AI can help your SAP migration team work through more findings, recommendations and test scenarios. The harder question is whether your organization has a reliable process for deciding which outputs are safe to use.
Key takeaways for enterprise buyers and leaders
- Confirm product access and applicability before building AI capabilities into the migration schedule.
- Define acceptance criteria before asking an assistant to recommend or execute a change.
- Measure accepted outcomes, including reconciliation and test results, rather than the volume of generated outputs.
- Keep accountable business owners involved in decisions affecting financial results, access and operational continuity.
What has changed in SAP migration tooling?
In its October 28, 2026 announcement, SAP described four migration assistants operating with early adopter customers. They cover data management, testing, custom code and business configuration. SAP also outlined additional planned assistants.
This is an early adopter milestone, not confirmation of general availability for every customer. Organizations should verify eligibility, target system requirements, commercial terms and permitted usage with SAP before committing project dependencies.
The controls below are practical editorial recommendations for evaluating AI assisted SAP migration. They are not a description of features included in a particular SAP product.
Start with an acceptance decision, then choose automation
A useful starting point is a task your team can explain and verify. “Prepare a migration assessment” is too broad. “Identify supplier records that fail an agreed completeness rule and provide the supporting evidence” gives reviewers a bounded result to evaluate.
For each candidate task, document the input, permitted action, expected output and acceptance owner. State whether the assistant may only suggest a change, prepare it for review or execute an approved action. These permissions should follow the sensitivity of the task.
A generated recommendation should enter a review process with a named owner. It should not become an approved requirement simply because it appears in a polished report.
Data: automate preparation, validate business meaning
Begin with a defined population and rules approved by the relevant data owners. Supplier master data, customer balances and material records require different checks. A technical field validation can succeed while the business interpretation remains wrong.
For a proposed correction, retain the original value, suggested value, reason and approval. Separate obvious formatting corrections from changes affecting payment instructions, tax treatment or organizational ownership. The latter require review by someone authorized to make that business decision.
Evidence to request before acceptance
- Source and target record counts with explained exclusions.
- Reconciliation of relevant financial balances and business control totals.
- A reviewable list of rejected records, unresolved exceptions and assigned owners.
- Repeatable validation rules that can be rerun after the final migration load.
Do not close a data workstream because a dashboard reports a high completion rate. Close it when the accountable owner accepts the reconciled population and understands the remaining exceptions.
Custom code: validate the process behind the program
A code finding is a starting point for investigation. Before accepting remediation, identify the process the code supports, the users who depend on it and the interfaces it touches. Ask whether the requirement remains necessary in the target landscape.
Consider whether standard functionality can meet the requirement, whether an extension remains justified and where that extension should operate. An automated correction can preserve a technically valid solution that the business no longer needs.
Review proposed changes through the existing development and transport controls. Require understandable change records, appropriate access and a tested recovery approach. For critical logic, include a functional reviewer alongside the developer.
Organizations planning SAP S/4HANA migration and implementation should connect code decisions to the target operating model rather than treating remediation as an isolated technical backlog.
Testing: generated cases still need meaningful coverage
A large collection of test cases can create confidence without demonstrating readiness. Start with the business processes that must continue operating, then map tests to requirements, integrations, roles and expected outcomes.
Review whether the proposed scenarios include exceptions. A successful sales order is useful, but so are blocked credit, partial delivery, a rejected interface message and an unsuccessful authorization attempt. Select scenarios according to the organization’s actual risks and process design.
Questions for the test owner
- Does each critical requirement have an agreed expected result?
- Does the test data represent the conditions the business needs to validate?
- Are integration failures and recovery procedures included where relevant?
- Can a reviewer trace a reported pass to execution evidence?
- Are unresolved defects assessed for business impact before acceptance?
Keep business acceptance separate from automated execution status. A tool can report that a script completed. The responsible business owner must decide whether the evidence demonstrates an acceptable outcome.
Configuration: review the consequences across functions
Configuration choices often affect teams beyond the person reviewing the change. A recommendation involving organizational structures, approval limits or account determination should be evaluated with the functions that own those outcomes.
Keep an approved design record explaining the requirement and the reason for the selected configuration. Record dependencies and distinguish reusable template decisions from local exceptions. This helps reviewers understand whether an apparent inconsistency is a defect or a deliberate business choice.
Before promoting a change, demonstrate its effect in an appropriate test environment. Review both the intended outcome and relevant boundaries, including who can perform the action and what happens when a condition is not met.
A hypothetical example: preparing supplier data
Consider a fictional manufacturer preparing supplier records for migration. Its team uses an assistant to identify missing fields and suggest records that may represent the same supplier.
The team does not approve automatic merging based only on similar names. Procurement reviews the business relationship, finance checks the relevant accounting implications and an authorized owner reviews any proposed changes to payment information.
Approved corrections are applied through the organization’s controlled process. The team then repeats its validation and reconciles the migration population. Unresolved records remain visible with owners and a documented decision about their treatment.
The value of this approach is a clearer review process and traceable decisions. It does not assume a particular product feature, a guaranteed reduction in effort or a result achieved by SAPSOL Technologies LLC.
Plan the skills required to review AI outputs
AI assistance changes the work reviewers perform. Teams need people who understand the business requirement, can examine the supporting evidence and know when to reject or escalate a recommendation.
Build review responsibilities into the project plan. Data owners need decision time. Functional leads need access to realistic scenarios. Developers need clear extension principles. Test leads need acceptance criteria that business owners understand.
SAP corporate training and workforce development can support these responsibilities by addressing specific skills gaps before critical project checkpoints. Where capacity is missing, define the required expertise before requesting additional resources.
A practical approval checklist
Before accepting an AI assisted work package, ask:
- Is the tool available and appropriate for this system and task?
- Are the inputs authorized, representative and sufficiently complete?
- Is the proposed action within the agreed permission boundary?
- Can reviewers reproduce the relevant validation?
- Are exceptions, dependencies and unresolved risks documented?
- Has the accountable owner accepted the evidence?
- Is there a tested recovery procedure if the change produces an unexpected result?
Use the answers to make a release decision. If the evidence is incomplete, narrow the scope or return the work for correction rather than lowering the acceptance standard.
Make AI assistance part of a controlled migration
The strongest migration plan connects automation to specific business outcomes and clear responsibilities. It explains what may be automated, what requires review and what evidence is needed before a change moves forward.
Begin with a bounded workstream, establish a baseline and evaluate the complete effort, including review and rework. Expand only when the accepted results justify doing so.
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