Why SAP S/4HANA Success Doesn’t Always Guarantee Business Value: Melissa Itoh

As organizations complete SAP S/4HANA migrations, many are finding that a technically successful go-live does not automatically translate into business value. This disconnect is becoming a defining challenge in enterprise ERP transformation, where projects delivered on time and within budget still leave business teams asking what has actually changed.

In this conversation with SAPinsider, Melissa Itoh, Principal Solution Architect for SAP BTP at NTT DATA Business Solutions, says, the question she hears most often after go-live is simple: “Where’s the value?” Drawing on her work across customers, she explains why this gap persists, and what organizations need to do differently to turn SAP S/4HANA from a technical milestone into a platform for measurable business outcomes.

Q: You’re often brought in at different points in a customer’s SAP transformation. What usually prompts organizations to seek help?

MI: Typically, we see a few common triggers depending on where the customer is in their journey. One is the immediate mover, organizations that know the deadline is coming and need to act, but don’t know where to start. For many, concepts like SAP Business Technology Platform, SAP Business Data Cloud, or SAP Business AI feel like a big black box. They may have licenses or credits but lack a clear plan for how to use them, so they’re trying to navigate both complexity and time pressure at the same time.

For example, we were brought in to do a Brownfield, lift-and-shift migration from SAP ECC to SAP S/4HANA. The focus was on getting to SAP S/4HANA on time and within budget, so the project was technically successful and passed all testing, but there was little to no innovation planned alongside the move. Once the system went live, we started hearing from the business: where is the value? Where are the improvements we were promised?

From the business perspective, nothing had changed. They were still seeing the same reports, the same screens, and the same manual workarounds. That gap between technical success and business value is one of the most common and most urgent problems we’re asked to address.

Q: Once SAP S/4HANA is live, why do so many business teams still ask, “Where’s the value?”

MI: Most of the time, it’s not the technology, it’s the people and the data foundations. I’ve been doing this for over two decades in the SAP ecosystem, and there are always edge cases where technology plays a role, especially with early adopters, but that’s rarely the primary issue. Even now, with AI, it’s not that the models aren’t capable – it’s that the surrounding foundations aren’t ready. I call it the “tech plus people foundations.” If those aren’t aligned, value doesn’t materialize.

For example, in the lift-and-shift, Brownfield migration, everyone involved understood it’s a technical move with no innovation planned for day one. But the business doesn’t experience it that way. They see the same reports, the same screens, the same manual workarounds, and from their perspective, after all the time, cost, and change effort, nothing is better. That’s when the question comes up: where’s the value?

What this really comes down to is organizational change management (OCM). OCM is critical and often overlooked. Were people prepared for what day one would look like? Did they understand when and how value would actually be delivered? If expectations aren’t managed and there’s no roadmap for value, frustration sets in quickly.

Without planning even a few quick wins alongside go-live, there’s no momentum, no excitement, and no visible payoff for the business. That’s where the disconnect becomes most visible.

Q: Can you share an example of a migration that was on track to be  technically successful  but still failed to deliver real business value?

MI: In one large enterprise transformation, the program took a lift-and-shift approach to move off SAP ECC. Midway, the business questioned the lack of improvement and uncovered the real issue: the data foundation.

Their SAP BW environment was siloed, not real time, and built on inconsistent definitions across finance, operations, and supply chain. Basic questions required days of Excel reconciliation, making a single trusted view, and any serious analytics or AI, unrealistic.

Rather than proceed to a technically successful but low-value go-live, the program paused and reset. They defined what day one needed to deliver, introduced a small set of quick wins (one per line of business), and set a clear roadmap to modernize and harmonize SAP BW data for analytics and AI.

That shift made value tangible. The business could see immediate improvements and understand the path forward. Without that reset, the project would likely have delivered on time and budget, but reinforced the post–go-live value gap.

Q: Beyond change management, what gets treated as optional during SAP S/4HANA programs but proves essential later?

