One of the most time-consuming tasks in SAP ECC to S/4HANA migration projects is understanding how the existing system actually operates. ABAP programs developed over the years, integrations with different systems, undocumented customizations, and interdependent business processes can create issues that were not anticipated at the outset.
Successfully completing the technical migration alone is not enough in a transformation project. Once the system goes live, production orders must still be created, financial postings must be processed correctly, and critical integrations must continue to function.
Artificial intelligence is opening up new possibilities for managing exactly this kind of complexity in SAP transformation projects.
SAP’s Agent-Led Transformation approach, announced in 2026, aims to support a wide range of project activities with AI-assisted capabilities, from system analysis and custom code transformation to data management and testing.
So, what could this approach change in real-world projects? What problems are the seven transformation assistants designed to address? And how are the responsibilities of SAP Basis teams evolving?
1. SAP’s New Approach: What Is Agent-Led Transformation?
Automation is not a new concept in SAP transformation projects. Tools have long been used for activities such as system readiness checks, custom code analysis, and data migration.
What makes the new approach different is the aim to assess the information generated by these tools within a more integrated structure and to use AI agents for specific transformation tasks.
At Sapphire 2026, SAP announced seven Migration and Modernization Assistants. These assistants are designed as components of what SAP calls the Agent-Led Toolchain.
Two concepts should be distinguished here:
AI Agent: A software component capable of gathering information, performing analysis, and carrying out actions within the permissions defined for it in pursuit of a specific objective.
AI Assistant: A capability that brings together multiple agents and AI functions to perform tasks within a specific transformation domain.
In SAP’s approach, SAP Cloud ALM provides the operational foundation for the transformation lifecycle, while Joule supports interaction between users and AI capabilities. Solutions such as SAP Signavio and SAP LeanIX also provide process and enterprise architecture context.
The objective is not simply to ask AI general questions about an SAP system, but to support transformation activities by using the organization’s own system-specific information.
It is also important to note that, as of September 2026, it would not be accurate to regard all seven assistants announced by SAP as a single product package at the same level of maturity and generally available to every customer. While certain capabilities, such as the custom code transformation agent, have already been made generally available, the scope and release timelines of the other assistants vary.
For this reason, project planning should clearly distinguish between currently available product capabilities and SAP’s future roadmap. Availability, supported releases, and licensing requirements should always be verified against the latest product documentation.
2. AI Across Seven Areas of SAP S/4HANA Transformation
The seven assistants announced by SAP focus on complementary activities within the transformation project. Each addresses a different challenge, but their real value comes from their ability to work in relation to one another.
SAP’s 7 Transformation Assistants and Their Core Responsibilities
| Assistant | Core responsibility |
|---|---|
| System Analysis | Analyzes the existing SAP environment and creates system knowledge that can be used by the other assistants. |
| Data Management | Identifies data inconsistencies and provides recommendations for remediation. |
| Custom Code | Supports the assessment and modernization of ABAP developments for S/4HANA compatibility. |
| Configuration | Generates recommendations for target-system configuration. |
| Test Management | Supports the preparation of test scenarios and test automation. |
| Rollout | Helps assess alignment between the central SAP template and local requirements. |
| Project Management | Supports the tracking of project progress, tasks, and risks. |
Note: The table summarizes the intended functions of the assistants. It does not mean that all of them are generally available.
2.1. System Analysis Assistant
Transformation plans prepared without a sufficiently detailed understanding of the existing system often require repeated revisions during implementation.
The System Analysis Assistant aims to analyze data, customizations, and custom code within the existing SAP environment and create a shared knowledge foundation that other assistants can also use.
For example, it may become easier to understand which business process an ABAP program supports or which components it depends on.
The value here is not simply in producing analysis reports faster, but in helping ensure that decisions are based on current and consistent system information.
However, identifying technical dependencies does not automatically mean that the business importance of an application can be assessed correctly. Input from business teams remains essential.
2.2. Data Management Assistant
Data quality issues are often known before a transformation begins, but their full extent may only become clear once data migration activities are underway.
Incomplete material master data, incorrect customer records, or inconsistent fields can create problems later in the migration process.
The Data Management Assistant aims to identify inconsistencies, investigate their root causes, and recommend corrective actions.
However, data cleansing is not purely a technical activity. For example, deciding whether two apparently duplicate customer records can actually be merged may require both commercial and accounting judgment.
AI can identify the issue, but final decisions concerning critical master data should remain with the authorized business teams.
2.3. Custom Code Assistant
Custom ABAP developments are among the areas that require the greatest level of expertise in an S/4HANA migration.
A program developed years ago may require code changes in order to work correctly in the new environment. In some cases, however, standard SAP functionality may already exist that addresses the same business requirement.
