Business Assurance for SAP: Perfect Storm or Plain Sailing?

What the 2026 Business Assurance Report Reveals

In the 2026 report (it’s our third, can you believe it?), more than 500 senior decision-makers across ten countries and eight industries were quizzed, and the single, overwhelming finding is the shift in quality assurance from a periodic, point-in-time test to a culture of continuous, lifecycle discipline that underpins migration success, resilient operations, and business performance. In many ways, this is no surprise. Last year, the World Economic Forum noted that “businesses must continuously adapt to ongoing transformations driven by emerging technologies, shifting consumer expectations, and sustainability goals” – what they termed the perpetually adaptive enterprise, and our Enterprise Core has, for a long time, fostered a culture of continuous innovation.

Nor is it a surprise that Gen AI and Agentic AI feature high on the list of priorities to accelerate test design, deepen change impact analysis, and enhance real-time monitoring, although adoption of AI technologies, particularly Agentic AI, has yet to gather pace. What is surprising, though, is the speed with which many enterprises have pivoted toward a continuous testing regime.

In business, across all sectors, there is no sense of organizations “battening down the hatches” and weathering the AI storm. The commitment to AI is absolute.

The question is how AI implementation equates to business value, and that question is still being answered. In the world of testing, the most anticipated advantage is AI’s ability to predict how changes could affect system performance and functionality, helping identify potential disruptions or inefficiencies early.

Data, Testing, and the Advantage of Early Insight

And this brings me back to my point about having access to the right data at the right time (the right time being as early as possible). The research shows that the most successful SAP transformations succeed because they treat data quality and testing as strategic enablers, not technical afterthoughts. Integrating real, production-mimicking data into testing frameworks early and aligning quality assurance efforts with wider business strategies, including AI readiness and continuous operational excellence, empowers organizations to achieve rapid, resilient transformations while securing an environment that fosters future innovation. In fact, it is the leading enterprises that deploy tools and knowledge, not as isolated technologies but as part of a unified, governed platform that continuously evaluates change, prioritises risk, and determines which tests matter most, that will most likely successfully transform their ERP environment and – when aligned to business strategies – their organizations.    

As a teaser to the report, I can tell you that the five key points are as follows (but I would really urge you to read the full report, available to download here):

1. Quality Assurance is essential
2. Data is key
3. RISE isn’t the future, it’s the now
4. Security remains a challenge
5. Transforming at scale is best done with experienced help.

What? No mention of AI, you ask. It is there, and it is the great enabler across all five points. The report highlights how AI is, for example, central to ensuring QA by implementing AI model validation and monitoring processes, how AI is improving data integrity through automated data quality checks, how the integration of AI into security and compliance processes is no longer a futuristic concept but a present-day reality, and how some organizations are using AI to enhance traceability, reduce manual errors, and strengthen compliance at scale.  

In fact, businesses, working with companies like Capgemini and Sogeti, are devising new and innovative AI and cutting-edge technology solutions to drive a new age of intelligence.  

Finally, to summarize, I will quote the final paragraph of the report, which says it succinctly: