Future of Test Automation Tools: AI Use Cases and Beyond

The way we approach software testing is very practical, if not utilitarian. 

Testing helps make sure your product works as intended and performs glitch-free. When manual testing was enough, we all just did that. 

Then automation software QA came to play, not because we were bored but because processes like UX UI test automation became a necessity. 

Now, AI automated testing starts replacing traditional test automation. 

This trend wasn’t born yesterday.  Many “classic” tools use machine learning for enhancing test management and intelligent automation. Traditional AI algorithms just didn’t get enough market attention before the success of generative AI.

AI helps us solve problems automation alone couldn’t. We’re no longer just running tests; we’re using tools that learn and adapt to find issues before they become problems.

To make all this sorted out, we have just held a panel on “The Not-So-Distant Future of Test Automation Tools.” 

This panel discussion is your guide to using AI in test automation. Hear practical advice from industry experts on how to start, scale, and optimize your automation efforts. 

The Future of AI in Test Automation Tools Development

AI-driven test automation tools are here. They’re faster, more comprehensive, and increasingly accessible to non-programmers. 

For C-suite executives, this means the potential for quicker releases, broader test coverage, and reallocation of QA resources to higher-value tasks.

Our recent webinar brought together three industry experts to discuss these changes:

• Taras Oleksyn, Head of the Test Automation Department, TestFort
• James Bent, VP of Solutions Engineering, Virtuoso 
• Bruce Mason, Delivery Director, TestFort

Watch now to explore key points of AI test automation:

1. How to evaluate and implement AI-powered test automation tools;
2. The impact of AI tools on existing QA processes and team roles;
3. Strategies for integrating AI with current testing workflows;
4. Practical use cases of AI improving test efficiency and coverage;
5. How to pilot AI solutions and avoid common pitfalls;
6. Balancing automation with human oversight in AI-driven testing;
7. Key trends in AI test automation for the upcoming year.

Choosing the Right Automation Tool

Selecting the right test automation tool involves various stakeholders across the company. Each role — CFO, CIO, and CTO — brings different priorities:

• CFO. They care about cost. Any tool you choose has to make financial sense. It’s not just about buying the software but understanding how it impacts budgets in the long run.
• CIO. For the CIO, it’s all about how the tool fits into your overall systems. Does it streamline workflows? Will it save time for your team? These are the questions they’re asking.
• CTO. The CTO needs to make sure the tool integrates smoothly with existing systems, complies with security protocols, and meets regulatory requirements. They’re focused on ensuring there are no disruptions.

Each of these roles has a different angle, so it’s important to have clear communication from the start. Tools that seem perfect on paper may not be right for every environment. 

That’s why some companies assign a neutral party who isn’t buried in the day-to-day tasks to oversee the tool selection process.

Key points for evaluation

• No single tool covers all testing needs. Expect to use multiple tools or supplement with manual processes.
• Assess how tools handle different testing levels (UI, API, backend). Many focus heavily on UI testing.
• Evaluate the tool’s ability to convert existing test scripts. Some offer import capabilities, but results vary.
• Consider starting with a pilot project to validate benefits before full implementation.

When engaging with AQA tool providers:

• Ask specifically what the tool doesn’t do. Credible providers will be upfront about limitations.
• Discuss how the tool can integrate with your current testing processes.
• Inquire about the tool’s AI capabilities, including test script creation, self-healing, and result interpretation.

Remember: The goal isn’t to replace your entire testing stack, but to enhance efficiency and coverage where it matters most for your business.

AI in Test Automation

AI-driven automated element identification is one area where progress is obvious. Instead of manually digging through selectors, AI can now recognize elements in the UI automatically. This speeds things up and leaves less room for error. Tools like Virtuoso have built this feature in, allowing testers to focus on bigger problems.

Self-healing tests were another hot topic. As Taras Oleksyn explained, AI-driven tools can fix broken scripts by detecting changes in the UI — something that used to take hours manually.