The Custom Code Assistant aims to support the analysis, classification, and modernization of custom developments.
There is also a concrete product capability in this area: SAP made the S/4HANA Custom Code Migration Agent generally available in the second quarter of 2026. The agent can run ABAP Test Cockpit (ATC) checks, interpret findings, and perform certain code corrections. Recommendations with a low confidence level are left for developer review. Changes can also be tracked through transport requests.
An equally important question is not only how legacy code should be migrated, but whether it actually needs to be migrated at all.
AI-assisted code transformation should not mean transferring every existing customization into the new environment. When developments are assessed, standard SAP functionality, Clean Core principles, and appropriate extensibility options should also be considered.
The Custom Code Assistant can support technical adaptations, but expert teams should decide which developments should be retained, redesigned, or retired.
2.4. Configuration Assistant
Configuring the target system in transformation projects is not simply a matter of copying existing settings as they are.
The Configuration Assistant aims to generate recommendations for the target system by evaluating customer requirements together with SAP standard practices.
Product scope is important here. SAP’s training materials describe the target system and capabilities of this assistant in the context of different cloud editions. Its applicability should therefore be validated against the selected S/4HANA edition and the latest product documentation.
2.5. Test Management Assistant
Migrating the code and bringing the system online does not prove that business processes are functioning correctly.
The Test Management Assistant aims to generate test scope and scenarios by using SAP Signavio process models and requirements. Integration with Tricentis also supports automated test execution processes.
For example, in an order-to-cash process, validation should cover not only whether an invoice is generated, but also whether inventory, accounting, and related integration outcomes are correct.
AI can accelerate test preparation. However, process owners must still determine which business outcomes are acceptable for a test to be considered successful.
2.6. Rollout Assistant
For companies operating in multiple countries, applying a central SAP template in exactly the same way in every location is not always possible.
The Rollout Assistant aims to compare the central template with local business processes and identify the adaptations that may be required.
Türkiye’s e-Invoice and e-Ledger requirements are good examples of why localization needs to be evaluated separately.
2.7. Project Management Assistant
In SAP transformation projects, a delay in one work package often affects multiple other teams.
The Project Management Assistant aims to support progress tracking, route tasks to the relevant people, and identify risks at an early stage.
For example, a delay in data cleansing may affect integration testing and can be reflected in the project plan.
This requires project information to be kept up to date. Reliable risk analysis cannot be generated from a project plan that does not reflect actual progress.
Further Reading
You can check out our article, [SAP S/4HANA Migration Preparation Guide], to minimize data migration risks and seamlessly plan your preparation steps during your S/4HANA transformation journey.
3. Application Scenario: Migrating a Manufacturing Company in Türkiye from ECC to S/4HANA
To make the topic more concrete, consider a manufacturing company operating in Türkiye that has been using SAP ECC for many years.
Its production, sales, procurement, and finance processes run on SAP. The system also contains numerous custom ABAP developments, integrations with external systems, and data quality issues accumulated over the years.
The target is to migrate to SAP Cloud ERP Private.
Note: The following example is a representative scenario. It does not assume that all assistants can currently be used together.
First stage: Understanding the existing system
System analysis is used to identify custom developments, technical dependencies, and the current architecture. Together with the business units, the project team determines which applications are actually still in use.
Second stage: Preparing data and custom code
AI-assisted tools are used to identify data inconsistencies and custom code compatibility issues.
The ABAP team reviews the remediation recommendations. Developments that are no longer needed are separated from critical applications that must be retained in the new environment.
Third stage: Target system and local requirements
SAP’s localization documentation for Türkiye shows that these processes may require country-specific configurations and, in some cases, integration providers in addition to standard system functionality.
There is an important point here: the fact that a global system template works technically does not mean that all business requirements in Türkiye are fully covered.
In transformation projects in Türkiye, e-Invoice, e-Ledger, and Revenue Administration (GİB) requirements must be assessed separately. Especially in environments that use private integrators, it is not enough for the target system to function correctly only within SAP.
Document creation, transmission, status feedback, and the completion of accounting postings should all be tested end to end.
Fourth stage: Testing and go-live
Critical business processes are tested end to end, from production orders and inventory movements to delivery, invoicing, and accounting postings.
AI can support the definition of test scope. Validating the results, however, remains a shared responsibility of application teams, Basis specialists, and process owners.
In this scenario, AI plays a supporting role by connecting activities across different teams. Migration decisions remain with project management and authorized experts.
4. Can AI Really Reduce Transformation Costs and Timelines?
In an assessment published in August 2026, SAP stated that its AI-assisted transformation approach aims to reduce total transformation effort by approximately 35%.
It would not be accurate to interpret this figure as a level of savings that will be achieved in every project.
