The Evolving Role of Quality Engineering in an AI-Driven World

The integration of artificial intelligence (AI) into software development is reshaping the responsibilities of Quality Engineers (QEs) working with SAP systems. Speakers during a recent webinar by Tricentis noted that while AI coding assistants are accelerating code production, they are also generating a huge volume of output, some of which include hard-to-detect, AI-authored coding mistakes and errors. This creates a significant challenge for QEs and peer reviewers who must manage this influx. However, it also presents an opportunity for the profession to evolve. 

The Evolving Role of QEs 

The speakers discussed the shifting role of a QE from tactical execution to strategic oversight. They said that AI is increasingly capable of handling the manual work of quality assurance, such as writing test code, maintaining frameworks, and executing tasks in a pipeline. This automation of routine tasks frees QEs to focus on higher-order responsibilities that ensure technology delivery aligns with business objectives. To succeed in this new paradigm, the speakers recommended that QEs must cultivate three specific, high-level skills: 

1. Renewed Focus on Business Value 

The primary function of a QE is shifting towards ensuring all testing activities are directly relevant to business outcomes. It means relying less on metrics like test case counts and defect numbers and focusing on business-relevant metrics such as revenue impact, customer retention, or speed to market. This requires closer collaboration with product owners and business stakeholders. 

2. Mastery of Peer Review 

With AI generating code and tests, peer review becomes an essential skill. This is because AI can make the same incorrect assumptions in the tests it writes as it did in the code it generated. As a result, the QE must act as the final gatekeeper, taking ownership of the AI’s output and applying rigorous review to prevent flaws from advancing. 

3. AI Context Management 

A key emerging technical skill is the ability to provide AI tools with relevant and specific context. AI models can be distracted by too much broad context, leading them to focus on irrelevant details and producing unreliable outputs. A proficient QE will know precisely what information to provide and what to withhold to keep the AI focused on solving the right problem to maximize its productivity and accuracy. 

The speakers concluded that the QE role is transforming from a reactive, detection-oriented function to a more proactive one centered on governance, architecture, and prevention. This allows QEs to focus on challenging assumptions, protecting business interests, and ensuring true quality in an increasingly complex, AI-driven development lifecycle. 

What This Means for SAPinsiders 

For QEs managing quality within complex SAP landscapes, these shifts have specific and immediate implications: 

• Treat AI-generated ABAP and test scripts as a first draft. When a team uses AI to generate ABAP code or Tosca test scripts, it’s easy to become overwhelmed by the sheer volume of output. Therefore, QEs working in SAP landscapes must mandate rigorous peer review for all AI-generated tests and code. The speakers in Tricentis’ webinar recommend that QEs treat AI like a junior developer who is capable but lacks deep business context and requires oversight. They added that QEs should own the outcome and must act as the final gatekeeper to ensure the AI’s work is sound before it impacts mission-critical systems, like core SAP modules.
   
• QEs must evolve from SAP testers to business process guardians. A QE’s value is no longer measured by how many test cases they automate for a specific T-code. AI will handle much of that repetitive work. The speakers noted that a QE’s new primary role is to ensure that AI-driven development aligns with end-to-end business value. They advised QEs to become experts in AI context management. Their deep knowledge of SAP business processes is a superpower that should be used to provide the AI with focused, relevant information, so it solves the right problem. This shifts QEs’ focus from merely checking acceptance criteria to validating that the technology truly enables the desired business outcome.
• Measure everything to justify AI investments. AI is an expensive and resource-intensive tool, which is why business leaders ask for the return on investment (ROI) for the AI platform they’re using in SAP projects. Additionally, traditional QA metrics, such as the number of defects found in SIT, are insufficient to demonstrate AI’s value. For this reason, QEs must measure business outcomes, such as defect leakage to production and cycle time, to prove that quality and speed are improving. They should also track productivity metrics by comparing how long a task takes with AI versus without it. These complex numbers are essential for building the business case and proving that investment in AI is delivering tangible results.