The World Quality Report 2024-25 by OpenText sheds light on groundbreaking trends shaping Quality Engineering (QE) and testing practices globally. With over 1,775 executives surveyed across 33 countries, the report uncovers how AI, automation, and sustainability are transforming the landscape of quality assurance. As AI technology progresses, organizations are being called to adopt latest, modern solutions for QE, especially as Generative AI (Gen AI) takes center stage.
We are going to explore the report’s findings, emphasizing key trends in QE, automation, and AI, and providing actionable insights for organizations able to embrace the long run of quality engineering.
The Rise of AI in Quality Engineering
Certainly one of the report’s least striking revelations is the rapid adoption of AI in QE. A staggering 71% of organizations have integrated AI and Gen AI into their operations, up from 34% in previous years. This shift marks a pivotal moment within the industry, with AI set to revolutionize various elements of QE, from test automation to data quality management.
AI’s impact is especially profound in test automation, where 73% of respondents cite AI and machine learning (ML) as key drivers of progress. Cloud-native technologies and robotic process automation (RPA) follow closely behind, with 67% and 66%, respectively, leveraging these advancements. The speed and efficiency of automation are improving dramatically, allowing organizations to scale back manual efforts and increase testing scope.
For example, 72% of organizations report that Gen AI has accelerated their test automation processes, while 68% highlight easier integrations, enabling a seamless fit into existing development pipelines. By automating repetitive tasks and generating test scripts, AI will not be only reducing costs but additionally enhancing the productivity of quality engineers.
Quality Engineering in Agile: A Shift Towards Integrated Teams
The growing importance of embedding QE into Agile teams is one other major trend highlighted by the report. Currently, 40% of organizations have quality engineers integrated directly into their Agile workflows. This shift is a transparent move away from traditional Testing Centers of Excellence (TCoEs), which have declined in use, now comprising only 27% of respondents’ QE structures, in comparison with a staggering 70% in previous years.
The give attention to embedding QE inside Agile teams ensures faster iterations and higher alignment with business goals. Moreover, cross-functional collaboration is recognized as critical for delivering higher-quality results, with 78% of respondents emphasizing its importance in ensuring higher quality products faster.
Despite these advances, challenges remain. The report finds that 56% of organizations still view QE as a non-strategic function, and 53% acknowledge that their current QE processes are insufficient for Agile methodologies. This calls for a more significant give attention to aligning QE metrics with broader business outcomes, similar to customer satisfaction and revenue impact.
Data Quality: The Foundation for AI-Driven Testing
As organizations develop into more reliant on data-driven decision-making, the standard of their data takes on heightened importance. The report reveals that 64% of organizations now consider data quality a top priority, but many are still grappling with effectively manage it. Establishing clear ownership of knowledge and improving frameworks for data governance are essential steps toward ensuring the accuracy and reliability of AI models utilized in QE.
Without high-quality data, AI’s ability to generate meaningful insights, create test scenarios, and predict outcomes is compromised. This explains why 58% of respondents rank data breaches as essentially the most significant risk related to Gen AI. As organizations integrate AI into their quality processes, ensuring robust data security becomes paramount.
Intelligent Product Validation: Testing Beyond Functionality
The validation of intelligent products is emerging as a critical component of contemporary QE practices. In line with the report, 21% of testing budgets are actually dedicated to validating smart technologies, reflecting the growing need for comprehensive strategies to make sure these products perform seamlessly in interconnected environments.
Functional correctness stays the highest priority for validating intelligent products, with 30% of respondents citing it as a very powerful factor. Nevertheless, security (23%) and data quality (21%) also rank highly, signaling a shift toward more holistic testing strategies that address the complexity of smart products.
The report also identifies challenges in testing these products, particularly relating to the validation of embedded AI models and the flexibility to check all integrations across devices and protocols. A scarcity of expert testers further exacerbates these challenges, with 44% of organizations struggling to seek out talent able to handling the intricacies of intelligent product testing.
Sustainability in Quality Engineering
With the rising concerns over climate change and environmental responsibility, 58% of organizations are prioritizing sustainability inside their QE strategies. Nevertheless, only 34% have implemented practices that measure the environmental impact of their testing activities. This highlights a big gap between intent and execution, underscoring the necessity for more robust frameworks to trace sustainability efforts.
Organizations are starting to explore how QE can contribute to Green IT initiatives, with areas similar to energy consumption monitoring, environmental data evaluation, and optimization of test environments gaining traction. AI can play a pivotal role in these efforts, with 54% of respondents identifying energy efficiency optimization as probably the most worthwhile uses of AI in quality validation.
Key Recommendations for the Future
The report offers several key recommendations for organizations trying to stay competitive within the evolving QE landscape:
- Leverage Gen AI for Automation: Start experimenting with Gen AI to reinforce and speed up test automation processes. Gen AI’s potential extends beyond script generation, offering opportunities for self-adaptive automation systems that may boost each efficiency and effectiveness.
- Spend money on QE Talent: To maintain pace with AI and automation, organizations must spend money on upskilling their quality engineers. Full-stack engineers, able to working across all the software lifecycle, are increasingly in demand.
- Give attention to Business Performance Metrics: Shift away from traditional metrics like process efficiency and test coverage. As an alternative, give attention to how QE initiatives contribute to business outcomes, similar to customer satisfaction and revenue growth.
- Develop a Sustainability Strategy: Implement comprehensive processes to measure and reduce the environmental impact of QE activities. Integrating sustainability into testing is not going to only advance corporate social responsibility goals but additionally improve operational efficiency.
Conclusion
The World Quality Report 2024-25 paints a vivid picture of an industry on the cusp of transformation, driven by AI, automation, and sustainability. As organizations navigate this latest landscape, adopting a forward-thinking approach to QE might be essential to gaining a competitive edge. By leveraging AI’s potential, investing in talent, and aligning quality initiatives with business goals, firms can ensure they’re prepared for the challenges and opportunities that lie ahead.
