Enterprise AI Readiness Depends on Data Independence and Business Context

Key Takeaways

• Define the records, relationships and historical data required to support priority AI use cases.
• Preserve business context during SAP S/4HANA transformation and before retiring legacy applications.
• Apply consistent access, retention and data quality controls across operational systems, archives and AI environments.

Enterprise AI requires more than access to business data. It needs the context behind that information, including the relationships between records, their meaning within business processes and the history needed to support reliable decisions.

For CIOs and ERP leaders, this makes data independence an important part of AI readiness. By separating enterprise information from the applications that originally created it, organisations can retain access to historical data after legacy systems are retired, while making relevant information available to authorised AI tools.

The issue is particularly relevant to SAP S/4HANA transformation programmes. As businesses modernise their ERP environments, they must decide which information belongs in the new operational system and which historical records need to remain accessible for compliance, reporting and future AI applications.

Without a strategy for preserving business context, organisations risk retaining data that AI tools can access but cannot interpret correctly.

Why Enterprise AI Needs More Than Data Access

Business context explains how individual records connect to wider business activities. An order number, for example, has limited value in isolation. Its relationships with a customer, material, delivery, invoice and payment provide the information needed to understand the transaction.

In complex enterprise environments, these relationships are often distributed across SAP ECC, SAP S/4HANA, non-SAP applications, software-as-a-service platforms and historical archives. The meaning of a record may also depend on application-specific tables, codes, identifiers and customised business logic.

Two scenarios illustrate the challenge.

• Demand forecasting: An AI agent can access three years of sales history in SAP S/4HANA but cannot retrieve earlier records held in SAP ECC. Its forecasts may therefore overlook longer-term demand patterns.
• Procurement analysis: A procurement copilot can access SAP Ariba transactions but cannot connect them to supplier records held in an ERP system inherited through an acquisition. Its assessment of supplier performance may be incomplete.

These examples highlight two distinct risks: missing historical information and missing relationships between records.

Enterprise AI readiness requires organisations to preserve both. Data must remain accessible, but it must also retain enough business meaning for AI applications to interpret it accurately.

Data Independence as a Foundation for Enterprise AI

Data independence separates enterprise information from the applications that generate and manage it, while preserving the context needed to interpret that information.

This allows historical records to remain available when applications are upgraded, consolidated or retired. Organisations can manage operational and historical data separately without losing information required for future analysis, audits or AI-supported workflows.

Data Migration International (DMI) applies this approach through its JiVS Information Management Platform (JiVS IMP). The platform creates an application-independent data layer designed to preserve enterprise information and its business context, making it available to authorised applications, analytics platforms and AI consumers.

The distinction is important. Archiving records alone does not necessarily preserve the relationships needed to understand them. An independent data layer aims to retain those relationships so that information remains useful beyond the lifecycle of its original application.

For ERP leaders, this approach can connect legacy system retirement with wider data modernisation and AI initiatives.

How JiVS Business Objects Preserve Business Meaning

A central element of the JiVS approach is the use of predefined Business Objects to represent technical application structures as meaningful business entities.

A Business Object can bring together the records and relationships associated with a customer order, supplier, invoice or asset. Instead of treating each record as an isolated technical structure, this approach preserves the wider transaction or business entity.

In a complex ERP environment, a customer order may span numerous database tables. Retaining the underlying records without their relationships can make it difficult for consuming applications to reconstruct the original transaction.

DMI states that JiVS provides more than 6,000 predefined Business Objects covering SAP, Oracle, Microsoft and other enterprise applications. These objects draw on established ERP application knowledge to help organisations preserve and expose business context without having to reconstruct every relationship from scratch.

For enterprise architects, this can provide a starting point for making historical information understandable across different applications and technologies.

However, the value of predefined objects depends on how well they reflect the organisation’s data structures, customisations and specific business requirements. Appropriate validation remains necessary before information is used in critical AI workflows.

Connecting Historical Data With AI Applications

Preserving business context is only one part of the architecture. Organisations must also establish how AI applications and other consuming systems will access the information they need.

The JiVS architecture includes application programming interfaces (APIs), Model Context Protocol (MCP) connections, data products and Open Resource Discovery (ORD) objects. These capabilities support the delivery and discovery of enterprise information across connected environments.