MI: Innovation, especially data modernization and AI and analytics readiness. Many organizations apply the same technical, lift-and-shift mindset to data as they do to ERP. That may get them compliant, but it often does not set them up for analytics or AI.

In legacy SAP Business Warehouse environments, data is typically siloed, not real time, and inconsistent across functions. Finance, operations, and supply chain operate on different versions of the truth, often requiring days of manual reconciliation. That makes accurate analytics, and any meaningful AI, difficult.

The key is to tie data modernization directly to business value and pair it with organizational change management. Rather than re-platforming your existing BW to a non-SAP alternative, a stronger and more streamlined approach is to move it to BW private cloud edition within SAP Business Data Cloud, so data is immediately in a platform that supports harmonization, analytics, and AI.

From there, start with customer-managed data products as a practical, cost-controlled commercial entry point to expose your BW assets for greater SAP BDC capabilities, then expand over time. The goal is to treat this as step one: get data into a future-ready platform, deliver a few quick wins, and build toward broader innovation and AI enablement.

Q: What are the clearest signs that a company’s data and reporting landscape is no longer supporting timely, trusted decisions?

MI: Time-to-consumption is the biggest issue. When the business or CFO asks for a report needed for a critical decision, it can take a week or even two to produce data that is actually trusted. By that point, the decision has already been made using stale or incomplete information. Closely tied to that is the second major issue: siloed data and multiple versions of the truth. Different functions, finance, operations, supply chain, have conflicting numbers, which leads to extensive manual reconciliation, often in Excel, before anyone can align on a single view.

Q: You mentioned that organizations can move from roughly 30% to 80% data utilization after BW modernization to SAP Business Data Cloud. How does day-to-day decision-making change once that foundation is modernized?

MI: Trust increases, but the real shift is in how decisions get made day to day. Before modernization, data is siloed, not real time, and inconsistent across functions.

Because of that, only a small portion of the data, often around 30%, is actually used, simply because it’s the only data that can be trusted and delivered in time. Once that data is harmonized and brought into a single, consistent, real-time foundation, that dynamic changes. Trust increases, reconciliation effort drops, and decisions can be made within the required time frame. That’s when utilization jumps closer to 80%, because teams can now rely on a much larger portion of their data to make timely, confident decisions.

Q: What do executives most often misunderstand about the relationship between data readiness and AI?

MI: There’s a common misconception that you can “just do AI” and see immediate efficiency gains. In practice, AI readiness depends on a few foundational pieces coming together. On the technology side, that means modern integrations that feed real-time data, a high-quality, harmonized, and semantically rich data foundation, and optimized processes, because AI cannot compensate for manual workarounds and Excel-based reconciliation outside the system.

Equally important is organizational readiness: aligning teams, setting expectations, and putting the right governance and change management in place. If those tech and people foundations are not in place, AI may produce results faster, but not more accurately.

One way to make this tangible for executives is to run a simple simulation. Take existing, inconsistent data and run it through an AI model. You will get an answer in seconds instead of a week, but the insight will be just as flawed, if not worse. That’s why we say AI without a proper data foundation only gives you bad decisions faster.

Q: When you look at the customers that do realize post–go-live value, what do they consistently get right?

MI: It starts with strong executive sponsorship across IT and the business, backed by an executive steering committee and clear governance model that keeps the program focused on business outcomes, not just technical milestones.

Equally critical is organizational change management. The best programs clearly set expectations for day one, what will change, and when value will arrive, keeping IT and the business aligned.

They also plan for momentum, identifying a few quick, high-impact use cases at go-live so the business can see immediate value and stay engaged.

Finally, they pair that with a clear long-term roadmap. The business understands not just what they’re getting now, but what comes next and how the full vision will unfold. When you have executive alignment, strong OCM, visible early value, and a defined path forward, you create the momentum needed to push through the complexity of transformation. That’s what separates the companies that succeed.