and one persistent challenge has been the fragility of scripts in dynamic development environments. Leading solutions now employ AI object detection that autonomously adapts test scripts to UI changes. This is now a well-established AI feature that has been slashing maintenance efforts for a number of years.
Modern frameworks use multi-layered object recognition strategies that analyse DOM (Document Object Model) structures, visual layouts, and semantic relationships.
Unlike static locators, these systems build probabilistic models of UI components, enabling them to adapt to font changes, repositioned buttons, or redesigned form fields without manual intervention.
For example, when a login button moves within a dashboard, the AI identifies it through spatial relationships with adjacent elements like username fields.
This capability can be transformative for organisations practising continuous deployment, where weekly UI updates often cause test suite failures.
2. AI-Guided Manual Testing
While automation dominates efficiency discussions, manual testing still accounts for a significant proportion of testing activities – after all, you should never aim for 100% automation.
Gen AI solutions like OpenText DevOps Aviator can now generate ideas for new manual tests and convert them into test cases complete with steps. This can dramatically reduce the time and effort required to design and create manual tests.
It’s important to note that AI-guided manual testing complements automated testing rather than replacing it. While automated tests excel at repetitive, high-volume tasks, AI-guided manual testing shines in exploratory testing, usability assessment, and edge case scenarios.
This synergy between AI-guided manual testing and automation allows teams to achieve more comprehensive test coverage and uncover subtle issues that might be missed by automated scripts alone.
3. AI-Driven Cross-Browser Functional Testing
For years, software has been required to deliver consistent user experiences across multiple platforms and devices. This used to mean more effort for test creation, but tools like OpenText Functional Testing leverage advanced AI-driven capabilities to streamline multichannel testing.
This solution significantly reduces the time and effort required for test creation and maintenance by harnessing AI-based object recognition and machine learning algorithms. Testers can script once and then seamlessly replay tests across leading browsers such as Chrome, Firefox, Safari, Edge, and Chromium Edge, ensuring comprehensive coverage of diverse user environments.
Plus, a different OpenText product—Core Software Delivery Functional Testing—is packed with additional features that, while not all AI-based, are significant recent breakthroughs that you should be using in 2025:
- Convert manual tests to automated codeless tests with generative AI
- Easily convert Selenium code into units for automated model-based testing
- Centralise mobile device testing
- Gain on-demand access to desktop browsers as a service
And yes, I’m very aware that the two products mentioned above have very similar names, and do very similar things. But honestly, they are different products:
- OpenText Functional Testing (previously UFT One) is a functional test automation tool that you install on your own machine. It has limited but powerful AI capabilities.
- OpenText Core Software Delivery Functional Testing (previously ValueEdge Functional Test) is a cloud-based functional test automation tool that leverages AI more heavily (via DevOps Aviator) and forms part of the overall OpenText Core Software Delivery suite of Value Stream Management tools.
4. Predictive Analytics for Test Planning and Risk Management
One of the most exciting developments in software testing AI is using predictive analytics to enhance test planning accuracy and proactively manage project risks.
In point 2 above I mentioned how OpenText DevOps Aviator can help with test design and creation. It can also help by combining generative AI with advanced predictive models to optimise your software delivery timelines.
I’ve seen many projects where test development was completed way ahead of schedule for a module that was lagging, meanwhile tests were still being developed for modules that were ready to test.
By analysing historical data and real-time project metrics, DevOps Aviator forecasts feature completion dates, thus allowing you to focus on building and executing the proper tests at the right time.
Check out this video for more about what DevOps Aviator can do. It’s got some great insights, but I recommend viewing at 1.5x or 2x speed—you’ll see what I mean.
Conclusion
Software testing AI solutions have become indispensable tools for QA teams. They achieve unprecedented efficiency gains and allow you to maximise coverage and risk mitigation.
Breakthroughs like self-healing scripts, AI-guided manual testing, omnichannel functional testing, and predictive analytics are reshaping the future of software quality assurance.
Companies that embrace these advancements will reduce costs, accelerate their release cycles, and significantly enhance their ability to deliver exceptional user experiences across all platforms.
The time to integrate intelligent software testing solutions into your workflow is now—don’t get left behind. With many solutions available in the Cloud, adopting future innovations can be easy.
To get started, consider the following steps:
- Assess your current testing processes and identify areas where AI could have the most significant impact.
- Research and evaluate AI-powered testing tools that align with your specific needs.
- Start with a pilot project to demonstrate the value of AI in your testing workflow.
- Invest in training your team on AI-driven testing methodologies and tools.
- Gradually scale your AI implementation across your entire testing process.
For more information on implementing these AI breakthroughs, please request a call.
