The ROI of autonomous testing: A deep dive

You are a progressive business owner or a decision-maker, and you truly understand that without testing, the product is doomed. So, you are ready to allocate some software testing investment.

And you have an in-house QA team that uses many testing tools. Yet, you are in the dark about the overall Quality Assurance ROI, because you didn't analyze whether a particular tool hits the goals or not.

While you know traditional testing costs, it's still difficult to estimate the return on investment (ROI) of innovative technologies like autonomous software testing.

Well, the longer you delay such calculations, the higher the risk of failure and financial losses as software failures have significant financial impacts. In 2022, US companies lost an estimated USD 2.41 trillion due to poor software quality.

Autonomous testing enhances software quality and delivers substantial financial returns. It may sound overpromising, but real cases back this point up.

Let's cover the ROI of autonomous testing with a comprehensive analysis of its financial, operational, and strategic benefits.

Understanding the basics of ROI in software testing

Return on investment is the ratio of profit to costs. The higher your net profit and the less your spending, the higher the ROI. In software development, the parameter implies evaluating the feasibility of new technology or process implementation. It's a critical metric that helps to decide whether a particular investment will yield tangible benefits.

Bringing back our topic, software testing ROI is essential when adopting new methodologies like autonomous testing. It provides a clear picture of whether the efficiency gains and cost savings justify the investment.

ℹ️ Consider ROI in this case deeper than just a financial parameter: if implementing a new tool reduces the testing phase and decreases post-release defects, the ROI isn't just the direct cost savings from reduced labor hours. Include the financial benefits of launching products sooner, capturing market share, and reducing bug-fixing costs in the final calculations.

Traditional testing costs

Manual testing and conventional test automation usually have significant costs, which impact a project's overall budget.

• Labor costs: This testing type requires a team of QA engineers to conduct all the manipulations with testing (creating, executing, documenting, etc.). Now, sum salaries, benefits, and overhead expenses — by the way, labor can account for up to 60% of a testing budget.

• Time expenditure: Manual testing is time-consuming as repetitive tasks slow down the development cycle. Extended testing phases often delay releases and decrease market competitiveness.

• Resource allocation: Even if you automate some tests, the process still needs maintenance and updating test scripts. Now look: Your most skilled personnel are supposed to develop these scripts and frequently update them. Not the wisest resource allocation.

Common inefficiencies and hidden costs

Longer development cycles: Manual and script-based testing can be a bottleneck. This extends the overall development timeline and increases time-to-market.

Defect leakage and quality issues: Although manual testing comes across as thorough, traditional methods usually do not catch all defects, especially subtle or complex ones. And everything that makes it into production can cost you an arm and a leg in the future. Poor software quality cost US companies about USD 2.41 trillion in 2022.

ℹ️ Case in point: In 2018, Fiat Chrysler recalled 4.8 million vehicles due to a software defect affecting the cruise control system. The recall, repairs, and regulation penalties cost the company USD 175 million.

Opportunity costs: Time and resources spent on manual testing could be redirected toward innovation, feature development, or improving user experience.

Employee turnover: Poorly organized process, mundane tasks, and insufficient working opportunities usually annoy skilled people and lead to dissatisfaction among them. In this case, a turnover increase is inevitable. Hiring and training new employees introduce additional costs.

Inefficient use of skilled resources: Again, highly skilled testers don't like spending time on routine tasks rather than focusing on areas where their expertise would add more value.

The cost structure of autonomous testing

Initial investment

Autonomous software testing usually comes with a higher upfront investment compared to traditional testing methods. Let's break down it into key components:

1. Software acquisition

AI and ML capabilities in modern autonomous testing products usually come at a premium plan. Enterprise-level solutions range from USD 30,000 to USD 100,000 per year depending on features, scalability, and support services.

2. Setup and integration

Infrastructure costs: To implement AI-powered testing you need cloud services capable of handling intensive computational tasks. Estimated costs range significantly, but generally, medium-sized businesses should expect USD 10,000 to USD 30,000.

Integration efforts: The most influential thing in implementation is probably your existing development environments, CI/CD pipelines, and workflows. So, you may need to customize the AI testing tool and require additional professional services. This can cost approximately USD 15,000 to USD 25,000.

