Analyst(s): Keith Kirkpatrick
Publication Date: November 20, 2024
The 2024 World Quality Report reveals that Generative AI (Gen AI) has become a cornerstone in modern Quality Engineering (QE), with 68% of organizations actively using or planning its adoption. The report highlights key trends, including enhanced test automation, the need for upskilling, and sustainability challenges within QE practices. It emphasizes the strategic role of Gen AI in achieving business objectives and fostering innovation in software development.
What is Covered in this Article:
- The adoption and impact of Generative AI in Quality Engineering
- Challenges organizations face with automation and legacy systems
- The critical importance of upskilling QE teams
- Insights into sustainability practices in software development
- Strategic implications for businesses leveraging Gen AI
- Future trends in Quality Engineering and the role of AI-driven solutions
The News: The 2024 World Quality Report, produced by OpenTextâ„¢ in partnership with Capgemini and Sogeti, highlights the increasing impact of Generative AI in Quality Engineering (QE). The survey indicates that 68% of firms currently utilize or intend to adopt Generative AI.
This technology is changing QE by expediting test automation, improving end-to-end testing strategies, and encouraging innovation across sectors. Nonetheless, obstacles persist, such as the necessity for comprehensive upskilling programs, overcoming legacy system constraints, and incorporating sustainability into software development methodologies.
Generative AI Revolutionizes Quality Engineering: Insights from the 2024 World Quality Report
Analyst Take: The 2024 World Quality Report underscores a critical crossroad for QE. Gen AI has transitioned from a future aspiration to a current reality, facilitating enterprises’ attainment of expedited automation, minimizing errors, and enhancing software quality.
Nonetheless, its full potential depends on addressing critical challenges: upskilling engineers, overcoming automation challenges, and synchronizing quality measures with business results. Despite its slow progress, sustainability offers substantial potential for firms to incorporate green IT practices into QE, generating long-term economic and environmental value.
How GenAI Affects Quality Engineering
The adoption of Gen AI has ushered in a new era for QE. The report indicates that 34% of organizations actively utilize Gen AI, while another 34% are in the planning stages following successful pilot programs. Its primary impact is test automation, where 72% of respondents report faster automation processes due to AI integration.
Gen AI excels at generating code and test scripts, streamlining what were once labor-intensive tasks. This automation accelerates the software development life cycle and reduces human error, improving the quality of deliverables. With organizations increasingly adopting agile and DevOps methodologies, the ability to test and deploy software quickly has become a competitive advantage.
Additionally, the use of Gen AI is enhancing end-to-end testing strategies. Modern software systems are more interconnected and complex than ever, requiring comprehensive validation across the entire ecosystem. AI tools offer predictive capabilities, enabling engineers to efficiently identify vulnerabilities and optimize test coverage. This ensures robust performance in real-world conditions, a critical factor in maintaining customer satisfaction and trust.
Redefining Metrics and Strategic Alignment
As AI tools redefine the scope of QE, organizations must rethink their approach to measuring success. Traditional QE metrics often focus on technical outputs, such as defect detection rates or code coverage. However, the report highlights the importance of aligning these metrics with business outcomes to showcase the strategic value of QE initiatives.
For example, by measuring the impact of quality improvements on customer retention, organizations can demonstrate the tangible benefits of investing in advanced testing solutions. Similarly, aligning QE goals with broader corporate objectives, such as time-to-market reduction, operational cost savings, or improving corporate reputation scores, reinforces its role as a driver of business success. This strategic alignment ensures that quality remains a priority, even as organizations adopt innovative tools and methodologies.
The Importance of Upskilling
The rapid adoption of Gen AI has also emphasized the need for continuous learning and skill development within QE teams. According to the report, 82% of organizations have established dedicated learning pathways for their engineers. However, only 50% actively monitor the effectiveness of these programs, suggesting a need for more robust evaluation mechanisms.
Upskilling in areas such as AI tool management, agile methodologies, and cross-functional collaboration is essential for effectively leveraging Gen AI. Engineers must be equipped to interpret AI-generated outputs and make informed decisions based on data-driven insights. Furthermore, fostering collaboration between QE teams and other departments, such as development and operations, ensures a cohesive approach to software quality.
Organizations prioritizing upskilling are better positioned to navigate the complexities of AI-driven QE. By investing in workforce development, they can build teams capable of adapting to technological advancements and driving innovation in their respective industries.
Addressing Automation Challenges
Despite Gen AI’s promise, systemic challenges often hinder automation efforts. The report reveals that 57% of organizations struggle with a lack of comprehensive test automation strategies. Moreover, 64% cite reliance on legacy systems as a significant barrier to progress.
While integral to many organizations, legacy systems often lack the flexibility required to integrate with modern automation tools. This creates bottlenecks in testing workflows, limiting the scalability and effectiveness of Gen AI solutions. Organizations must adopt a phased approach to modernization to address this issue, gradually replacing outdated systems with more adaptable technologies.
In parallel, developing robust automation strategies is crucial for overcoming inefficiencies. These strategies should include clear guidelines for tool selection, workflow integration, and performance evaluation. By addressing these foundational challenges, organizations can unlock the full potential of AI-driven automation and ensure consistent quality across their software portfolios.
Sustainability in QE
While innovation is at the forefront of QE, sustainability remains an area where progress is needed. The report highlights a concerning gap between awareness and action: only 25% of organizations measure the environmental impact of their IT development activities, and just 44% track the sustainability of testing processes. Furthermore, only 34% are actively implementing sustainable QE practices.
Sustainability in QE can be achieved through several means. For instance, optimizing testing processes to reduce resource consumption lowers operational costs and minimizes software development’s carbon footprint. Gen AI tools can play a pivotal role in achieving these goals with their ability to streamline workflows.
However, organizations must adopt comprehensive Green IT strategies to drive meaningful change. This includes setting clear sustainability targets, tracking progress through standardized metrics, and fostering a culture of environmental responsibility. Organizations can contribute to a more sustainable digital ecosystem by aligning QE practices with broader sustainability objectives.
Looking Forward: The Future of QE
The integration of Gen AI marks a turning point in QE, redefining how organizations approach software development and testing. As AI tools become more sophisticated, they will enable engineers to focus on higher-value tasks, such as strategy development and customer engagement.
However, the successful adoption of Gen AI requires a holistic approach. Organizations must address upskilling, automation, and sustainability challenges while ensuring their QE strategies align with broader business objectives. By doing so, they can unlock the full potential of AI-driven innovation, driving long-term success in an increasingly digital world.
The 2024 World Quality Report offers a roadmap for navigating this transformation. Its insights highlight the critical interplay between technology, talent, and strategy, providing a blueprint for organizations seeking to thrive in the next era of QE. By embracing these principles, businesses can ensure that quality remains at the heart of their operations, delivering exceptional value to customers and stakeholders.
What to Watch:
- Organizations are expanding the use of Gen AI in test automation and beyond.
- Monitor advancements in training programs and their impact on QE team performance.
- Observe how businesses address legacy system constraints and develop comprehensive automation strategies.
- Increased adoption of green IT strategies and their impact on software development.
- Stay tuned for innovative uses of Gen AI in areas such as predictive analytics and compliance automation.
See the complete press release on the world quality report on the OpenText website.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Other insights from The Futurum Group:
OpenText’s Q1 FY2025 Earnings Highlight Growth in Cloud and AI Solutions
OpenText Announces New Enhancements in Its Cloud Edition 24.3 Release
OpenText Axcelerate Integrates AI-Power via Aviator
Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
