Author: Priyasha Purkayastha, Global Content Manager, TJC Group | Co-author: TJC Americas team, TJC Group
As organisations accelerate their S/4HANA migration journeys, artificial intelligence is emerging as a transformative force that can dramatically enhance business outcomes. However, sustainable AI value depends on one often-overlooked asset: your historic data. This article explores how businesses can unlock the full potential of AI within their SAP landscapes during and after their digital transformation.
Table of contents
- Introduction
- The convergence of S/4HANA migration and artificial intelligence
- Why historic data matters for AI success?
- Data quality: The foundation for reliable AI predictions
- Preparing your data strategy for AI-driven transformation
- Migration approaches that preserve AI-ready data
- Key takeaways for business leaders
Introduction
SAP has made it easier than ever to start AI projects within S/4HANA. With over 350 AI features and more than 2,400 Joule skills now available, the platform offers unprecedented capabilities for intelligent automation, predictive analytics, and natural language interaction. However, these powerful tools are only as effective as the data that feeds them.
In a recent TJC Group webinar, Andreas Welsch, CEO & Chief AI Strategist and author of the AI Leadership Handbook, highlighted a critical insight: while SAP has democratised access to AI capabilities, sustainable value depends entirely on the quality and depth of your historic data. This perspective challenges organisations to rethink their approach to S/4HANA migration, not just as a technical upgrade, but as a strategic opportunity to build an AI-ready data foundation.
The convergence of S/4HANA migration and artificial intelligence
S/4HANA represents far more than a simple ERP system upgrade. Built on the SAP HANA in-memory database, it delivers real-time processing capabilities that fundamentally change how organisations can leverage their data. The 2025 release marks the ninth edition of S/4HANA, evolving from simplifying core processes to delivering intelligent, AI-augmented workflows.
The integration of Joule, SAP’s generative AI copilot, directly into S/4HANA workflows represents a paradigm shift in enterprise software. Users can now interact with their ERP system using natural language, automate complex tasks, and receive intelligent recommendations based on historical patterns and predictive models.
Key AI capabilities embedded in S/4HANA
Augmented analytics: SAP Analytics Cloud integration provides automated data exploration, natural-language interaction, and detection of data correlations
Predictive planning: Machine learning models that forecast demand, identify risks, and optimise resource allocation
Intelligent automation: AI agents that can automate routine tasks, from accounting accruals to production planning
Deep research capabilities: Joule can now synthesise explanations for complex inquiries using both internal and external data
For organisations planning their S/4HANA migration, understanding these AI capabilities is essential. The decisions you make about data migration today will directly impact your ability to leverage these intelligent features tomorrow.
Why historic data matters for AI success?
Artificial intelligence, at its core, learns from patterns in historical data to make predictions about the future. The more comprehensive and high-quality your historical data, the more accurate and valuable your AI predictions become. This fundamental principle has profound implications for S/4HANA migration strategy.
Consider a practical example: an AI model designed to predict customer payment behaviour requires years of transaction history to identify reliable patterns. It needs to understand seasonal variations, economic cycles, and customer-specific behaviours. Without sufficient historical context, even the most sophisticated algorithm will produce unreliable predictions.
The same principle applies across virtually every AI use case in the enterprise:
- Demand forecasting requires historical sales data across multiple business cycles
- Predictive maintenance needs equipment performance data spanning various operating conditions
- Credit risk assessment depends on payment history and financial patterns over time
- Supply chain optimisation requires historical data on supplier performance, lead times, and disruptions
Many organisations approaching S/4HANA migration view historical data as a burden—something to be minimised or archived away to reduce migration complexity and costs. While data volume management remains important, this perspective fails to recognise historic data as the strategic asset it has become in the age of AI.
Watch our webinar now for invaluable expert insights:
The hidden value in your legacy systems
For organisations with decades of SAP history, legacy systems contain a treasure trove of data that could fuel AI-driven competitive advantage. This data represents years of business transactions, customer interactions, supplier relationships, and operational patterns – information that cannot be recreated or purchased.
However, this valuable data often sits dormant in systems scheduled for decommissioning. The traditional approach to legacy system decommissioning focuses primarily on compliance and cost reduction: extract what’s legally required, archive it for audit purposes, and shut down the system.
While these objectives remain valid, forward-thinking organisations are now asking a different question: how can we extract maximum value from our historical data before, during, and after decommissioning?
The answer lies in treating legacy data not as a liability to be managed, but as an asset to be leveraged. This shift in perspective has several practical implications:
- Data extraction scope: Rather than extracting only what’s legally required, consider what data could provide AI training value
- Data format and accessibility: Ensure extracted data is stored in formats that AI tools can readily consume
- Data lineage and context: Preserve metadata and business context that helps AI models interpret historical patterns correctly
- Integration planning: Design data architectures that allow historical data to be combined with current S/4HANA data for AI analysis
Data quality: The foundation for reliable AI predictions
If historic data is the fuel for AI, then data quality is the refinery that determines whether that fuel powers performance or causes engine failure. Poor data quality doesn’t just reduce AI accuracy; it can lead to actively harmful predictions that erode trust and create business risk.
