According to the 2026 State of DevOps Report: AI in Testing Edition, AI-augmented testing has become a standard practice, delivering significant gains in speed and quality. 44% of organizations report using AI-powered tools integrated directly into existing platforms and 31% report using AI-native tooling.
However, many organizations still face challenges with cost and governance as they scale their AI initiatives, with 51% of organizations reporting increased total costs. Meanwhile, cost visibility is uneven, with 43% reporting comprehensive tracking of AI costs and 40% reporting partial tracking.
For Tabcorp, a leader in the Australian gambling and entertainment market, these challenges were all too familiar. With a massive digital footprint and constant UI updates, their traditional QA processes struggled to keep up. By adopting AI in QA, Tabcorp not only overcame these hurdles but achieved measurable business outcomes. They secured a 90% reduction in maintenance efforts and realized six-figure cost savings.
This post explores how Tabcorp used AI-driven automation to transform their testing strategy. You will also discover actionable lessons that your business can apply to guarantee a strong return on investment.
Back to topThe QA Bottleneck: Challenges That Demand AI
Maintaining a competitive edge during software delivery requires a rigorous testing strategy that ensures a flawless user experience. Yet, as digital portfolios expand, traditional test automation often creates resource bottlenecks. Tabcorp encountered several critical roadblocks that diminished the value of their testing efforts.
The Maintenance Trap
Traditional automation relies heavily on static locators. When developers update the user interface, these locators break. For Tabcorp, the rapid pace of UI and UX changes turned automation into a relentless cycle of rework.
The team spent an average of two days every week simply maintaining existing automation scripts. This constant repair work diverted valuable engineering resources away from innovation and test expansion.
Talent Shortages and Skill Gaps
Finding talent with specialized coding knowledge presents a significant challenge in many local markets. Tabcorp found it difficult to hire engineers with the deep technical expertise required to build and maintain complex automation frameworks.
When they did hire new talent, the onboarding process was incredibly slow. New team members required up to three weeks of training before they could contribute meaningful scripts to the pipeline.
Regression Fatigue
As maintenance demands grew, manual regression cycles expanded. Frequent updates to the application caused standard locators to fail, leading to high volumes of false positives. These unstable tests forced the QA team into lengthy manual validation processes. Full regression testing cycles stretched up to 10 days, severely delaying software releases and frustrating management.
The ROI Dilemma
Ultimately, the return on investment for traditional automation began to vanish. Last-minute code changes frequently broke test cases right before a release. The testing suite became unusable exactly when the team needed it most. The automation strategy became a liability rather than a business advantage, prompting the urgent need for AI in QA.
Back to topHow AI in QA Enhanced Tabcorp’s Testing Strategy
Recognizing that traditional methods were no longer sustainable, Tabcorp partnered with Perfecto to modernize its infrastructure. By adopting advanced AI in QA, they shifted their focus from writing code to validating user experiences.
From Reactive to Proactive Testing
Tabcorp eliminated the maintenance burden by deploying locator-free tests. AI in QA automatically detects and updates changed elements without requiring manual script repair. When developers move a button or alter a field, the AI understands the context and adapts the test dynamically. This self-healing capability ensures tests remain highly resilient, even during rapid release cycles.
Empowering Teams with Natural Language
The introduction of natural language test creation democratized the automation process. Instead of writing complex code, testers can use simple prompts, such as instructing the system to log in with a username and password. This feature allows manual QAs and business analysts to build automated tests. By lowering the technical barrier to entry, Tabcorp expanded its testing capacity without needing to hire specialized developers.
Smarter, Context-Aware Validation
Traditional frameworks validate precise, static data points. AI-driven automation validates the application based on visual context and user intent. The system intelligently handles dynamic content and responsive user interface behaviors. By looking at the broader business context rather than strict code implementation, the AI delivers a much more accurate assessment of the customer journey.
Back to topHow Can Organizations Measure the Impact of AI in QA?
The implementation of intelligent testing features delivered immediate and profound business results, from massive efficiency gains and cost savings to accelerated release cycles and simplified onboarding.
Tabcorp revitalized its entire QA operation, turning a former bottleneck into a driver of enterprise speed and quality.