• No need to dig into broken code when minor UI tweaks cause tests to fail. AI takes care of it in real-time, reducing maintenance headaches.
• But don’t expect perfection. AI might be great for handling predictable changes, but for more complex shifts, human oversight is still needed.

James shared how AI tools can create test cases directly from user stories. While it’s still not perfect, it’s a huge time-saver.

Then there’s test data creation, another task where AI is proving its worth. Testers often struggle to create realistic, large-scale data for testing. AI tools can generate relevant, production-like data sets without the manual work.

But AI isn’t a magic fix. There’s a reason the speakers emphasized balance. AI is making things more efficient, but testers still need to guide the process, make decisions, and ensure the tools are working in the right context.

As Taras Oleksyn put it:

Practical Use Cases and Examples: Future-Proof QA

As a business owner or startup launcher, you’re constantly balancing quality, speed, and cost.

Quality assurance challenges can directly impact your bottom line, market position, and customer satisfaction.

Let’s see how AI test automation addresses these challenges and what it means for your business.

Keeping up with fast release cycles

Challenge. Agile and DevOps practices demand faster testing without compromising quality.

Business impact. Slow testing processes delay product launches, giving competitors an edge and potentially losing market share.

AI solution. Test creation and execution based on AI technology can significantly reduce testing time. For example, a fintech startup reduced their regression testing time from 3 days to 6 hours using AI to generate test cases and applying parallel execution.

Gains. Faster time-to-market, allowing you to respond quickly to customer needs and stay ahead of competitors.

Handling complex, dynamic UIs with AI-assisted UI testing

Challenge. Modern web applications with frequently changing UIs make maintaining test scripts a nightmare.

Business impact. Unstable tests lead to false positives, wasting developer time and potentially delaying releases or letting bugs slip through.

AI solution. AI automation testing tools help to create self-healing test scripts that can adapt to minor UI changes “on the go.” Test maintenance efforts go down, freeing time for creating better tests and paying attention to more complex user interface testing challenges.

Gains. More reliable testing, fewer delays, and reduced QA costs, allowing you to allocate resources to innovation rather than maintenance.

Respecting data-driven test management

Challenge. Creating and maintaining relevant test data is time-consuming and often a bottleneck.

Business impact. Inadequate test data can lead to missed bugs, compliance issues, and poor user experience simulation.

AI solution. AI can generate synthetic test data that mimics production scenarios. 

What this means for you. Better quality assurance, reduced compliance risks, and more accurate simulation of real-world usage, leading to higher customer satisfaction.

Prioritizing test cases

Challenge. With limited time and resources, determining which tests to run can be a guessing game.

Business impact. Running unnecessary tests wastes resources, while skipping critical tests can lead to costly bugs in production.

AI solution. AI can analyze code changes and historical test results to prioritize tests. A software development team reduced their build time by over 20% by using AI to run only the most relevant tests for each code change.

What this means for you. Optimized resource allocation, faster feedback cycles, and reduced risk of critical bugs reaching customers.

Bridging the skills gap

Challenge. Finding skilled automation testers is difficult and expensive.

Business impact. Lack of skilled QA personnel can slow down development, increase costs, and compromise product quality.

AI solution. Low-code/no-code AI testing platforms allow manual testers to create automated tests without extensive programming knowledge. 

What this means for you. Faster ramp-up for new team members, more efficient use of existing resources, and reduced dependency on hard-to-find specialist skills.

Improving test coverage

Challenge. Ensuring comprehensive test coverage, especially for edge cases, is challenging.

Business impact. Incomplete test coverage can lead to unexpected bugs in production, damaging your reputation and customer trust.

AI solution. AI can analyze application flows and suggest additional test scenarios. A banking application team discovered and fixed three critical edge-case bugs after implementing AI-suggested test cases, potentially avoiding a major production issue.

