Quality Reimagined: Capgemini’s Vision for AI-Driven Engineering Excellence

As Artificial Intelligence (AI) continues to redefine the technology landscape, its transformative impact on quality engineering (QE) is both profound and far-reaching. From automating complex test processes to predicting defects before they occur, AI is reshaping the way software is developed, tested, and delivered. Dr. Sudatta Kar, Vice President & Head of Engineering at Capgemini Engineering India, shares a compelling view on how AI—particularly Generative AI—is driving a paradigm shift in QE practices. In this exclusive interaction with Express Computer, Dr. Kar explores the emerging trends in AI-driven quality assurance, the integration of AI into agile and DevOps ecosystems, and the evolving role of engineers in this new era of intelligent automation

Some edited excerpts:

How is AI reshaping quality engineering practices today?
AI is significantly transforming quality engineering (QE) by enhancing test automation, improving test accuracy, increasing test coverage, enabling early defect detection, and accelerating testing processes. The shift towards AI-driven QE practices lead to more efficient, reliable, and scalable testing processes.

Generative AI (Gen AI) enables the creation of test data, automated test case & test scripts, self-adaptive test automation systems, and predictive analytics for defect detection.

AI-driven tools are being integrated into various QE activities such as data generation, test reporting, and defect analysis. Usage of Gen AI and AI-driven tools streamlines QE workflows and reduces manual interventions. AI is becoming the core of next-generation QE, augmenting human expertise to deliver high-quality software more efficiently.

What key trends are emerging during the implementation of AI-driven tools for quality assurance? How are these tools expected to impact the cost and speed of testing in the long term?
The key trends in AI-driven quality assurance include the adoption of Gen AI for test data/script generation & test automation, the use of agentic AI in self-healing scripts to adapt to software changes, the integration of AI in continuous integration/continuous deployment (CI/CD) pipelines, self-healing scripts adapting to application changes and smart test data generation for comprehensive coverage, and the application of AI for predictive analytics and defect detection.

These tools are expected to significantly reduce testing costs by automating repetitive tasks and minimizing the need for manual interventions. Additionally, AI-driven tools enable early defect detection, enhanced code coverage, speed of testing by generating test scripts, real-time data analysis, and more efficient defect identification and resolution.

In the long term, these advancements will lead to a reduction in cost of quality (COQ), faster go-to-market (GTM), and improved user satisfaction.

How are companies incorporating AI into agile and DevOps practices within quality engineering?
Companies are embedding AI into agile and DevOps practices by integrating AI-driven tools into their CI/CD pipelines and agile workflows.  AI is used to test data generation including comprehensive set of boundary conditions, automate test case generation, enhance test data management, and provide real-time analytics for continuous testing.

This integration allows faster feedback loops, improved test coverage, and more efficient defect detection and resolution. By leveraging AI, companies can achieve higher levels of automation and maintain the agility and speed required in DevOps environments.

What ethical or data privacy concerns may arise while using AI in quality engineering?
The use of AI in quality engineering raises several ethical and data privacy concerns, including the risk of data breaches, the potential for biased AI models, and the challenge of ensuring data integrity. AI-driven tools often require access to large datasets, which can include sensitive information.

Ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR), and implementing robust data security measures are critical to mitigating these risks. Additionally, addressing biases in AI models and ensuring transparency in AI-driven decision-making processes are essential to maintaining ethical standards in QE.

While AI offers substantial benefits, challenges such as ensuring output reliability, data privacy, mitigating bias, and evolving QE skillsets must be addressed for successful adoption.

How is the role of engineers evolving with the rise of AI automation in QE?
With the rise of AI automation in QE, the role of engineers is evolving to focus more on strategic and complex tasks. AI-driven tools are expected to do most of the heavy lifting, while engineers are increasingly required to have expertise in AI/ML as well as strong analytical and problem-solving skills.

The demand for full-stack quality engineers and software development engineers in test (SDET) is growing as these roles involve developing and maintaining AI-driven test frameworks. Engineers are also expected to collaborate closely with AI systems, validating AI-generated outputs and ensuring the accuracy and reliability of automated testing processes.

How are government programs driving industry and academic collaboration to build a robust AI ecosystem?
The government’s ‘IndiaAI Mission’ aims to build a robust and inclusive AI ecosystem, with the goal of achieving ‘AI for All’ and positioning the country as a global leader in the technology. This mission is founded on pillars such as India AI innovation centers, datasets platform (AIKosha), skilling (FutureSkills), and financing startups. Government programs are also playing a crucial role in fostering industry and academic collaboration. These programs provide funding for AI R&D, support the establishment of AI research centers, promote partnerships between academic institutions and industry players.

Industry-academia collaboration employs mechanisms like Centres of Excellence (CoEs), Technology Innovation Hubs (TIHs), joint R&D through AI innovation centres, and startup incubation programs. While skilling initiatives aim to create talent pool, AIKosha ensures crucial data access.

By facilitating knowledge exchange and innovation, government initiatives help accelerate the development and adoption of AI technologies. These programs also focus on addressing ethical and regulatory challenges associated with AI, ensuring AI advancements are aligned with societal values and public interests.