Don't fall behind: Why you should implement autonomous testing ASAP

The software industry moves fast — too fast for traditional testing methods. When you’re dealing with constant updates, tight deadlines, and the pressure to ship flawless products, relying on old-school testing can slow you down. This is where autonomous software testing steps in, powered by AI to handle the work that bogs your team down.

You can’t afford to fall behind when your competitors are automating their testing processes. However, you definitely can:

1. Speed up your development cycle: And replace lengthy testing phases with faster time-to-market.

2. Improve software quality: By catching bugs early and creating a fantastic user experience.

3. Reduce costs: While minimizing the need for manual testers and streamlining your QA processes.

4. Stay competitive: As a final consequence of everything above.

A bit of philosophy: you have two pains in life. The first is starting efforts, tuning processes and AI-powered testing. And the second is the pain of falling behind the market and losing your business. Those who hesitate now are the ones who will struggle to catch up later. Speed, precision, and efficiency rule. Your competition isn’t slowing down, and neither should you.

The changing landscape of software development

If you are an old hand in the software development field, you can recall times when development cycles were long and predictable. Teams had months, even years, to plan, build, test, and release a product. This might not have applied to big market players like Amazon or Google, but still. Testing was a linear process, with dedicated phases that allowed for thorough manual checks. Yet, that is water under the bridge now.

Today, everything is much faster and more complex. Agile and DevOps have transformed how software is built. Instead of long cycles, we now deal with continuous integration and continuous delivery (CI/CD), where updates happen almost daily. Software is more interconnected, often relying on microservices, APIs, and cloud-based infrastructure that require constant monitoring and testing.

Trying to twist your arm, we can say that traditional testing methods simply can’t keep up with this pace. Manual testing was a cornerstone of quality assurance. Now young and promising tech founders consider it as a bottleneck since it’s slow, resource-intensive, and prone to human error. Automated testing has helped, but even that requires significant maintenance.

So, autonomous software testing, your time is now! Powered by AI, it’s the natural next step in testing evolution. It adapts to changes in your codebase automatically, identifying what needs to be tested, running those tests, and analyzing the results — all without human intervention.

What is autonomous testing?

Autonomous testing is a next-level approach to software testing. Backed by AI and machine learning it automates the mentioned testing process. Let’s turn back to the traditional approach. Even automated, it requires human input for tasks like script creation and maintenance. And autonomous testing operates independently, adapting to changes in the codebase without manual intervention. Like the “ancient” vacuum in your granny’s house. You don't need to know how it works. What matters is that it works!

Key differences

The traditional way implies that your QA engineers create and maintain testing scripts. Yet, these scripts are prone to failure when there are changes in the software, such as UI updates or code refactoring. For example:

• If a button on a webpage changes its location, traditional automated tests might fail because the script is no longer valid.

• This leads to false positives or missed bugs, requiring engineers to spend time updating and revalidating test scripts.

AI-powered testing advantage

New-way approach simply overcomes these issues by using AI to dynamically understand and adapt to changes in the application:

• The AI can recognize changes in the UI or underlying code and automatically adjust its tests. This way, the need for constant maintenance is evening out.

• Eventually, we get fewer false positives, more accurate testing, and a significant drop in the manual effort.

AI and machine learning are the driving forces behind autonomous testing. They serve as enablement for:

Data-backed impact

Forrester predicts that even in an early, young market, implementing AI-driven testing will increase productivity by 15%. Autonomous testing represents a shift from traditional testing methods, offering a smarter, faster, and more reliable way to ensure software quality.

The competitive edge of autonomous testing

Autonomous testing tools host many benefits that give you a significant competitive advantage.

Speed

By eliminating manual interventions, autonomous testing can execute tests continuously, even during off-hours, ensuring that any issues are identified and addressed promptly. For example, when Netflix adopted new testing strategies, they reduced their regression testing time from days to just hours, allowing for over 100 updates per day without sacrificing quality.

Cost savings

The ability to reduce manual effort and detect issues earlier adds up directly to cost savings. The above-mentioned study by Forrester revealed that companies implementing AI-driven testing saw a 15% reduction in testing costs due to increased efficiency and early bug detection.

Accuracy

We are all human beings, and we all screw up sometimes. Autonomous software testing minimizes human error, which is often the main red flag when considering the testing approach. AI is able to adapt to changes in the codebase which means tests remain relevant and accurate, even when your project scales.

The British Department for Science, Innovation, and Technology conducted a massive survey and found that particularly in Britain, companies mostly leverage ready-to-use external AI services (up to 60%). The question is what the testing share is? No need for guessing — cyber security and testing grab 67% of overall AI usage.

Scalability

If you don’t fall under 90% of unsuccessful startups, your project will scale sooner or later. And maintaining test coverage can become that task. This is where autonomous testing enters the room as it can easily scale to accommodate large projects with numerous test cases.

Consider how Google manages its massive infrastructure, with thousands of interdependent services. By using AI-driven autonomous testing, they can efficiently manage and scale their testing processes (without burnt QAs).

Resilience

The autonomous test platform also provides the flexibility to adapt to changes without the need for constant human oversight. This adaptability has been crucial for marketplaces in particular. Recall the first COVID-19 year.

Amazon and other giants found themselves up in the air: on the one hand, they got an unbelievable increase in client base; on the other, that enormous wave of website visitors could break their system at all. Their success hinged on how quickly they were able to respond to changes without compromising on quality.