What this means for you. Higher product quality, reduced risk of costly post-release fixes, and improved customer satisfaction and retention.

Trends in the Automation Testing Tools Market

During the panel, our speakers explored current trends, but it’s hard to draw a line between what’s a trend and what’s already part of the present. The key is focusing on the trends that will have the biggest impact in the upcoming year. Here’s a breakdown of the most relevant trends to keep an eye on.

Increased role of AI in advanced test case generation

Right now, AI can create basic test cases from user stories, but it still needs human oversight to handle more complex scenarios. In the near future, we could see AI getting better at generating detailed test cases that cover a wider range of scenarios, reducing the amount of manual work even further.

As this technology improves, AI could take on more of the heavy lifting in terms of planning and structuring test cases.

Better AI test automation integration with DevOps

Another trend we can expect to grow is the tighter integration of AI-driven test automation tools with DevOps pipelines. Right now, there’s a lot of manual work needed to keep tests running smoothly within continuous integration and continuous delivery (CI/CD) environments. As AI tools get better at predicting issues and automatically adjusting tests, they’ll likely become a more seamless part of the DevOps process.

AI-enhanced continuous testing means faster feedback loops and fewer bottlenecks when it comes to releasing new features.

AI-driven test maintenance

Self-healing tests are improving, but the next stage could be full AI-driven test maintenance. We might see AI tools that not only fix minor issues like broken elements but also handle larger changes in the system architecture. This would make tests even more resilient, requiring minimal manual intervention from testers.

This could help teams maintain complex testing environments with fewer resources, freeing testers to focus on high-level strategy and planning, improving the overall test performance.

Growing benefits of AI for predictive testing

AI’s ability to analyze patterns in software could evolve into predictive testing, where the tool anticipates where future bugs or issues are likely to appear based on past data. This could allow teams to proactively test areas that are more prone to failure, catching issues before they impact users.

This would be a huge shift, making testing more strategic and less reactive.

Wider adoption of No-Code/Low-Code testing

The move towards low-code and no-code automation tools is set to accelerate. These tools allow testers to create automated tests without writing extensive code, making automation more accessible to a broader range of people within QA teams. In the future, we can expect these platforms to become even more user-friendly and powerful, further reducing the barrier to entry.

This trend will democratize automation testing, enabling teams with limited coding skills to still benefit from advanced testing strategies.

Regulations regarding tools like AI in software testing

New US and EU regulations of Artificial Intelligence will affect AI QA automation as well. Should providers and users of these AI systems be worried? In the panelists’ opinion, AI and Machine Learning regulations will keep evolving but won’t impose extreme hurdles on low-risk applications like QA tools.

Most QA automation systems will fall under low-risk categories, meaning they won’t face the same level of scrutiny as high-risk AI applications, such as those used in healthcare or finance.

For those using AI in QA, this means staying aware of evolving requirements and ensuring your AI processes are transparent and well-documented.

Applications of AI in Test Automation: Where to Start

Before jumping into AI tools, it’s important to start with what you already have. Rushing to overhaul your entire QA process can create more problems than it solves. Our panelists emphasized starting small, identifying specific areas where AI can help, and building from there.

Here’s a guide to help you take those first steps and avoid the common pitfalls of implementing AI in test automation.

Do your homework

Before you start talking with AI tool vendors, take a good look at your current setup. What tests are you running? Where are the bottlenecks? This groundwork will help you ask the right questions and avoid getting dazzled by flashy features you don’t need. AI based automation testing is a valuable add-up to most of the settings, but it still may not be the first thing you need right now. 

Start small

Don’t try to overhaul your entire QA process overnight. Pick a contained project or a specific test suite for a pilot run. This approach lets you test the waters without disrupting your entire workflow.

As James Bent pointed out, “Some AI tools can import existing scripts, like Selenium, which can be a good starting point.”

API testing is another field to leverage AI in software testing.