Massive Efficiency Gains
By eliminating the need to fix broken scripts constantly, Tabcorp reduced maintenance time by 90%. The time spent repairing automation dropped from two full days a week to less than half a day. Furthermore, test authoring time accelerated by 60%. This streamlined creation process allowed the team to automate minor test cases that they previously ignored due to resource constraints.
Accelerated Regression Cycles
With self-healing tests and smarter validation, regression testing no longer held back deployment schedules. Tabcorp shortened its full regression cycles by 70%. A process that once took 10 exhaustive days now requires only three to four days to complete. This acceleration guarantees that high-quality software reaches the market faster.
Six-Figure Cost Savings
Efficiency directly translates to financial success. By reducing engineering hours spent on manual maintenance and accelerating test creation, Tabcorp achieved $150K to $200K in cost savings within just three months. Over a year, this strategy unlocks massive six-figure savings, proving that AI in QA delivers a tangible, measurable return on investment.
Onboarding Simplified
The transition to natural language authoring completely transformed the hiring and onboarding experience. Because the platform relies on plain English rather than complex scripting languages, new team members can contribute on their very first day. The cumbersome three-week learning curve vanished entirely, allowing Tabcorp to scale its QA efforts effortlessly.
Back to topWhat are Top Tips for Businesses Adopting AI in QA?
Important considerations for businesses adopting AI in QA include:
- Start small, but strategic.
- Run AI-driven tests and legacy scripts in parallel.
- Shift testing philosophy to focus on business outcomes.
- Invest in team enablement.
- Track metrics diligently.
Tabcorp’s success proves that modernizing your testing strategy yields massive dividends. However, adopting new technology requires careful planning. Here are the actionable lessons you can apply to your own organization.
Think Big, Start Small
Do not attempt to overhaul your entire testing framework overnight. Tabcorp began with a strategic pilot phase, selecting 10 to 15 high-value regression journeys to automate first. This controlled environment allowed them to prove the concept, secure management buy-in, and ensure consistent results before expanding the program.
Hybrid Models Work Best
Transition gradually to minimize operational risks. Tabcorp utilized a framework coexistence model. They ran new AI-driven tests in parallel with their existing legacy scripts. As they proved the reliability of the AI tools, they systematically retired the old scripts. This hybrid approach ensures continuity and protects your existing automation investment.
Focus on Business Outcomes
Shift your testing philosophy from code-centric validation to business-centric automation. Use AI to evaluate the actual customer journey and user experience. When you align your testing metrics with business goals—such as defect escape rates and lead time to release—you demonstrate the true strategic value of your QA department.
Invest in Team Enablement
Introduce the new tools to your team gradually so they can build comfort and confidence. Tabcorp provided their QA team with the user interface to play around with prompt-based authoring. By familiarizing your staff with the new workflows, you reduce resistance to change and empower them to leverage the technology effectively.
Measure and Broadcast Success
Track your performance metrics diligently. Document your maintenance reduction, regression cycle times, and overall cost savings. Sharing these concrete numbers with leadership justifies the investment and secures ongoing funding for your quality assurance initiatives.
Back to topThe Future of AI in QA
Artificial intelligence continues to evolve, bringing even more sophisticated capabilities to software testing. Organizations will soon leverage AI to handle highly personalized user journeys, complex dynamic data, and predictive defect analytics. Technology will shift testing even further left, catching vulnerabilities before code ever reaches a testing environment.
Tabcorp views their current implementation as just the beginning. They plan to continue migrating their massive repository of legacy test cases to the AI platform. By viewing AI in QA as a long-term investment, businesses build scalable, future-proof automation practices that adapt to rapidly changing market demands.
Back to topElevating QA with AI
Your automation strategy should accelerate your business, not hold it back with endless maintenance and brittle scripts. Tabcorp’s journey illustrates the transformative power of intelligent automation. By embracing AI in QA, they slashed maintenance efforts by 90%, cut regression cycles drastically, and saved hundreds of thousands of dollars.
You can replicate these results by starting small, employing a hybrid migration strategy, and empowering your team with intuitive tools. Adopt AI-driven testing today to eliminate inefficiencies, accelerate your development cycles, and achieve a higher standard of digital excellence.