Enterprise AI has entered a new phase. The question for CIOs and CFOs is no longer whether AI can work, but whether it can deliver measurable value at enterprise scale. That question brings the economics of enterprise data into focus.
Companies are investing in copilots, AI agents and RAG, yet the business information these technologies need remains fragmented across SAP environments, legacy systems, archives and documents. Every new AI initiative can trigger another cycle of finding, extracting, cleansing and governing data before business value can begin. This is more than a data-management challenge — it’s an AI economics challenge.
Poor Data Is Blocking AI ROI
McKinsey research shows 88% of enterprises now use AI in at least one business function. Yet a separate Gartner survey found only 28% of AI initiatives in infrastructure and operations are fully meeting ROI expectations, with poor data quality and fragmented infrastructure cited as leading causes. RAND Corporation research puts the broader AI project failure rate above 80%.
These figures highlight that AI is only as powerful as the business context it can access. Models may understand language, but they do not understand a company’s customers, suppliers, products, contracts or the relationships between them. That knowledge resides in enterprise data, and much of it is historical.
Therefore, AI readiness cannot mean moving current data to a modern cloud platform. AI-ready enterprise data is trusted, governed and accessible information that preserves the business context behind processes, transactions and relationships. When that foundation does not exist, organizations pay for the gap repeatedly.
“Enterprises don’t need more standalone AI tools. They need a unified, full-stack data platform that seamlessly connects historical and operational context with real-time AI execution,” says Thomas Failer, CEO and owner of Data Migration International.
The Hidden AI Data Integration Tax
In a typical enterprise AI use case, information may sit partly in SAP S/4HANA, partly in an older ECC environment, and partly in CRM systems, documents or archives. Before AI can use any of it, teams must locate the data, interpret it, confirm it can legally be used and decide how to supply it to the application.
Multiply that across dozens of initiatives and an expensive pattern of bespoke pipelines, duplicated data, vector stores and point solutions assembled project by project emerges. Organizations effectively pay an AI data integration tax every time they reconstruct business context that already exists somewhere in the enterprise.
The cost can compound as AI consumption expands. Gartner forecasts that AI coding token costs are on track to meet or exceed the average software developer’s salary by 2028. The projection highlights how quickly unmanaged AI spending can grow when projects lack a reusable data foundation.
Stop Paying Twice for Enterprise Data
Legacy ERP makes this inefficiency especially visible. Some older applications remain operational because the business still needs the historical information they contain, not because employees continue to use the application itself. Consequently, organizations pay for infrastructure, maintenance and security simply to retain access to data. When an AI or transformation initiative needs that information, the organization pays again to extract and prepare it.
Application retirement can change that equation. Separating historical data from the application that created it allows organizations to decommission obsolete systems while maintaining structured and compliant access to business information.
Manufacturer Bühler and energy group E.ON both faced the bind of costly legacy SAP systems kept alive purely for compliance, with manual historization too slow to keep pace. One-click historization let both companies accelerate decommissioning, cut complexity and retain audit-compliant access to historical data, while laying a foundation for future AI applications.
JiVS IMP, the platform behind that approach, has been used to decommission more than 1,500 SAP systems across 3,000-plus implementations, with potential IT operational cost reductions of up to 80%. However, reducing cost is only half the opportunity.
Turning Historical Data Into an AI Asset
Historical ERP data contains accumulated knowledge about customers, suppliers, products, assets, transactions and business relationships. Traditionally, application retirement preserved this information for compliance. AI changes its potential value.
When historical information sits in a structured, governed data layer with its business context intact, it becomes available for analytics, automation, RAG and AI agents.
At engine manufacturer Deutz, product, supplier and customer history was scattered across SAP, Infor, PLM systems and SharePoint. Historizing that data into a common object layer connected to the company’s OttoVerse platform means questions that once required manual cross-system searches can now be answered in seconds, with a knowledge graph and RAG layer planned to sharpen answer quality further.
The same underlying strategy can therefore address two economic objectives at once: reduce data cost by eliminating unnecessary legacy infrastructure, duplicated integrations and repeated data-preparation work, and increase data value by making governed enterprise knowledge reusable across analytics, automation and AI.
Archive. Govern. Activate.
The business case for AI-ready data rests on three moves:
- Archive applications without losing valuable enterprise information
- Govern retention, security, auditability and trust independently of the source system
- Activate trusted historical and current data for analytics, automation and AI
This is why AI-ready data should become a joint CIO and CFO conversation. The relevant question is how much the organization spends to repeatedly prepare enterprise data, and how much more value that information could generate as a reusable, AI-ready asset.
Foundation models will keep improving, but they do not contain the unique knowledge of an enterprise. Applications have lifecycles. Data does not. In the AI era, the value of that data may be only beginning.
What This Means for ERP Insiders
Stop treating legacy retention as a sunk cost. If systems are kept alive purely to preserve historical data, that data can likely be separated, archived and made audit-proof independently of the application, removing the infrastructure cost while keeping the information.
Budget for reusable data, not one-off pipelines. Every AI project that rebuilds its own data integration layer from scratch pays the same cost twice. A governed, AI-ready data foundation amortizes that cost across every future initiative.
Bring the CFO into the data conversation early. Framing AI-ready data as an economics question, cost avoided plus value unlocked, gives finance a clear basis to evaluate and prioritize data foundation investment alongside AI application spend.