Mix and match

You don’t have to go all-in on AI right away. Especially if you have a team with strong “AI replaces human testers” bias. Keep your tried-and-true methods for critical tests while experimenting with AI tools. Use AI for manual testing as well. This hybrid approach gives you a safety net while you learn the ropes.

Know your data

AI tools are data-hungry beasts. Before you implement them, make sure you understand what kind of data they need and where it’s coming from.

Invest in curiosity

Encourage your team to explore new tools and share their findings. Set up a weekly “tech talk” where team members can discuss new AI developments in testing. This doesn’t have to be formal – even a quick chat over coffee can spark ideas.

Count the cost

AI tools can be pricey, both in terms of licensing and the resources needed to implement them. Don’t forget to factor in training time for your team. 

Take stock

Before you bring in AI tools, take a hard look at your current processes. Are there inefficiencies that AI could address? Or would AI just add another layer of complexity to an already convoluted system? 

Rethink your strategy

Adding AI to your toolkit isn’t just about learning new software. It might mean rethinking how you approach testing altogether. Be prepared to update your QA and automation strategies. This could involve redefining roles, adjusting timelines, or changing how you measure success.

AI Test Automation Implementation Checklist

Here’s a brief checklist to get you started with implementing AI in your test automation process. For a comprehensive, step-by-step guide, download our full checklist here (Download PDF).

Assess current testing environment

• Identify existing bottlenecks in your testing process
• Evaluate current test coverage and areas for improvement
• List manual processes that could benefit from automation

Define objectives and KPIs

• Set clear goals for implementing AI in test automation (e.g., reduce testing time, improve coverage)
• Establish measurable KPIs to track progress and success

Research AI-powered testing tools

• Investigate available AI testing tools and their capabilities
• Compare features against your specific needs and objectives
• Consider integration capabilities with your existing tech stack

Secure stakeholder buy-in

• Present the business case to key stakeholders (CFO, CIO, CTO)
• Address concerns about costs, implementation, and ROI
• Highlight potential long-term benefits and competitive advantages

Plan for implementation

• Choose a pilot project or specific test suite for initial implementation
• Develop a timeline for gradual rollout and expansion
• Allocate necessary resources (budget, personnel, training)

Prepare your team

• Communicate the benefits of AI in testing to alleviate concerns
• Identify and support “AI champions” within your team
• Plan for upskilling and training sessions

Implement and monitor

• Begin with your chosen pilot project
• Closely monitor initial results and gather feedback
• Make necessary adjustments to your implementation strategy

Evaluate and expand

• Assess the impact of AI tools against your defined KPIs
• Document lessons learned and best practices
• Plan for broader implementation across other testing areas

Continuously improve

• Stay informed about new developments in AI testing tools
• Regularly reassess your testing strategy and tool selection
• Encourage ongoing feedback and suggestions from your team

Ensure compliance and security

• Review AI tool usage against relevant regulations (e.g., GDPR, industry-specific rules)
• Implement necessary data governance and security measures
• Establish protocols for human oversight of AI-generated tests and results

AI Software Testing + Human Insight = Effective Test Automation

AI in automation testing is causing a divide in many companies. While executives see potential for increased efficiency, many testers fear losing their jobs to AI. This fear, fueled by media hype, can lead to resistance and even sabotage of AI initiatives.

However, this fear is largely misplaced. AI in QA automation and human testers are not competitors, but complementary forces.

Traditional AI has been part of industries for decades, consistently creating more jobs than it eliminates. The recent excitement is about generative AI, which offers new capabilities but doesn’t change this fundamental dynamic.

The real power in test automation comes from combining AI’s strengths with human expertise — no matter you are a manual tester or an automation engineer.

