In the high-stakes world of enterprise technology, few deadlines loom as large as SAP’s 2027 cutoff for its legacy ECC system. With countless organizations still navigating the complex and costly transition to S/4HANA, the pressure is mounting. We’re joined by Vernon Yai, a leading data protection and risk management expert, whose work focuses on developing innovative techniques to safeguard sensitive information during large-scale digital transformations. His deep understanding of data governance provides a unique perspective on one of the most significant migration challenges in a generation.
This conversation will explore the practical realities of using generative AI to accelerate these modernization efforts. We’ll delve into the stubborn obstacles that have slowed adoption, address the critical issue of technical debt from decades-old customizations, and examine how new tools are aiming to deliver genuine business value rather than simply replicating outdated processes. Finally, we’ll look ahead to the role AI will play as the migration landscape intensifies in the coming years.
The article mentions your new suite uses generative AI for code changes and proactive monitoring. Could you walk us through a step-by-step example of how this works in practice and what specific efficiency metrics, like reduced project time or cost, a company might expect?
Certainly. Imagine a large manufacturing firm that has been running on a heavily customized ECC system for 15 years. The first step is what we call process flow reverse engineering. The AI doesn’t just look at the code; it analyzes how the system is actually used, mapping out the convoluted, often undocumented, workflows that have evolved over time. From there, the generative AI engine gets to work. It takes those legacy customizations and automatically generates new, clean code that is optimized for the S/4HANA environment, ensuring it aligns with the client’s specific governance and coding standards. Finally, the agentic AI provides proactive monitoring, simulating the impact of these changes before they go live, catching potential disruptions that a human team might miss. While every project is unique, the goal is to cut down on the thousands of manual hours typically spent on analysis and remediation, significantly de-risking the project and accelerating the timeline.
A survey cited in the content shows only 45% of organizations are live on S/4HANA. In your experience, what are the top two hurdles causing this delay, and could you share an anecdote about how a company successfully navigated one of these major challenges?
The two biggest hurdles we consistently see are legacy complexity and the sheer resource-intensive nature of these projects. The 45% figure isn’t surprising when you consider that many of these ECC systems are tangled webs of customizations built up over decades. It’s not just a technical problem; it’s an operational one. I remember working with a retail client that was completely paralyzed by this complexity. They had spent a year just trying to document their existing processes and were getting nowhere. By using an AI-driven tool to reverse engineer their process flows, they had a comprehensive map within weeks. This map was a revelation for them. It revealed that nearly 30% of their custom processes were redundant or obsolete. Seeing that data gave them the confidence to move forward, not with a lift-and-shift, but with a streamlined migration that ultimately saved them millions and got them to the finish line before their competitors.
A Gartner analyst called ECC customizations the “elephant in the room,” saying tools often just make S/4HANA run like the old system. How does your suite specifically tackle this technical debt and help clients unlock new value instead of just recreating outdated processes?
That analyst is absolutely right; that “elephant” is something we designed our suite to address head-on. Simply migrating technical debt from one platform to another is a recipe for a very expensive failure. You end up with a modern system hobbled by old logic. Our approach is different because it starts with illumination before action. The process flow reverse engineering capability is key here. It doesn’t just prepare old code for a new home; it provides a crystal-clear, objective view of how the business actually operates today, warts and all. We present this to leadership, and it’s often a jaw-dropping moment for them. This visibility allows them to make strategic decisions about which processes are essential, which can be retired, and which should be redesigned to take advantage of S/4HANA’s native capabilities. It turns the conversation from “How do we move our old stuff?” to “How do we build a better business?”
With SAP offering its Joule copilot and Kyndryl also releasing a tool, what are the key differentiators of the IBM suite? Please describe a unique capability, perhaps in process reverse engineering, and the tangible benefit it provides to a client during their migration.
It’s a dynamic space, and having more tools is ultimately good for customers. Where we differentiate is by focusing on business transformation, not just technical migration. While other tools are excellent at code conversion or data movement, our suite’s ability to perform deep process flow reverse engineering provides a unique strategic asset. The tangible benefit is clarity. I was in a meeting where a CIO presented our AI-generated process map to the CEO. For the first time, the CEO could visually trace an order from placement to fulfillment and see every bottleneck and manual workaround. That single visualization did more to build the business case for transformation than a hundred spreadsheets. It moves the tool from an IT-level migration utility to a C-suite strategic planning asset, providing a foundation for genuine operational improvement long after the S/4HANA project is complete.
What is your forecast for the S/4HANA migration landscape as the 2027 deadline approaches, particularly regarding the role generative AI will play in clearing the backlog of complex projects?
I forecast a frantic acceleration over the next two years. The 55% of organizations that aren’t yet live are facing a hard deadline, and many of them are the most complex cases that have been putting it off. Generative AI will become an indispensable co-pilot in this race against time. It won’t be a magic wand that eliminates all the work, but it will be crucial for automating the most time-consuming and error-prone tasks like code analysis, remediation, and testing. This will free up the scarce human experts to focus on the high-value strategic questions, like how to redesign processes and drive innovation. My prediction is that the success stories of the final push to 2027 will be defined by the organizations that effectively pair human expertise with AI-driven automation, using the technology not just to meet a deadline, but to fundamentally transform their enterprise.
