5 AI trends shaping software testing in 2025

Thanks to AI, a few people might be starting the new year with bright, shiny smiles. The technology has exploded in popularity and augmented almost everything, including a toothbrush and app combo that uses AI to optimize your dental hygiene habits.

Teeth brushers aren’t the only ones grinning due to the AI explosion. Enterprise leaders have been cheerful about the advantages AI can bring to their company’s testing efforts and the software development lifecycle (SDLC). To speed up the SDLC, 61% of organizations prefer to use generative AI for code generation and auto-completion.

Tricentis’ own research has shown that 80% of software teams will use AI next year. That’s a staggering adoption rate that hasn’t been seen since maybe the smartphone explosion in the 2010s. Simply put – if you’re not using AI in your personal or professional life, there’s a likely chance that 2025 is the year that changes.

AI will become a necessity for QA leaders and engineers to remain competitive as it becomes embedded in every facet of software quality, influencing product development cycles, customer experiences, and business operations. So where will the technology go from here? In this blog, we’ll share the AI trends that will likely enhance software testing in the months ahead and set us up for success for years into the future.

Trend 1: AI fears will turn into excitement

You know that feeling when you’re at an amusement park looking at the crazy ride that will take you upside down and twirl you around? Your initial fear can quickly turn into a rush of excitement. That’s the feeling right now around AI. The positive perceptions about AI have now surpassed the negatives. Years of uncertainties that AI can’t be trusted or is taking over our jobs have given way to what it can do for software testing.

The job outlook is positive. The U.S. Bureau of Labor Statistics predicts jobs for software developers, quality assurance analysts, and testers will grow at a “much faster” rate than the average of all occupations from 2023 through 2033. In its report, the agency credited AI, in part, for driving the increase. Some of the ways testing teams implement AI are to identify what needs to be tested based on requirements, to generate test cases for those areas much more quickly, and to simplify test case maintenance with self-healing capabilities.

So why the excitement now? Because enterprises are seeing results from their AI investments. AI is helping to increase cost efficiency, shorten time to market, and improve quality. The World Quality Report 2023-24 found that 75% of organizations consistently invest in AI and utilize it to optimize QA processes. Almost two-thirds (65%) of organizations say higher productivity is the primary quality outcome for using AI.

Trend 2: AI will drastically shrink test suites and execution timelines

A typical New Year’s resolution is to lose weight. Waistlines won’t be the only thing shrinking this year – test suites will be too. Teams will use AI-augmented technologies more often to reduce the number of tests needed and ensure a change won’t wreck production systems. Shortened test execution time – sometimes up to 80% faster – means a faster delivery pipeline and less reliance on resources.

AI is infiltrating many areas of the test case portfolio to make testing more efficient. AI algorithms can analyze historical test data to identify flaky, unused, redundant or ineffective tests, tests not linked to requirements, or untested requirements. The AI analysis allows teams to identify weak spots or areas for optimization.

Generative AI is being used to speed up test case creation by auto generating tests and analyzing requirements (user stories and epics) using natural language. It includes the title, preconditions, and description for each generated test case, plus the informative descriptions and the expected test result.

We are witnessing the rise of a new class of AI-powered testing solutions called quality intelligence. The AI pinpoints the impact of code changes and risks up front to minimize the redundancy in test suites. By prioritizing only the necessary tests based on what’s actually at risk, teams can cut test cycle time and costs and minimize the risk of production errors.

Trend 3: Developer productivity and output will skyrocket

If 2023 was the year of the emergence of the term “AI copilot” and 2024 was when it seemed that every company in the universe launched a copilot (present company included!), then this year we expect to see how well all those copilots can so-called “land the plane.”

The behemoth among the copilots is Microsoft 365 Copilot. A recent Morgan Stanley study found that 94% of CIOs expect to adopt Microsoft generative AI products over the next year, up from 63% in Q4 2023. However, according to Forrester, despite adopting AI copilots, business leaders are still waiting to see their payoff and true ROI beyond the benefits of better employee experiences.

The probable exception to the AI disbelievers? Testing. In a survey of more than 400 IT leaders at U.S. and U.K.-based organizations, more than two-thirds of respondents indicated high levels of trust in tool performance. Just 1 in 10 organizations aren’t convinced AI tools will improve testing efficiency and effectiveness.

As AI-powered software testing gains traction, developer productivity is expected to soar with the help of copilots and other AI assistants to auto generate customized coded test steps or optimize an existing test portfolio by finding unused, unlinked, or duplicate test assets. According to the Tricentis and Techstrong Research survey, writing code (58%) and testing (42.5%) are the two leading areas where respondents use AI copilots today. The survey reveals that AI copilot functionality will be available for use in close to 100% of the roles across the SDLC by the end of 2025.

Trend 4: AI tools will become multilingual (but maybe not the way you’re thinking)

It’s New Year’s Eve, and you’re listening to Auld Lang Syne while hearting your Insta friends’ party pics and reading the celebration headlines from around the world – congrats, you’re essentially a multimodal AI machine. Multimodal AI can simultaneously process information from multiple data types, like text, images, audio, and video, versus unimodal AI, which relies on a single data type.

According to a report by MIT Technology Review, the global multimodal AI market is expected to grow at an annual average rate of 32.2% between 2019 and 2030 to reach US$8.4 billion. Multimodal AI tools are already being used in testing, especially for areas where having an improved understanding of visual elements, user interactions, and contextual information helps to ensure test accuracy and coverage. For instance, a UI tester could leverage multimodal AI to analyze user interface screenshots and user interaction data.

Trend 5: AI agents will work independently alongside humans

Dictionary.com chose “demure” as its 2024 Word of the Year. If we had to predict the buzzword in tech circles for 2025, it would be “agentic AI” (okay, that’s two words). Agentic AI will not be demure-ish but will confidently move to the front of the AI tool collection.

Agentic AI happens when autonomous “agents” make decisions, plan actions, or solve problems independently, with little to no human interaction. Agentic AI is goal-oriented and focused on outcomes. Recent agentic AI launches include Salesforce Agentforce and ServiceNow AI Agents embedded in its Now Platform.

According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. Agentic AI is still evolving, and Gartner expects software developers to be some of the first to use it as existing AI coding assistants gain maturity, and organizations look for ways to increase the number of automatable testing tasks and workflows.

The advantage of agentic AI for software developers is its ability to automate multiple steps in the SDLC based on context and objectives. Agentic AI has the potential to write code and review code for errors as well as take on some of the tedious and repetitive tasks like bug fixes, freeing up developers to focus on more business-critical activities.

AI will continue to transform software development and testing

For the second consecutive year, AI tops the list (58%) as the most important technology in the year ahead in the annual IEEE survey of global technology leaders. AI isn’t one of those tech trends that come and go. AI has staying power, and as it continues to evolve and innovate, we predict an ongoing trend of it helping enterprises and their testing teams succeed in 2025 and beyond.

If you would like to learn more about AI and testing, read our 4 ways generative AI will transform the way you manage testing guide and discover how generative AI can boost your IT and quality engineering teams’ testing efficiency.