REALTECH-AG-Logo

REALTECH aiLAB

SAP Business AI: Joule, use cases & launch plan

SAP Business AI brings AI into SAP processes – from embedded AI to Copilot Joule. With SAP’s current vision of the Autonomous Enterprise, this topic is becoming increasingly important: In the future, AI should not only assist but also be more deeply embedded in business processes, data context, and governance. The key is translating this into your reality: governance, data, operations, costs (AI Units), and architecture (SAP BTP & Side-by-Side). This guide provides you with direction, highlights typical SAP Business AI use cases, and outlines the next realistic step.

Use cases
How to start

TL;DR – Key facts in brief

  • Definition: SAP Business AI is SAP’s approach to integrating AI into workflows – from Joule as an assistant to AI functions that support decisions and process steps directly within SAP.
  • Where teams start: side-by-side prototyping on the SAP BTP is a common way to get started.
  • Cloud-first, but hybrid: many AI services run close to the cloud, while ERP remains stable on-prem. BTP creates a side-by-side model that enables innovation without touching the SAP core.
  • Latest update: With Autonomous Enterprise, SAP Business AI is shifting its focus more toward AI agents, Joule Studio, and process-oriented automation. This makes the structured evaluation of use cases, data infrastructure, governance, and operating models even more critical for businesses.
  • Why aiLAB: in the REALTECH aiLAB, possibilities become a plan: prioritize use cases, check feasibility in your landscape and define guard rails - as a basis for piloting and scaling.

What is SAP Business AI?

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 Joule

The generative AI copilot from SAP, integrated into S/4HANA, SuccessFactors, Ariba and other solutions. SAP Joule provides context-based answers and suggested actions directly in the workflow.

Embedded AI

Ready-made AI functions directly in SAP modules – e.g. for forecasts or recognizing exceptions. Advantage: anchored in the process and linked to existing data and roles.

SAP BTP & AI Core

The SAP Business Technology Platform is the basis for integration, side-by-side extensions and the operation of AI services. SAP AI Core is the runtime and orchestration layer for AI.

SAP Business AI vs. Joule vs. AI on SAP BTP

  • SAP Business AI: The broad term for AI in SAP processes – ranging from embedded AI capabilities in SAP applications to advanced AI scenarios at the platform level.
  • Joule: The AI assistant and gateway to your workflow — ask questions, retrieve context, get suggestions, and, depending on your permissions, initiate tasks or actions.
  • AI on SAP BTP: The platform layer for custom AI scenarios — with services for development, integration, side-by-side extensions, governance, operation, and scaling of AI applications and agents.
  • Long story short: SAP Business AI provides the business framework. Joule is the AI assistant within the workflow. SAP BTP is the platform for extensions, integration, and operations.

From SAP Business AI to Autonomous Enterprise

SAP uses the term “Autonomous Enterprise” to describe the next stage in the evolution of business AI: AI is no longer intended merely to support individual tasks, but rather to increasingly guide, prepare, and – where appropriate and manageable – execute business processes in a context-based manner.

The focus is on AI agents based on SAP process knowledge, corporate data, and clear governance rules. SAP is integrating this development with the SAP Business AI Platform, Joule, Joule Studio, and new ways to embed agent-based AI into business processes.

For businesses, this means that business AI is becoming more strategic. It is no longer just about individual AI functions, but about determining which processes are suitable for AI-driven decision-making, automation, and assistance, and what conditions need to be put in place to make this possible.

Advantages, but realistic: where business AI helps

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.

With Autonomous Enterprise, this balancing act becomes even more critical. AI is not only intended to streamline individual tasks but also to increasingly support end-to-end processes. This also raises the bar for quality, transparency, data access, approvals, and human oversight.

This is exactly where Business AI can help: not as “AI everywhere,” but as targeted support for typical bottlenecks in SAP operations and business processes – for example, when quickly categorizing documents or tickets, identifying exceptions, consolidating context from data, logs, and documentation, or preparing the next steps. With sound governance and the right architecture, the benefits become measurable and manageable.

For companies

Controllable productivity, more impact in the process

  • Faster through automation: routine work is relieved, exceptions are visible earlier and processes are accelerated.
  • Deciding with context: patterns, anomalies and forecasts supplement reporting with concrete impulses for action.
  • Managing risks & compliance: transparency, traceability and clear rules support governance in the process.