3. Training and skill boost

• Team training: Any time you integrate a new tool or framework, your team should get used to it first. There is training for this reason. But note: skill development programs or workshops could cost around USD 5,000 to USD 10,000 for a team.

• Specialized personnel: Hiring or contracting AI and machine learning experts to oversee the autonomous testing processes — add USD 100,000, give or take, annually (if we are talking about a full-fledged salary).

Total initial investment estimate

For a medium-sized business, the total initial cost is about USD 90,000 to USD 215,000.

Let's compare this to the traditional approach. It has its benefits, namely lower licensing fees and a vast number of open-source solutions. Initial costs for traditional test automation tools: USD 20,000 to USD 50,000 (excluding spending on building and maintaining test scripts).

Yes, the initial investment for autonomous testing is higher, but in the long run, it pays off. First, it reduces manual effort, second, it accelerates testing cycles, and finally, it enhances software quality.

Operational costs

Some news: Initial implementation is not all the costs you should take into account in your cost-benefit analysis. Good news: following operational costs of autonomous testing are generally lower and more predictable compared to the traditional model's costs.

Maintenance and updates

• Software maintenance fees: Annual maintenance and support fees are typically around 15-20% of the initial licensing cost. These fees cover software updates, support services, and access to new features (if any).

• System adjustments: You should expect some fine-tuning of the AI model from time to time as it will learn new things. Yet, these adjustments are often minimal and can be managed by current team members.

Infrastructure costs

Let's face it, cloud services are the most common for AI testing. On that, ongoing costs are also effortlessly scalable, and expenses are often offset by the reduced need for physical infrastructure.

Reduction in labor costs

• Almost without intervention: With AI-powered testing, there is no need to create test cases, execute them or even supervise, and maintain them.

• Smaller QA teams: The consequence from the previous point — you will be able to operate with fewer testers or reassign them to higher-value activities like exploratory testing.

• Labor cost savings: Also the consequence. For example, reducing a QA team by four members at an average salary of USD 90,000 will result in USD 360,000 annually.

Lower maintenance requirements

• Self-healing capabilities: Autonomous testing tools can adapt to changes in the application automatically, minimizing the need for manual script updates and reducing maintenance workloads.

• Consistent test execution: The processes ensure that tests are executed consistently, reducing errors and the need for rework.

Improved quality = cost savings

• Reduced defect leakage: Early and accurate defect detection prevents costly fixes after deployment. The cost to fix a production-found defect can be up to five times higher than if found during testing.

• Customer satisfaction and retention: Higher-quality software enhances user experience, leading to increased customer loyalty and revenue growth.

Key ROI drivers of autonomous testing

1. Efficiency gains

AI testing makes the typical test execution cycle much faster because it does not only automate tests, it makes the cycle almost independent. Here is how:

Independent test creation: Pre-trained AI model analyzes code changes and user behaviors and generates relevant test cases without manual scripting. This reduces the initial setup time for testing new features.

Parallel execution: Through distributed computing, it runs multiple tests simultaneously.

Intelligent test optimization: AI algorithms assess the level of the risk on a particular case, and then prioritize test cases based on this assessment. High-risk areas are tested first.

Self-healing capabilities: The adaptive system changes with each application modification and eliminates delays caused by broken test scripts.

Continuous testing integration: Once the code is changed, tests run automatically — this is what smooth integration with CI/CD pipelines looks like. This immediate feedback loop reduces the time between the development and testing phases.

Reduced maintenance effort: Traditional automated tests require frequent updates when the application changes, and next-gen testing tools make it automatically.

Time estimation comparison

💡Case study: Vodafone

An Italian branch of the global telecommunications company (362 million customers at that moment) implemented autonomous testing and marketing using Eggplant's AI-driven tools. The results:

• Reduced testing time: Regression testing cycles decreased from ten days to three days.

• Increased deployment frequency: Enabled more frequent releases, improving their responsiveness to market demands.

• Sales boost: 13% more sales (another topic, but still impressive).

