The quality engineering (QE) landscape is undergoing a significant transformation, reshaping everything from requirements engineering to value delivery. This change is being primarily driven by artificial intelligence (AI), specifically, generative AI (GenAI).

GenAI’s compelling potential is prompting many quality engineering teams to adopt an “AI-tinted lens.” However, equating progress with GenAI integration could prove delusive. Without a clear strategy and proper validation, deploying GenAI can be risky.

This blog explains how GenAI can be effectively leveraged in QE through a strong and strategic testing framework that conforms to the core principles of agentic AI in QE.

Risks of Hasty GenAI Adoption in QE

Organizations that rush into GenAI testing without a well-defined roadmap risk falling into an unproductive cycle of isolated experiments that fail to deliver tangible business value. With significant investments in tools and experiments, they face low returns. This highlights the need for a paradigm shift in QE for agentic AI, from focusing on unit tests to evaluating how these agents perform in complex, real-world scenarios.

According to a recent Leapwork survey, while 85% of companies integrated AI into their testing stack last year, only 16% consider their testing processes highly effective. This underlines the fact that adoption without a strategy leads to effort without outcomes. The same holds true for agentic AI, where even advanced algorithms do not guarantee reliability or safety without a robust QE framework that evaluates goal achievement, adaptability, and long-term behavior.

Integration Strategies for Effective GenAI in QE

In addition to using the latest AI tools, the QE approach demands a tactical alignment across GenAI capabilities, QE practices, and core business objectives. This integration involves:

• Developing a targeted GenAI roadmap for QE: Defining clear objectives, timelines, and measurable outcomes in line with agentic AI’s testing objectives
• Cultivating long-term GenAI capabilities: Shifting from isolated pilots to sustainable, scalable solutions – as essential for GenAI in QE as continuous testing and adaptive learning are for agentic AI
• Driving GenAI efficiency while enhancing quality: Prioritizing high-impact, business-focused GenAI applications that mirror agentic AI’s scenario-based testing method to assess its robustness in real-world situations

Designing a Strategic Framework for GenAI in QE

To effectively leverage GenAI capabilities in QE, organizations need a strategic framework modeled on agentic AI’s testing techniques, goals, and metrics. This includes:

• Outlining a comprehensive GenAI strategy: Define clear objectives, identify high-value use cases, and follow a phased rollout similar to agentic AI testing.
• Focusing on high-value use cases: Prioritize applications with strong return on investment (ROI) potential and strategic business alignment, in line with agentic AI’s shift from functional verification to goal achievement.
• Building long-term capabilities: Invest in upskilling and equip teams with the expertise to effectively apply GenAI in QE.
• Measuring and monitoring results: Establish metrics to monitor the impact of GenAI initiatives and ensure they deliver measurable outcomes, similar to agentic AI.
• Integrating human oversight: Integrate human-in-the-loop (HITL) reviews to validate GenAI output, mitigate risks, and uphold quality, much like how biases and ethical concerns are addressed in agentic AI.
• Fostering cross-disciplinary collaboration: Encourage collaboration among QE engineers, data scientists, AI developers, and business stakeholders to drive robust GenAI adoption, as with agentic AI.

Conclusion

With a strategic approach, organizations can unlock the full potential of GenAI in QE, transforming testing from a cost center into a business asset. This shift will help them build efficient, reliable, and high-quality software in an AI-powered world, in the same way as agentic AI systems.