Let’s take Netflix as another example. One of the most innovative companies definitely keeps up with testing trends and due to this they:

• Reduce testing time: Netflix's autonomous testing solution, "Chaos Monkey," automatically introduces simulated failures into their production environment to test the resilience of their systems.

• Improve defect detection rate: Autonomous testing has enabled Netflix to detect defects earlier in the development process.

• Achieve 100%-ish test coverage for critical functionalities: The testing solution has helped them ensure that all critical functionalities of their platform are thoroughly tested with the tiniest risk of outages.

Risks of delaying implementation

#1 Falling behind competitors

Can’t help but admit: Modern software development field is unforgiving. Yet, companies that have already implemented autonomous testing profit from faster releases, better quality, and lower costs. For instance:

• Airbnb: Flexible trip planning, housekeepers check, and other features require thorough testing. And the time it takes to deploy such features. This is where Airbnb reaps the benefit of autonomous testing.

• Tesla: Tesla’s software development if it doesn’t hinge on testing, does significantly rely on it. Because without rapid updates to their vehicles’ software they won’t achieve minimal downtime and high reliability.

• Salesforce: Allows them to maintain high standards across their diverse product offerings.

Key alert: Competitors who have already embraced autonomous testing are not just ahead — they’re pulling further away. Delaying adoption puts your business at a significant disadvantage.

#2 Increased costs and inefficiencies

Traditional testing methods are becoming increasingly costly and inefficient as software grows in complexity. Testrail: Companies with manual-first testing culture spend up to 38% more on QA efforts compared to those who’ve adopted AI-driven solutions (Gartners gives an even more impressive figure of 58%).

Also, the mentioned Gartner’s report indicates that businesses can reduce testing costs by 25-30% with the adoption of autonomous testing.

Key alert: The longer you stick with outdated testing methods, the more you bleed resources – both time and money – while your competitors optimize and advance.

#3 Difficulty in maintaining software quality and customer satisfaction

Maintaining high software quality is crucial for customer satisfaction, which directly impacts revenue. PwC found that 32% of customers will leave a brand they love after just one bad experience. Companies that fail to adopt autonomous testing are at a higher risk of releasing buggy or subpar software.

Harvard Business Review surely stated that customer retention rates have a colossal impact on profit. With an increase only by just 5% it can boost profits by 25% to 95%. In a market where software quality is a key differentiator, this impact is too significant to ignore.

Key alert: Delaying the implementation of autonomous testing can lead to a decline in software quality, directly affecting customer satisfaction and, ultimately, your bottom line.

#4 Opportunity cost of delayed adoption

Every day spent without autonomous testing is a day where your competitors gain ground, your resources are wasted, and your customer satisfaction is at risk. The opportunity cost of delayed adoption isn’t just about lost time; it’s about losing out on the significant benefits that autonomous testing brings — faster time-to-market, lower costs, and higher quality products.

Key alert: The true cost of delay is not just in what you spend but in what you miss out on—reduced costs, enhanced quality, and sustained competitive advantage.

Steps to implement autonomous testing

1. Evaluate your current testing processes

Identify pain points: Start off by reviewing your existing testing processes. Try to find bottlenecks, areas of redundancy, or stages where errors occur too frequently. Pay special attention to repetitive tasks that consume significant time but offer little value. These are the first things to automate.

Take into account: Dependencies. Make sure to clarify them between different stages of your testing process. Often, the real bottleneck is not in a single step but in how these steps interact. Understanding these dependencies will help you determine where autonomous testing can have the most immediate impact.

2. Choose the right tools

Select tools and partners: Look for autonomous test platforms that integrate seamlessly with your existing tech stack and offer flexibility in handling different testing scenarios. OwlityAI, for instance, offers a plug-and-play solution where you simply input your web app URL and receive comprehensive test reports without needing prior QA expertise. It also automatically discovers your app features and creates test cases.

Take into account: When evaluating tools, don’t just consider their current capabilities. Assess their adaptability to future needs. Choose a tool that not only solves your current pain points but can evolve with your product and team, scaling as your testing requirements grow more complex.

3. Train and upskill your team

Training is your area for growth: If you can’t operate an axe, chances are, you won’t be able to insulate your house by burning logs. Advanced testing tools need skilled operators — so, invest in your QA team. Equip them to work with these tools effectively. Ideally, they should understand AI-driven testing methodologies, interpret test results, and know how to moderate when necessary.

Take into account: Don’t zero in on tool-specific training themselves. Cross-functional workshops can bring much more value. Get together your developers, QA engineers, and product managers to ensure everyone understands the role of autonomous testing in the broader development process.

4. Think globally, act locally

Pilot project: Forget the all-or-nothing approach. Start with a pilot project — perhaps a smaller, less critical application. This allows your team to double-check their knowledge and skills gained during training and understand their limitations in a controlled environment.

Take into account: During the pilot phase, set clear, quantifiable benchmarks for success, such as a reduction in test cycle time or defect rates. This data-driven approach will help you make a compelling case to stakeholders when it’s time to scale up the implementation.

Bottom line

Delaying the adoption of autonomous testing puts your business at risk. Autonomous testing offers the medicine against such a risk: speed, accuracy, efficiency, and scalability. Delays bring about higher costs, compromised quality, and lost market opportunities.

Begin your implementation by evaluating your current testing processes, choosing the right tools, training your team, and scaling gradually. Without push, OwlityAI is here to help. With our AI-powered software testing tool, you can implement autonomous testing quickly and effectively, ensuring your software meets the highest quality standards. Get started by hitting the button below.