You’ll make the 2027 deadline. Your SAP S/4HANA program is funded, your system integrator is engaged, and the cutover plan is taking shape.
But here is the question fewer transformation leaders are asking: When the board asks what your AI strategy runs on, will your data be ready — or just your ERP?
Many SAP customers treat the move from SAP ECC to SAP S/4HANA as the endgame of modernization. That assumption is understandable, but incomplete, because ERP migration modernizes systems — it does not modernize enterprise knowledge.
That distinction is now surfacing across transformation programs in ways that are difficult to ignore. Even after a successful SAP S/4HANA go-live, enterprise information often remains fragmented, spread across retired SAP instances, non-SAP applications, documents, cloud platforms, and decades of operational history.
A modern ERP core sitting on top of disconnected enterprise data is not a foundation for AI. It is a liability.
The Missing Layer Between ERP and AI
Most transformation programs overlook the Enterprise Data Layer, a governed framework that preserves access to enterprise information across systems while maintaining retention, auditability, security, and trust.
Without it, Application Retirement and Archiving gets treated as a back-office cleanup exercise rather than a strategic capability. As a result, ERP decommissioning becomes the goal, while AI-Ready Enterprise Data should be the outcome.
This matters now because AI initiatives are moving from pilot to production across the enterprise. Copilots, automation agents, and generative AI tools are only as useful as the business context they can reliably access. If the history of customer contracts, procurement decisions, engineering records, or financial transactions is trapped in legacy environments or siloed across platforms, the AI layer inherits the same fragmentation as the systems beneath it. The result is expensive guesswork.
DuPont: A Governed Enterprise Data Foundation at Scale
DuPont offers a concrete example of what getting this right looks like. Working with Data Migration International (DMI), the company decommissioned 17 SAP systems and managed more than 50 TB of enterprise data within 12 months. Powered by DMI’s JiVS Information Management Platform (IMP), it also established a GDPR-ready environment that keeps that information accessible for future business use.
The strategic intent is notable. DuPont did not simply shut down legacy applications. It preserved the enterprise knowledge those systems contained and governed it in a way that supports future reporting, compliance, and AI use cases. Thus, modernization created a trusted data foundation that travels forward with the business.
That is the model: organizations can retire complex SAP landscapes without losing access to the information that runs through them.
Why AI Readiness Starts with Data Readiness
There is a growing tendency to discuss AI readiness as if it begins with model selection or use-case design. In practice, it begins earlier, with enterprise data readiness.
That means knowing where critical information lives, preserving it beyond application lifecycles, governing it consistently, and making it accessible across SAP and non-SAP environments. It also means confronting the challenge of connecting business information across multiple generations of technology.
Energy Transfer illustrates that challenge clearly. Its environment spans SAP, mainframe platforms, Infor, JD Edwards, and additional legacy systems accumulated across decades of operations, all of which must remain accessible, compliant with GDPR and CCPA, and usable for enterprise-wide decisions. The issue is not SAP versus non-SAP, but whether the organization can create a governed enterprise data layer across the entire organization.
Archive, Govern, Activate
A practical framework for building that layer is to Archive, Govern, and Activate:
- Archive: Retire legacy applications while preserving enterprise information in a structured, accessible form, reducing infrastructure costs without sacrificing historical access. According to DMI, organizations across its more than 3,000 implementations worldwide have reduced legacy IT operating costs by up to 80%.
- Govern: Apply controls that ensure data trustworthiness through retention policies, audit trails, privacy rules, legal hold capabilities, and defensible compliance. Without governance, preserved data is not an asset. It is a risk.
- Activate: Turn governed information into business value, supporting analytics, process automation, AI agents, and future platform decisions. This is where Enterprise Data Activation moves the conversation beyond retention and into strategic impact.
Four questions to ask your transformation program this quarter
For CIOs, enterprise architects, and transformation leaders, AI readiness starts with data readiness — and that can be tested with four questions:
- Have we inventoried which legacy systems hold data our AI and analytics roadmap will need?
- Is application decommissioning in the transformation business case, or parked as post-go-live cleanup?
- Who owns retention, privacy, and legal hold for retired instances after the project team disbands?
- Can our future copilots and agents actually reach governed historical data — or only what moved to S/4HANA?
Finally, an organization can complete an ERP transformation and still be unprepared for AI. The ones that close that gap will be those treating Application Retirement and Archiving
— and platforms purpose-built for it, such as JiVS IMP — as the foundation for everything that follows go-live.
The organizations that succeed with AI will not necessarily be those with the newest ERP systems. They will be the ones with a governed Enterprise Data Layer that preserves, governs, and activates enterprise knowledge long after the source applications have been retired.
What This Means for ERP Insiders
Clean core cuts both ways. Keeping S/4HANA clean means most historical data stays out of the new system — which makes a governed Enterprise Data Layer a prerequisite, not an afterthought. Plan decommissioning into the migration business case, not after it.
The real measure of transformation is not systems retired, but data preserved. As the examples of DuPont and Energy Transfer illustrate, maintaining access, governance, and trust across decades of operational information is what enables future analytics, automation, and AI, rather than just go-live.
The Archive, Govern and Activate framework turns application retirement and archiving into a strategic capability. Organizations that follow this model are not just decommissioning legacy systems. They are building the AI-Ready Enterprise Data foundation that makes every future initiative more intelligent.
