Part two of a two-part series. Read part one on how effective quality assurance strategy can increase business success.
Code is being written faster than it can be tested through traditional quality assurance (QA) processes. Scripts that break every time the code changes were never built for this pace. Agentic AI is.
Agentic systems reason, adapt, and act autonomously. They move from a reactive, manual process into a predictive, self-healing, and continuously learning capability – one that gets better over time rather than requiring constant maintenance. Your organization can use agentic AI to test at the same speed that code is produced, helping avoid backlogs and maintain consistent application quality.
Making that shift takes intention, and it starts with the strategic foundation covered in part one of this series, positioning QA as a driver of business value rather than a final-stage checkpoint. From there, here's where agentic AI takes it further.
Assess your AI readiness
Companies once had to define a cloud plan before investing in cloud initiatives. Similarly, they now need a deliberate AI plan that clarifies what level of AI adoption they're targeting and how. Before implementing AI for QA, take time to assess whether your organization’s tools, processes, and infrastructure actually support AI.
Start by taking an honest inventory:
- Are your current testing tools and workflows built to support AI-driven processes, with accessible data, and automation-ready pipelines?
- Do your teams have the skills to build, manage, and govern AI agents with confidence?
- How much budget and organizational commitment are you prepared to invest in relation to the outcomes you expect in return?
Answering these questions will shape a strategy that delivers measurable business value rather than a pilot that stalls.
AI evolves quickly, so your organization needs to stay agile as new tools emerge. Many organizations are in the middle stage of AI readiness — running pilots, conducting proofs of concept, or experimenting with individual tools — without having reached full integration. Knowing where you stand helps you make more intentional decisions about how to use AI for QA.
Deploy AI for core testing functions
AI can reshape QA, turning what used to be a painstaking manual process into an agile, insightful, and far more efficient endeavor. These are the capabilities most likely to deliver the biggest advantages to your organization.
- Faster defect detection: AI agents can autonomously plan and run test scripts around the clock, identifying issues significantly faster than any manual process.
- Intelligent test orchestration: Agents handle the full testing lifecycle, from strategy to execution, adapting continuously rather than following static scripts, freeing your skilled employees for more business-critical work.
- Autonomous remediation: Self-healing capabilities allow agents to fix the underlying code themselves, in certain situations. They can re-run tests to confirm the fix.
Ensure humans stay in-the-loop
AI capabilities are impressive, but humans remain an essential part of QA, providing judgment, context, and creativity that keep quality standards high. A “human in the loop” approach creates a dynamic balance. AI spots patterns and anomalies at scale, while people interpret those insights, make critical decisions, and plan ongoing improvements.
AI’s analytical prowess and human intuition work best together. Human expertise navigates nuances and unexpected scenarios to achieve better outcomes, while AI frees people to focus on insight-driven problem-solving.
Consider where agentic AI fits your QA strategy
Speed and quality no longer have to compete. Agentic AI gives QA the ability to keep pace with how fast code is written today, without sacrificing the reliability that protects your brand, your releases, and your customers' trust.
Organizations that treat agentic AI as a strategic capability, not just a tooling upgrade, will turn quality assurance into the business advantage it was always meant to be.
Ready to advance your application quality with AI?
