The software testing landscape has reached a defining moment. The 16th edition of the World Quality Report, released in October 2024 by Capgemini, Sogeti, and OpenText, reveals that quality engineering is no longer a technical afterthought—it’s a strategic imperative driving business transformation. Based on insights from 1,775 senior leaders across 33 countries and 10 industries, this year’s report, themed “New Futures in Focus,” uncovers how organizations are harnessing AI, automation, and data quality to redefine software excellence.
Generative AI: From Experimentation to Essential Practice
The most striking revelation from this year’s report is the rapid adoption of Generative AI in quality engineering. A remarkable 68% of organizations are now actively using Gen AI (34%) or have developed implementation roadmaps following successful pilots (34%). This represents a seismic shift from just one year ago when only 10% planned adoption—that figure has jumped to 25% in 2024.
Test automation leads the charge, with 72% of respondents reporting faster automation processes due to Gen AI integration. The technology is revolutionizing how teams generate test cases, extract test data, and reduce manual workloads. Large Language Models and AI tools like GitHub Copilot are seamlessly integrating into software development lifecycles, ushering in unprecedented efficiency gains.
Yet the report emphasizes a crucial balance: 71% of organizations have integrated AI and Gen AI into operations, but the expertise and proactive involvement of quality engineers remain critical to success. AI is a partner in the testing process, not a replacement for human judgment.
Data Quality Emerges as Mission-Critical
In an age where AI models are only as good as the data they consume, quality engineering teams are confronting a fundamental truth: garbage in, garbage out. The report reveals that 64% of organizations now rate data quality as critically important or of very high priority. This focus has intensified as organizations realize that poor data quality undermines AI effectiveness, regulatory compliance, and customer trust.
The stakes have never been higher. As businesses rely increasingly on data-driven decision-making, ensuring data accuracy, completeness, and consistency has become a strategic differentiator. Quality engineers are now tasked with validating not just code, but the data pipelines that feed intelligent systems.
The Automation Paradox: Progress Amid Persistent Challenges
Automation continues its steady march forward, with organizations now averaging 44% of testing automated—a meaningful increase that reflects sustained investment. However, the journey is far from complete. The report identifies significant barriers holding teams back:
- 57% cite lack of comprehensive test automation strategies as a key obstacle.
- 64% struggle with legacy systems that resist modern automation frameworks.
- Only 50% actively track the effectiveness of their upskilling programs, despite 82% offering dedicated learning pathways.
These findings underscore a critical gap: while organizations invest in automation tools and training, they often lack the strategic alignment and measurement practices needed to maximize returns. The path forward requires not just technological adoption, but organizational transformation.
Quality Engineering in Agile: Bridging the Strategic Gap
A troubling disconnect emerges when examining how organizations perceive quality engineering’s role. While leadership increasingly acknowledges QE’s importance, there remains a significant gap between perception and strategic action. The report highlights that organizations must better align QE metrics with business outcomes to showcase its true value.
Key challenges in embedding quality engineering within Agile processes include insufficient automation, skills gaps among quality engineers, and slow QE workflows that bottleneck delivery. The solution lies in adopting product-aligned quality engineering structures that integrate quality engineers directly into product teams rather than isolating them in traditional Testing Centers of Excellence (TCoE). Notably, only 27% of organizations still rely on traditional TCoEs, signaling a structural shift.
The Sustainability Blind Spot
Despite widespread recognition of sustainability as a corporate priority, the report uncovers a troubling implementation gap. Only 25% of organizations measure the environmental impact of their overall IT development, while 44% track testing activities specifically. Even more concerning, just 34% implement efficient quality engineering practices to drive sustainability.
This disconnect reveals limited awareness among IT staff regarding sustainability targets—a gap that demands improved education and strategic alignment. As regulatory pressures intensify and stakeholders demand accountability, quality engineering must evolve to include environmental impact as a core metric.
Strategic Imperatives for 2025 and Beyond
The World Quality Report 2024-25 delivers clear guidance for organizations seeking to elevate their quality engineering capabilities:
Embrace Gen AI Strategically: Experiment with AI-powered test generation and data extraction, but maintain human oversight. Organizations that successfully integrate Gen AI will gain competitive advantages in speed, accuracy, and coverage.
Invest in Data Quality Infrastructure: Build robust data validation frameworks that ensure AI systems operate on reliable inputs. This requires cross-functional collaboration between data engineers, quality teams, and business stakeholders.
Develop Comprehensive Automation Roadmaps: Move beyond tactical tool adoption to strategic automation strategies that align with business objectives. Measure effectiveness rigorously and iterate based on outcomes.
Prioritize Continuous Learning: Upskill quality engineers in Gen AI, Agile integration, and cross-functional collaboration. Track the ROI of training programs to ensure they deliver measurable improvements.
Embed Quality Engineering Structurally: Break down silos by integrating quality engineers into product teams. This proximity enables faster feedback loops and stronger alignment with business goals.
Address the Sustainability Gap: Implement Green IT strategies that measure environmental impact across the software development lifecycle. Make sustainability a shared responsibility rather than an afterthought.
Looking Ahead: Quality as Competitive Advantage
The quality engineering landscape is evolving faster than ever. Organizations that view QE as merely testing human-written code will fall behind. The future belongs to those who recognize quality engineering as a strategic function that spans AI-generated code, end-to-end software chains, and environmental responsibility.
As Mark Buenen, Global Leader of Quality Engineering & Testing at Sogeti and Capgemini, notes: “Gen AI tools and solutions are clearly gaining adoption by quality engineers to assist them in their function and focus on higher-value-added tasks”. The transformation is underway—those who adapt will thrive, while those who resist will struggle to compete in an increasingly demanding digital landscape.
The World Quality Report 2024-25 makes one thing abundantly clear: quality is no longer negotiable. It’s the foundation upon which digital transformation, customer trust, and business success are built.