Common data quality issues that undermine AI effectiveness include:
- Inconsistent data formats: Dates, currencies, and units of measure that vary across systems and time periods
- Duplicate records: Multiple entries for the same customer, supplier, or product that skew pattern analysis
- Missing values: Gaps in historical records that create blind spots in AI training
- Outdated information: Master data that no longer reflects current business relationships
- Data silos: Information trapped in disconnected systems that prevents holistic analysis
S/4HANA migration presents a unique opportunity to address these quality issues. The simplified data model in S/4HANA consolidates many tables—for example, customer and vendor accounting tables are now unified in a universal ledger. This consolidation, while requiring careful data transformation during migration, creates a cleaner foundation for AI analysis.
Organisations should view data quality improvement not as a migration prerequisite alone, but as an ongoing capability that will continue to deliver value as AI adoption matures. Implementing data governance frameworks, quality monitoring tools, and remediation processes during migration establishes practices that will support AI initiatives for years to come.
Preparing your data strategy for AI-driven transformation
Successful AI adoption requires more than just preserving historical data—it requires a deliberate strategy that aligns data management with AI objectives. For organisations planning S/4HANA migration, this means integrating AI considerations into every phase of the transformation journey.
Assessment phase
Before migration begins, conduct a comprehensive assessment that goes beyond traditional readiness checks:
- AI use case identification: Work with business stakeholders to identify high-value AI applications that could benefit from historical data
- Data inventory: Catalogue historical data across all systems, including legacy environments scheduled for decommissioning
- Quality baseline: Assess current data quality levels and identify remediation priorities
- Gap analysis: Determine what additional data might need to be captured or preserved to support AI objectives
Planning phase
Incorporate AI requirements into migration planning decisions:
- Migration approach selection: Consider how greenfield, brownfield, or hybrid approaches impact historical data availability
- Archiving strategy: Develop data archiving approaches that balance storage costs with AI accessibility requirements
- Integration architecture: Design data flows that enable AI tools to access both current and historical information
- Timeline considerations: Allow sufficient time for data quality remediation before migration
Execution phase
During migration, maintain focus on data integrity:
- Validation protocols: Implement rigorous testing to ensure historical data transfers accurately
- Transformation documentation: Record all data transformations to maintain lineage for AI model training
- Quality gates: Establish checkpoints that verify data quality meets AI requirements before proceeding
Post-migration phase
After go-live, continue building AI capabilities:
- Model training: Begin training AI models on the combined historical and current data
- Performance monitoring: Track AI prediction accuracy and identify data quality issues that may require attention
- Continuous improvement: Establish feedback loops that improve data quality based on AI performance
Migration approaches that preserve AI-ready data
The choice of S/4HANA migration approach significantly impacts your ability to leverage historical data for AI. Each approach presents different trade-offs that organisations must carefully evaluate.
Brownfield migration (system conversion)
A brownfield approach converts your existing SAP system to S/4HANA, preserving historical data and custom configurations. This approach offers the most straightforward path to maintaining AI-ready historical data, as transaction history remains intact within the converted system.
However, brownfield migrations also carry forward data quality issues from the legacy environment. Organisations choosing this path should invest in data cleansing before conversion to ensure historical data is AI-ready.
Greenfield migration (new implementation)
A greenfield approach implements S/4HANA as a completely new system, offering a fresh start with optimised processes and clean data. While this approach simplifies the migration itself, it creates challenges for AI adoption because historical data must be explicitly migrated or made accessible through integration.
Watch our webinar on how to select the right S/4HANA migration approach:
Organisations pursuing greenfield implementations should develop comprehensive strategies for preserving access to historical data, whether through data migration, legacy system retention, or specialised archiving solutions.
Hybrid approach (selective data transition)
A hybrid approach combines elements of both greenfield and brownfield, selectively migrating data and configurations based on business value. This approach offers flexibility to optimise for AI requirements, migrating high-value historical data and leaving behind low-quality or irrelevant information.
The hybrid approach requires sophisticated planning to ensure AI-critical data is identified and properly transitioned. Working with experienced partners who understand both migration complexity and AI data requirements is essential.
Key takeaways for business leaders
As you plan your S/4HANA migration, consider these essential recommendations for unlocking AI potential:
- Reframe historical data as a strategic asset: Stop viewing legacy data purely as a compliance burden. Recognise that your decades of business history represent irreplaceable training data for AI models that could drive competitive advantage.
- Integrate AI planning into migration strategy: Don’t treat AI as a future consideration. Incorporate AI use cases and data requirements into your migration planning from day one to avoid costly rework later.
- Invest in data quality before migration: AI models amplify data quality issues. Use the migration as an opportunity to establish data quality foundations that will support AI adoption for years to come.
- Preserve access to legacy data: Even when decommissioning legacy systems, ensure historical data remains accessible in formats that AI tools can consume. The cost of preservation is minimal compared to the potential value of AI insights.
Successful AI-ready migration requires expertise in SAP transformation, data management, and AI implementation. The organisations that will thrive in the AI-powered future are those that recognise today’s S/4HANA migration as more than a technical upgrade. It’s an opportunity to build the data foundation that will fuel intelligent automation, predictive insights, and competitive differentiation for decades to come.
TJC Group, with over 25+ years of experience in SAP data volume management and legacy system decommissioning, helps organisations navigate this complex intersection. Contact us now to discover more!