Traditional AI vs. Generative AI

First, it’s crucial to distinguish between traditional AI and generative AI:

Traditional AI in testing. Has been around for decades, primarily focused on:

• Pattern recognition in test results
• Automated test execution
• Basic test case generation based on predefined rules

Generative AI. The recent breakthrough causing excitement and concern, capable of:

• Creating test scripts from natural language descriptions
• Generating test data that mimics real-world scenarios
• Analyzing and interpreting test results in human-readable formats

Importantly, traditional AI in testing has historically created more jobs than it eliminated by increasing the need for skilled testers who can work with and manage AI-powered tools.

The Synergy of Human Insight and AI Capabilities

Here’s how human testers and AI complement each other:

Test Planning and Strategy

•  Human: Defines overall testing strategy, prioritize critical areas
•  AI tool: Suggests test coverage based on code analysis and historical data

Test Case Creation

•  Human: Designs complex, edge-case scenarios based on domain knowledge
•  AI tool: Generates a large volume of test cases for common paths and data variations

Test Execution

•  Human: Perform exploratory testing, usability testing
•  AI tool: Execute repetitive tests quickly and consistently

Result Analysis

•  Human: Interpret complex failures, identify root causes
•  AI tool: Flag anomalies, group similar issues, suggest potential causes

Continuous Improvement

•  Human: Refine test strategies based on product changes and user feedback
•  -AI tool: Learn from past results to improve test generation and execution

Will AI replace me? Addressing fears and resistance

Once again — implementing AI software test automation isn’t just about the tools, it’s about people. Some team members will resist, no matter how well you explain things. Skills won’t develop overnight. Testers might feel threatened, not empowered. Your first AI project could fail. And involving everyone can slow things down. 

• Education isn’t enough. Simply explaining AI won’t convince everyone. Some team members will remain skeptical or fearful despite your best efforts.
• Skill gaps are real. Not all testers will easily adapt to AI tools. Expect a learning curve and potential frustration.
• The value-add trap. While AI can free up time, some testers may feel their expertise is devalued. Be prepared for pushback.
• Pilot projects can fail. Your first AI implementation might not deliver expected results. Be ready to learn from failures and adjust.
• Collaboration challenges. Involving testers is crucial, but it can slow down implementation and lead to conflicting opinions.

AI is revolutionizing tests, but AI can also be a source of a resistance and even sabotage. To help your team members grow more fond of AI in software test automation, try the following:

• Acknowledge concerns openly. Don’t dismiss fears as irrational. Address them head-on.
• Identify AI champions. Find team members excited about AI and let them lead by example.
• Start small, but meaningful. Choose a pilot project that solves a real pain point for testers.
• Expect resistance. Plan for how you’ll handle both passive and active opposition.
• Measure and communicate. Track concrete benefits of AI implementation, but also be honest about challenges.
• Be flexible. Your AI strategy may need to evolve based on team feedback and real-world results.

You need a buy-in not only from stakeholders, but from people who will actually do all that performance testing, API checks, and all other movements necessary when it comes to software testing. AI can automatically do unit testing, LLM can power a quality AI chatbot, but there should a knowledgeable and motivated operator behind the strategy and daily commands.

Wrapping up: How to Automate Testing in 2025

Is there a way to avoid using AI-based automation testing tools? Can you just plan and execute tests old-school? You absolutely can. The only concern here is that the impact of AI revolutionizing software testing will do too much for your competitors and leave your product aside of the main user flow. 

Think of AI testing and strategic usage as the necessity to go online by the end of the 90s. 

Just a quick reminder:

• Choose a pilot project to introduce AI gradually without overwhelming your testing process.
• Use AI for repetitive tasks but rely on human testers for complex, high-value work.
• Focus AI on test generation, data creation, and script maintenance to get the best results.
• Ensure buy-in from the team and C-level
• Introduce AI step by step, fine-tuning your strategy along the way.
• Continuously assess new AI tools and strategies to keep improving your testing process.
• Human judgment is still critical to guide testing strategy and decision-making.

It is time to switch from wondering whether implementing AI would be good for you to planning which tools you want to use and how to try them in the near future.

The future of doing automation testing using AI.