For example, accelerating custom code analysis may not reduce overall project cost by the same proportion. Data cleansing may take longer than expected, business teams may not be able to allocate sufficient resources to testing, or critical integrations may need to be redeveloped.
Time saved in one activity is not the same as a reduction in the total project duration.
For this reason, the value of AI investments should be assessed through measurable pilot initiatives.
For example, comparing a conventional approach with an AI-assisted approach on a defined ABAP development package can provide concrete results in terms of analysis time, remediation quality, rework requirements, and total expert effort.
5. The Role of SAP Basis Teams in AI-Assisted Transformation
SAP Basis teams are responsible for areas such as system architecture, technical readiness, installation, system copies, security, performance, and operational continuity during transformation projects.
Greater use of AI does not eliminate these responsibilities. It changes how some of the work is performed.
For example, AI may analyze system dependencies and identify potential issues. However, evaluating the target system’s resource requirements, integration architecture, and tolerance for downtime still requires an understanding of actual operating conditions.
Similarly, a successful automated code correction does not guarantee that the application will run without issues in the production environment.
For a Basis specialist, the key question is not simply whether a change can be implemented technically, but whether the system can continue to operate securely and sustainably.
For this reason, architectural decisions, authorization design, performance validation, rollback planning, and go-live controls remain critical in AI-assisted transformations.
Basis teams not only need to become familiar with these new tools, but also need to know how to validate the results they produce.
6. Risks: Should Every AI-Recommended Action Be Implemented?
SAP systems manage critical business processes. An incorrect configuration change, faulty data correction, or incomplete code adaptation can have cascading consequences.
For this reason, the permissions granted to AI agents should be clearly defined.
Especially for changes made in production environments, appropriate access controls, human approval, audit logs, and rollback mechanisms should be in place.
Data security is another important consideration. Organizations should evaluate which system data AI will be allowed to access and on what infrastructure that data will be processed.
SAP’s approach of leaving low-confidence changes in its custom code transformation agent for developer review is a useful example of how automation and human oversight can be designed to work together.
Product availability, licensing conditions, and supported releases should also be verified at the beginning of the project.
7. Conclusion: Building an AI-Ready Transformation Strategy
The real value of AI in SAP S/4HANA transformation lies not only in accelerating individual tasks, but in its potential to bring activities such as system analysis, custom code, data, configuration, and testing—often handled by separate teams—closer together within a shared system context.
This can be particularly valuable in SAP environments that have grown over many years and contain large numbers of custom developments and integrations. In transformation projects, the main challenge is often not the technical activity itself, but understanding which dependency affects which business process and determining the right sequence of actions.
However, AI does not replace the need to understand the existing SAP environment, design the right target architecture, or make critical project decisions. Even the most advanced tool will struggle to produce reliable outcomes if it is working with incomplete or inaccurate system information.
For this reason, an AI-ready S/4HANA transformation should begin with making the current environment visible. Organizations need to maintain up-to-date inventories of custom code, data, integrations, and business processes. They can then measure where AI-assisted tools deliver tangible value, while ensuring that critical changes remain subject to expert review.
For projects in Türkiye in particular, the global transformation approach should also incorporate local regulatory requirements, e-Transformation processes, GİB integrations, and any private integrators in use as an integral part of the project design.
As SAP’s transformation assistants continue to mature, the way project teams work is also likely to evolve. Rather than making specialists less valuable, a more realistic expectation is that they will spend less time on repetitive analysis and focus more on higher-value areas such as architecture, risk, validation, and decision quality.
Ultimately, the formula for a successful S/4HANA transformation does not change; the tools simply become more powerful. Sound architecture, clean and reliable data, controlled custom code, experienced specialists, and strong project management will remain the foundation. When AI is applied appropriately on top of that foundation, it can make transformation faster, more transparent, and easier to manage.
Frequently Asked Questions
AI can be used in areas such as system analysis, data quality, custom ABAP code adaptation, configuration, test management, and project tracking. The goal is to reduce repetitive work and help specialists make faster, data-driven decisions.
The assistants announced by SAP cover System Analysis, Data Management, Custom Code, Configuration, Test Management, Rollout, and Project Management. Each assistant is designed to support specific transformation activities with AI.
As of September 2026, not all assistants are at the same level of maturity or availability. Some capabilities, such as the custom code transformation agent, are generally available. For the others, the latest product scope, supported releases, and roadmap should be checked.
AI can support the analysis of the existing system and integrations, code adaptations, and the preparation of test scenarios. In projects in Türkiye, e-Invoice, e-Ledger, and GİB requirements should also be addressed separately, and local processes and private integrator connections should be validated end to end.
AI can make the work of Basis specialists easier in areas such as system analysis and certain automation activities. However, system architecture, security, performance, technical migration, operational continuity, and the validation of critical changes still require expert oversight.