Data replication can also bring operational SAP information into scope alongside historical records. Together, these components are intended to make relevant enterprise context available beyond the application in which it originated.

The appropriate integration model depends on the AI use case, the source systems involved and the capabilities of the consuming environment.

Enterprise architects should establish which records an AI application requires, how relationships will be represented, how frequently information needs to be updated and which permissions govern access. These considerations are particularly important when AI agents interact with both operational and historical data.

The objective is not simply to expose more data to AI. It is to provide the right information, with sufficient context and appropriate controls.

Data Quality and Governance Remain Critical

Data independence does not automatically resolve data quality problems. Duplicate supplier records, missing fields, inconsistent codes and conflicting historical values can still affect the reliability of AI-generated outputs.

Organisations should assess data quality against the requirements of each use case. Validation rules, reconciliation processes and human review remain important where records are incomplete, ambiguous or inconsistent.

Governance must also extend beyond the original application. When historical information becomes available through an independent data layer, access permissions, retention requirements and auditability must remain effective across every consuming environment.

This includes AI agents that retrieve information from archives or combine records from multiple systems.

Organisations should define who can access specific datasets, which actions AI tools are permitted to perform, how long information must be retained and how access and usage are recorded.

The broader principle is that increasing data availability must not weaken control over sensitive business information.

The Economics of Legacy System Retirement

Many organisations continue running legacy applications primarily because they need access to historical records. Maintaining these systems can involve infrastructure, licensing, support and specialist administration costs, even when the applications no longer support day-to-day operations.

DMI reports that retiring legacy systems using JiVS can reduce IT operating costs by up to 80% while preserving access to historical information. This is a vendor-reported potential saving, and actual results will depend on the applications involved, the existing cost base and the scope of the retirement programme.

An application-independent data layer can help separate the retention of business information from the ongoing operation of the software that created it.

This creates a link between legacy modernisation and AI readiness. Organisations can reduce their dependence on older applications while retaining the historical information needed for reporting, compliance and future analytical use cases.

The business case should nevertheless account for migration, validation, ongoing platform costs and the governance requirements associated with retaining historical records.

Connecting SAP S/4HANA Transformation With AI Readiness

SAP S/4HANA transformation programmes and enterprise AI initiatives often depend on overlapping business information, even when they are managed as separate projects.

ERP teams need to determine which data is required for current operations, while compliance teams may need to retain historical records and AI teams may require longer-term datasets to identify patterns or support decision-making.

Treating data as a lasting enterprise asset can help bring these requirements together.

Before an SAP S/4HANA cutover, organisations can identify the historical information that does not need to be migrated into the new operational environment. They can then preserve the relevant records and relationships independently, subject to retention and governance requirements.

This can reduce the scope of operational migration while keeping historical information available for future business needs.

The approach also supports SAP’s clean-core principles by helping separate the requirements of the operational ERP from the wider requirements for retaining and reusing enterprise information. Rather than making the new ERP responsible for every historical data requirement, organisations can manage operational processes and long-term information retention through complementary architectures.

For programme leaders, this means legacy retirement, data retention and AI readiness should be considered together during transformation planning rather than addressed as disconnected initiatives.

What This Means

Define the business context required by priority AI use cases

Identify the records, relationships and historical depth needed to support each use case. Test whether the intended AI environment can interpret the information correctly, rather than checking access alone.

Preserve business context before migrating or retiring applications

Assess historical data requirements before SAP S/4HANA cutover or legacy system decommissioning. Retain the relationships and business meaning needed for future reporting, compliance and AI applications.

Apply governance across every consuming environment

Establish consistent requirements for access, retention, data quality and auditability. Ensure these controls remain effective when information is shared across SAP, non-SAP applications, archives and AI tools.

Building an Enterprise Data Foundation for AI

As organisations expand their use of AI, the ability to access and interpret information across application boundaries becomes increasingly important.

Data independence offers a way to preserve enterprise knowledge beyond the lifecycle of individual systems, while helping organisations manage historical information separately from operational ERP workloads.

For SAP S/4HANA programmes, this creates an opportunity to align transformation, legacy retirement and AI readiness around a common data strategy.

The long-term value will depend on more than retaining records. Organisations must preserve business context, maintain data quality and enforce governance wherever information is used. These foundations can help ensure that enterprise AI works with information that is not only accessible, but also meaningful and trustworthy.