SAP Business AI brings AI into SAP processes – from embedded functions to the copilot Joule. The decisive factor is the translation into your reality: governance, data, operation, costs (AI units) and architecture (SAP BTP & side-by-side). This guide gives you orientation, shows typical SAP Business AI use cases and the next realistic step.
TL;DR – Key facts in brief
SAP Business AI bundles SAP’s AI portfolio, which is intended to make process AI productively usable in SAP applications: Joule as a copilot/orchestrator, embedded AI and AI services via SAP BTP. For IT, this primarily means clarifying architecture, authorization and governance issues so that AI in SAP can be controlled, audited and operated – regardless of whether systems run in the cloud, hybrid or on-premise.
SAP teams are currently facing a real balancing act: cloud speed, rising expectations from the specialist departments (Joule & Co.) – and at the same time strict requirements for authorizations, auditing, operation and costs.
This is exactly where Business AI can help: not as “AI everywhere”, but as support in the typical bottlenecks in SAP operations and processes – e.g. when quickly classifying documents/tickets, recognizing exceptions, merging context from data, logs and documentation or preparing the next steps. With clean governance and a suitable architecture, the benefits become measurable and manageable.
Before you go live with SAP Business AI, it’s worth doing a quick reality check: which AI scenarios fit your landscape? In SAP in particular, it is not so much the features that are decisive, but rather consumption (AI units), authorizations, operating model (cloud/hybrid/on-prem) and the question of whether you use embedded or extend side-by-side.
SAP Business AI is not an “add-on” that you activate on the side. It changes how SAP processes are managed and operated: AI is moving into the standard, making governance, cost control and architecture important management issues.
When evaluating SAP Business AI, a simple filter helps: where is “invisible work” being created today? In other words, work that arises daily in SAP operations but hardly creates any value – searching, sorting, summarizing, matching, forwarding, clarifying queries. These steps are often the best place to start because they don’t change the core process, but save a lot of time. Here are typical use cases that many companies start with:
Many new AI functions are emerging in the cloud, while central SAP systems in companies continue to be operated in hybrid or completely on-premise mode.
SAP Business AI is clearly cloud-first: Functions such as Joule or new generative scenarios are primarily provided in SAP cloud environments. For on-premise landscapes, this does not mean exclusion, but a different integration model.
The REALTECH aiLAB is a practice and strategy room for SAP teams who want to realistically evaluate and advance business AI in a controlled manner. Together, we sharpen use cases, check feasibility and define the guidelines for productive use. The result: clear priorities, a resilient approach – including an initial prototype if required.
Realize your AI vision together with REALTECH. Let us examine together which SAP AI use cases have the greatest leverage for your company.
aiLAB – Discovery Call
SAP Business AI is SAP’s approach to bringing AI functions directly into applications and processes – from Joule as a copilot to embedded AI scenarios and services via SAP BTP. The decisive factor is not “AI per se”, but its controlled use: clear tasks, reliable data, clean governance and an operable model.
Joule is SAP’s copilot that provides users with contextual support – e.g. when searching, summarizing, explaining and initiating process steps across SAP solutions. In practice, the benefits depend heavily on which data, authorizations and processes are connected and how governance and logging are implemented.
SAP charges for certain AI functions via AI Units – in simple terms via usage-based consumption (depending on the respective scenario/service). It is important to create transparency at an early stage: which use cases consume what, how are limits/monitoring implemented and how does this fit into your cost model.
To get off to a good start, you need clear responsibilities and guidelines: subject-specific owners for use cases, IT/architecture for integration, security/compliance for rules and an operating model (monitoring, approvals, support). Particularly important: a common understanding of which tasks AI is allowed to take on – and which should deliberately remain with humans.
Good pilots are clearly definable, low-risk and measurable. Typical examples include ticket/query triage with knowledge access, document/receipt support or a side-by-side prototype on SAP BTP. A clear KPI (time, quality, queries, throughput time) and a clean governance framework are crucial.
The best start is a clearly defined use case with metrics – not an “AI rollout”. Define data access and authorizations, define governance (logging, approvals, compliance) and validate the approach first as a pilot. In the REALTECH aiLAB, you prioritize use cases, check feasibility and derive a resilient procedure for pilot and operation.
The REALTECH aiLAB is a format in which SAP teams classify and structure business AI in a practical way: Prioritize use cases, evaluate feasibility and define guidelines for governance and operation. The result is a reliable basis for decision-making – including an initial prototype for validation if required.
AI functions arise in many SAP areas – often where processes are standardized and data is cleanly available (e.g. Finance, Procurement, HR, Sales/Service). The specific functions available depend heavily on your SAP product landscape, cloud/hybrid setup, releases and licenses.
Many new AI functions are available close to the cloud, but on-premise is not fundamentally ruled out. In practice, it is often hybrid: the ERP remains on-prem, AI services are connected via SAP BTP and operated outside the core system. Data sovereignty, security and a clearly defined integration model are important here.
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