2. Quality gains

Enhanced test coverage

The more cases you test, the fewer chances you'll get the product down during the possible peak (especially, when it comes to e-commerce projects).

• Comprehensive scenarios: Advanced tools generate more test cases than human QAs can assume because of more data they are learned on. This also involves edge cases.

• Average coverage increase: The average test coverage of autonomous testing is over 90%. We'd say it's a real game-changer.

Early defect detection and its financial impact

• Shift-left testing: Continuous testing throughout the development cycle catches defects earlier when they are less costly to fix.

• Defect reduction: Early detection can reduce defects in production by up to 30%, according to industry studies.

• Reduced post-release bug fixes: Lower costs associated with emergency patches and customer support.

• Fewer support issues: Decreased volume of customer complaints and support tickets related to software defects.

• Increased customer satisfaction: Higher-quality software leads to better user experiences, enhancing brand reputation and customer loyalty.

💡Case study: Netflix

Netflix uses over 40,000 long-run tests every day on internal and external devices all over the world. The company established Netflix Test Studio to allow internal and external QA professionals to test the platform ensuring a smooth experience. Main gains:

• Improved test coverage: Over 95% test coverage across applications.

• Reduced downtime: Minimized service disruptions, leading to higher customer satisfaction.

• Enhanced user experience: Consistently delivered high-quality streaming, contributing to subscriber growth.

3. Scalability and resource optimization

Software testing investment makes sense when gains overcome spending, not just offset them. There are two ways for this reason: increase outcome and optimize spending (of any kind: human resources, time, money, etc.).

AI-powered testing tools cover each mentioned aspect. They manage thousands of test cases across multiple applications and platforms without proportional increases in resources. And the resource allocation is also smart enough to get adjusted according to changing needs: workload, shortened deadlines, budget cuts/increases, etc.

Adaptability to evolving systems

• Continuous integration support: Integrators are one of the in-demand specialties now, and proper tuning of a new testing tool to functionalize with the existing workflow requires additional support. Emerging technologies like AI have such communities and a wide market of appropriate pros.

• Support for new technologies: Technologies are still evolving faster than any other field and seamless “co-existence” with new worthy tools or systems is becoming a new challenge. AI testing tools handle this perfectly.

Optimizing human resources

• Focus on high-value moves: QA teams can shift their attention from repetitive test execution to strategic activities like exploratory testing, risk assessment, and test strategy development.

• Boosted productivity: Testers utilize their expertise more effectively and really try to make a fresh impact. This way, you can hit up to a 30% increase in productivity.

• Manual efforts shrink: Labor cost savings can show up to 40% level after adopting autonomous testing.

Quantifying the ROI of autonomous testing

What ROI calculation models exist?

The ROI of autonomous testing is identified in the same way as any other tech activity, just with several nuances that determine the calculation model. The basic formula estimates the costs of implementation against the financial benefits gained over time.

1. Basic ROI Formula

• Total benefits: You changed out the testing system to an AI-powered one. The financial gains triggered by this change will condition your return on investment.

• Total costs: The sum of initial investments and ongoing operational expenses.

2. Payback period

Calculates the time required to recover the initial investment from the net cash inflows.

3. Net Present Value (NPV)

• Net Cash Inflow_𝑡: Cash inflow at time 𝑡

• r: Discount rate

• 𝑛: Number of periods

Model recommendation for startups

The basic ROI formula often better fits startups' objectives due to its simplicity and ease of application. They typically need quick insights without complex financial modeling, and the basic formula provides a clear percentage that can guide investment decisions.

Basic ROI calculation: Example

Company: A mid-sized software development organization.

