The SAP Dilemma: Navigating the AI Paradox of Speed and Quality

Implementing complex migrations like SAP S/4HANA, rolling out new Fiori apps, or simply trying to keep pace with business demands highlights the need for speed in many organizations today. However, as organizations accelerate, they may take on dangerous shortcuts.

The Tricentis 2025 Quality Transformation Report, and a recent webinar discusses the staggering cost of failure when taking shortcuts. Even with QA practices in place, about 40% of respondents said that grappling with quality issues costs them a million dollars a year or more.

For SAP teams, this is a stark reminder of the operational risks. A defect in the production environment can be a potential business catastrophe that can halt financial closes or disrupt global supply chains.

The Speed vs. Quality Paradox

The core of the issue is a surprising paradox. When researchers asked teams about their desired outcomes, the answers were telling. David Colwell, VP of AI and Machine Learning at Tricentis explained, “When we asked people what they were looking for, we noticed that it was about improving release speed and staff productivity far more than it was about quality.”

This reflects the organizational pressure SAP teams face daily. The business wants new functionality live now, which often means regression testing cycles for quarterly releases get squeezed.

Still, according to Colwell, while the desire for speed seems to trump quality, the industry now sees modern QA as an accelerator, not a bottleneck.

Misaligned Teams and the AI Opportunity

So, what’s holding teams back? It’s often not technology but people and processes. The report points to poor communication between development and QA teams (33%) and a disconnect with leadership (29%) as significant barriers.

Now, over 80% of IT professionals believe AI can help deliver both quality and speed in unison. Imagine AI-powered tools that can intelligently analyze transport requests, identify the highest-risk objects, and automatically run the most critical regression tests for the Order-to-Cash process.

Moreover, 82% of leaders look forward to AI agents taking on repetitive tasks, freeing up invaluable functional experts to focus on true business value, not tedious test execution.

However, the rise of AI introduces a new twist. AI can generate code and configurations faster than any human, but it can also create what Colwell calls “AI slop.”

He warned, “AI can introduce an absolute ton of technical debt, and as long as it’s passing the automated tests, maybe you ship it, but that technical debt falls back onto the engineers, and it falls back onto the testers in the form of unexpected regressions.”

Take the example of an AI assistant generating a new ABAP code. It might fulfill the immediate requirement perfectly, but without a deep understanding of the specific SAP landscape, it could inadvertently change dependencies that impact a seemingly unrelated module—creating defects “in places I never would have thought a human would put a defect,” as Colwell puts it. This makes intelligent, impact-aware testing more critical than ever.

What This Means for SAPinsiders

AI is essential for the future of QA. A staggering 99% of the study’s respondents believe autonomous testing will be helpful. For SAP users, this is a necessary counterbalance. As AI is used more in development to customize SAP systems, SAPinsiders should incorporate AI in testing to validate those changes. It’s about fighting fire with fire—using intelligent automation to find the complex issues that AI-generated code can create across an integrated landscape.

Businesses should be assured of desired outcomes in QA practices. Organizations are asking for speed because they believe QA can deliver it. The SAP quality team needs to shift its identity from being a final quality gate to a strategic enabler of speed. By leveraging AI-powered tools to pinpoint risks, reduce redundant testing, and provide fast feedback, SAPinsiders can transform QA from a cost center into a value driver that accelerates transformation projects like SAP S/4HANA migrations.

The role of the SAP Quality Engineer is evolving dramatically. As Colwell suggests, the new job is to become a “team lead of little AI armies,” using your expertise to guide, review, and edit the work of AI agents. The most critical skills are not just knowing SAP transactions but also deep business knowledge and critical thinking. The engineer’s irreplaceable value lies in being the subject matter expert who ensures the AI’s work aligns with real-world business processes.