Service
SAP AI and ML enablement without breaking deterministic ERP
Use probabilistic services at the edge while the transactional core stays predictable and auditable.
This service is for teams that want demand forecasts, risk scoring, retrieval workflows, or operator copilots around SAP, but do not want to push uncertain logic into the ERP core. The work defines the sidecar pattern, data flows, controls, and implementation boundaries.
Start with the decision, not the model
A workable AI use case begins with a decision that is currently slow, inconsistent, or dependent on scattered information. It then asks what evidence the reviewer needs, what a good recommendation looks like, what the tool must never do, and how a wrong output will be detected. This is a more useful starting point than selecting a model or building a polished demo.
Use cases
- Forecasting workload, demand, ETA, or backlog risk.
- Summarizing incidents, tickets, or documentation with human approval loops.
- Retrieval and recommendation flows for operators working across SAP and external systems.
What the engagement covers
- Side-by-side architecture for models, prompts, vector retrieval, and feature views.
- Governance for audit trail, retention, security, and explainability.
- Decision rules for what stays deterministic in S/4HANA and what moves outside.
Assessment model
Every candidate starts with the decision it is meant to support, not with the model. The assessment identifies the input evidence, deterministic rules, uncertainty that requires judgment, permitted actions, human approval point, audit trail, and rollback path. A workflow without those boundaries is an automation experiment, not an operational capability.
When AI is the wrong tool
Do not use AI where a deterministic validation, workflow rule, database query, or ordinary integration control already solves the problem. This includes posting decisions, legal or financial approvals, production retries, and data updates that lack a clear verification step. AI is useful where interpretation or unstructured information is the bottleneck—not as a substitute for source-system discipline.
Expected outputs
- A prioritised use-case register with value, evidence, risk, and operating constraints.
- A side-by-side architecture sketch that keeps transactional truth and irreversible actions under deterministic control.
- An evaluation and human-review design suitable for the selected workflow.
- A build-versus-buy decision framing that separates product capability from operating cost and lock-in.
Decision boundary preview
| Use case type | Preferred approach | Control boundary |
|---|---|---|
| Known validation or status check | Deterministic rule, query, or workflow | Explicit logic, testable result, ordinary change control. |
| Repeated evidence gathering or knowledge retrieval | AI-assisted search, summarisation, or drafting | Reviewer checks sources and decides the next action. |
| Commercial, financial, master-data, or posting decision | Human decision supported by deterministic controls | No autonomous change; approval and audit trail remain accountable. |
| Low-risk, reversible operational action | Controlled automation after rule and recovery design | Named owner, logging, exception handling, and rollback path. |
Public-safe example
Illustrative scenario: a support analyst needs to understand a repeated integration exception. A retrieval tool can assemble related runbooks, prior evidence, and owner contacts; it can draft a handover summary; it can highlight missing information. It should not decide that a message is safe to replay or change a mapping. The difference is between reducing the time to an informed review and automating an unbounded production action.
Related decision guides
Rule-based automation versus AI · SAP planning exception automation · AI agent for SAP support · Evaluation guardrails · AI-ready support knowledge scenario
Related pages
Profile · AI routing page · Responsible AI · AI and ML around SAP · Integration architecture consulting · FAQ