Initial investment:

• Autonomous testing tool acquisition and setup: USD 100,000

• Training and integration: USD 20,000

• Total initial investment: USD 120,000

Operational costs (annual):

• Maintenance and updates: USD 15,000

• Infrastructure costs: USD 10,000

• Total annual operational costs: USD 25,000

Benefits (annual):

• Labor cost savings: Less need for manual QAs, therefore rough saving is USD 200,000

• Efficiency gains: Faster time-to-market; roughly an additional USD 100,000 in revenue

• Quality improvements: Reduced last-minute firefighting saves USD 50,000 (considering support and rework costs)

• Total annual benefits: USD 350,000

Eventually — ROI

First-year total costs:

Initial investment: USD 120,000

Operational costs: USD 25,000

Total costs: USD 145,000

First-year total benefits: USD 350,000

Net Profit: Total benefits - Total costs = USD 350,000 - USD 145,000 = USD 205,000

ROI = (USD 145,000 / USD 205,000)×100% ≈ 141%

Conclusion: Our conditional company gains about 141% return on their investment in the first year. Note: this is a very optimistic example, exaggerated to ostensibly show the best-case scenario.

Comparative analysis: Traditional testing vs. autonomous testing

Autonomous testing ROI rises in these cases

Strategic benefits beyond financial ROI

Competitive advantage

Innovation and creative utilization of existing resources come before market share and leadership. And autonomous testing empowers companies to innovate faster.

The deduction chain looks like this: testing is a time-consuming and attention-demanding process → AI speeds up testing without sacrificing quality → the development team can focus on creating genuinely new features → management can count on the dev and the testing teams and free their attention for strategic improvements.

All these moves result in both product advancement and brand strengthening, which, basically, are advantages themselves.

💡AI testing in Google

Google is one of the most innovative companies. They tested (and failed) thousands of projects. But some of them brought about a revolution in the industry, even if this revolution was only internal. In the early 2000s, Google introduced the ToT (Testing on the Toilet) concept to its developers.

The best practices in the bathroom featured upcoming advancements in the software development and testing industry.

Including machine learning algorithms and future computing techniques, the giant ends up reducing testing time and catching up with Apple products, sometimes even giving the “Job's child” a head start.

Risk mitigation

Autonomous testing secures identifying defects, security flaws, and compliance issues. In particular, AI algorithms can detect patterns and anomalies that might be overlooked by manual testing.

Imagine the anxiety of releasing a new software update, only to discover a critical bug that compromises customer data. The fallout could be catastrophic — not just in financial terms but also in lost trust and damage to the brand's reputation.

💡Equifax Data Breach

The middle 2010s brought a bright case of neglecting security and testing.

Equifax encountered a massive data breach that exposed the personal information of 147 million people. The breach wouldn't have happened if the company's teams had tested the system more thoroughly (because actually the vulnerability was known). The results:

• Costly consequences: The company faced over USD 1.4 billion in costs related to the breach, additionally allocated USD 425 million to people affected by the breach.

• Impact on reputation: Trust in the brand was severely damaged.

Peace of mind and brand protection

Another side of the autonomous testing monad is peace of mind. By ensuring that software is thoroughly tested and reliable, an AI-powered testing approach in some senses protects your mentality and the very essence of your brand.

Imagine the relief of knowing that your software performs flawlessly under pressure, that your customer's data is secure, and that your brand step-by-step is becoming a lovemark. This confidence is almost equal to completed ambitious goals and the absence of unexpected failures.

The trust factor

• Customer confidence: Every successful transaction, every smooth user experience, builds trust. One major glitch can erase years of reputation building.

• Team morale: Development teams can innovate confidently, knowing they have a reliable testing infrastructure backing them up.

• Stakeholder alignment: From investors to executives, knowing that your software is protected by sophisticated autonomous testing provides invaluable reassurance.

Bottom line

The ROI of autonomous testing is not equal just financial returns. Also, financial returns are not immediate. Moreover, the average return on AI-based projects in 2023 was only 5.9%. Yet, companies implementing autonomous testing achieve increased ROI compared to those stuck to traditional testing.

The cost-benefit analysis demonstrates that while initial investments range from USD 50,000 to USD 100,000, the long-term savings in testing time (80% reduction), maintenance costs (15-20% annual savings), and defect detection (30-40% improvement) create compelling value propositions.

Why? Faster innovation, enhancing product reliability, optimizing resource utilization — autonomous testing positions companies to adapt swiftly to market changes and customer demands.

OwlityAI is a next-gen autonomous testing tool that offers advanced capabilities beyond traditional test automation: intelligent test generation, self-healing tests, and seamless integration with development workflows.