The ongoing AI boom is transforming the online user experience (UX) at a rapid pace, and the development of automation technology is redefining usability testing.
The global AI-enabled testing market size is expected to surpass a value of $1 billion in 2025 before reaching $3.8 billion by 2032, representing a compound annual growth rate of 20.9%. This market growth is expected to prompt a sharp rise in intelligent use cases online, with the proliferation of generative AI models paving the way for unprecedented developments in usability and user testing. AI models are already implemented in many industries, from human resources and customer support to web development and manufacturing.
Usability and user testing help designers and product teams create a better UX. With usability testing, users are asked to complete a series of tasks on a website or app, and then the product team evaluates the results and identifies the areas that could be improved or need to be fixed.
At my work at my SEO agency, we’re constantly trying to improve UX, both for our businesses and our clients. With more and more web-browsing AI agents rolling out every month, I believe it’s possible that in the near future, websites will have to be primarily optimized for AI agents as opposed to humans. With this in mind, let’s take a deeper look at some recent developments within the industry, along with how companies can keep up.
Web-Browsing AI Agents
AI agents that can browse webpages and perform actions on behalf of users are one AI application transforming the user experience. Consider Google’s Project Mariner, an “experimental AI agent that browses and uses websites,” according to TechCrunch. Currently, the tool is available to U.S. subscribers. TechCrunch noted that Google’s tool “competes with other web-browsing AI agents, such as OpenAI’s Operator, Amazon’s Nova Act and Anthropic’s Computer Use.”
However, I believe one prospective challenge emerging from AI agents could be that human users won’t be as engaged with the websites they visit since the AI agents will complete tasks on their behalf.
If AI agents become the primary users of websites and apps, I think we’ll see a shift in usability and user testing methods, including testing that becomes both human-centric and AI-centric. These shifting sands for usability and user testing mean that a more intuitive approach must be taken to ensure both human and AI audiences are served effectively. This will hinge on adapting websites to meet the needs of an AI agent and prompt fresh considerations for product teams.
Sentiment Analysis
AI is not only changing how web developers adapt their UX; the technology is also enhancing the relationship web developers have with their users. For instance, Amazon Comprehend is a natural language processing (NLP) tool that uses machine learning (ML) to perform sentiment analysis and extract insights from text documents. It can tell developers “if a piece of text is positive, negative, neutral or mixed,” according to an Amazon Web Services page titled “What is Sentiment Analysis?”
Insights like these can help businesses better understand user sentiment toward specific parts or products or services as a whole, and NLP models are capable of supporting a wide range of languages to ensure no valuable data is lost in translation. NLP sentiment analysis uses AI to turn the feedback of users into actionable insights, and information can be extracted across a variety of online sources, including user reviews, support tickets and surveys. This approach allows developers to gauge satisfaction or frustration among users, which can help them quickly identify recurring pain points.
Synthetic Users
Synthetic users are AI-generated profiles designed to mimic a user group, offering efficient artificial research findings without having to rely on humans to create the data themselves. Platforms like Synthetic Users have created an ecosystem built around large language models (LLMs) that have been trained on high volumes of data about target audiences.
The impact of synthetic users revolves around speed and decisiveness. In many cases, developers need to user-test new ideas quickly, a process AI can enhance by creating simulations of your defined personas to test these ideas on. The end result is an effective means of filtering out bad UX concepts at their ideation stage while allocating more time to more positive experiences.
While some LLMs offer variations of synthetic user testing, it’s worth noting that many generative AI models have a tendency to please their users, meaning developers could have trouble extracting useful insights from an LLM that’s unwilling to criticize UX models. With this in mind, I would advise professionals to avoid becoming dependent on synthetic users and to instead use the tool as a preliminary option before testing with human users.
Moving Forward
The AI boom is transforming user testing at a rapid pace, and I see it as a good thing for decision makers who are willing to adopt the technology. However, the future of UX will be collaborative and will rely on the intelligence of existing teams to successfully navigate the new AI landscape.
Algorithms and ML tools will only be as intelligent as their source materials, meaning that sentiment analysis and synthetic testing can be flawed. One of the biggest challenges the industry will face in the years ahead focuses on exactly how to accommodate AI in online UX.
Getting started in preparing your existing workforce for the collaborative future of AI will be essential sooner rather than later for a more frictionless transition. The future of AI user testing will be transformative for web development, and getting in on the act early could pay dividends in getting ahead of your competitors.
https://www.forbes.com/councils/forbesbusinesscouncil/2025/09/18/how-ai-use-cases-are-redefining-usability-user-testing-and-online-ux/a>