For development teams

Integrate AI cleanly – without “Clean Core risk”

  • Embedded instead of a stand-alone solution: AI sits in the workflow and uses existing roles, data flows and authorizations.
  • Side-by-side via SAP BTP: integrate your own scenarios without changing the SAP core (Clean Core).
  • Run-ready scaling: standards plus monitoring for quality, operation and costs/usage (AI Units).

AI in SAP: stumbling blocks & strategic questions

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.

AI Units & license costs

Consumption models scale quickly if usage is not monitored. Without a use case focus, limits and monitoring, unnecessary cost risks arise.

Data quality & master data

AI reinforces existing data patterns. Unclear or inconsistent master data leads to incorrect results and decreasing trust.

Cloud/BTP dependencies

Many AI functions require SAP BTP and cloud connectivity. Hybrid or on-premise landscapes need clear integration strategies.

Change & adoption in the teams

AI changes working methods and decision-making logic. Without communication, training and approval models, there will be no adoption.

AI readiness & target image

Is there already a clear AI strategy or just individual initiatives? Without a target image and prioritization, there will be no scalable deployment.

Build vs. buy vs. embedded

Do you use embedded AI, activate assistance – or build side-by-side on the BTP? The decision determines effort, differentiation and operation.

Why is SAP Business AI strategically important?

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.

AI becomes part of standard work

As soon as Joule and embedded AI run in Finance, Purchasing or Service, expectations in the specialist area increase. SAP teams have to determine where AI provides support, where human approvals are mandatory and which processes deliberately remain AI-free.

SAP is increasingly approaching Business AI from an agent-based perspective

With Joule, Joule Studio, and the SAP Business AI Platform, SAP Business AI continues to evolve toward agent-based scenarios. This means that AI is not only intended to provide information, but also to support, prepare, or execute tasks across multiple steps within defined parameters.

Governance becomes a prerequisite for productivity

Without clear guard rails (roles/authorizations, logging, approvals, traceability), shadow usage, unclear responsibility and audit risks arise – especially in generative scenarios.

Costs and use must be controllable

Consumption models such as AI Units make transparency mandatory: Which functions are used? By whom? In which processes? And which KPI justifies the consumption?

Architecture decisions depend on the clean core

Many companies start hybrid: core system stable, AI added value via side-by-side/integration. It is crucial that extensions are clean-core-capable and operationally viable.

In the REALTECH aiLAB, we translate these questions into a pragmatic setup: Use case prioritization, governance light, pilot design and a decision template for rollout.

AI functions in SAP: typical use cases

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:

Service: Ticket processing

AI supports initial processing: inquiries are structured, summarized and linked to suitable teams or knowledge sources – with clear guidelines for authorizations and transparency.

Finance & Controlling: Recognize deviations faster

AI can help to make anomalies in figures, documents or process steps visible earlier and support clarification – especially where a lot of time is spent searching, reconciling and reworking.

Procure-to-Pay: process documents and receipts efficiently

A frequent entry point is support with standardized document tasks: Capturing, checking and summarizing content and preparing it for downstream steps.

Developer Enablement (SAP BTP)

Teams use AI to sketch and test side-by-side scenarios on the BTP – including MVP scoping, architecture decisions and initial integration logic towards processes, APIs or events.

How SAP Business AI works on-premise

Many of SAP’s new AI features continue to be developed with a focus on the cloud. At the same time, SAP is increasingly making Business AI features available to existing customers with on-premise ECC or S/4HANA environments. For companies, this means that on-premise deployment is no longer a barrier to entry – though implementation typically occurs through hybrid architectures, SAP BTP, cloud services, and clearly defined integration models.

This is why a realistic architectural decision is particularly important in the context of the Autonomous Enterprise. Not every AI or agent scenario is implemented directly within the existing ERP core. Often, the most sensible approach is to start with SAP BTP, integration services, and controlled data access. This allows AI scenarios to be tested, expanded, and operated without unnecessarily altering the stable core of the ERP system.

SAP Business AI remains strongly cloud- and platform-oriented: features such as Joule, Joule Studio, AI agents, and generative AI scenarios are primarily delivered through SAP Cloud and BTP services. This does not exclude on-premise environments, but rather involves a different access and integration model.

Hybrid architecture

The ERP system (e.g. S/4HANA On-Premise) remains stable. AI services are connected via SAP BTP and operated outside the core system.

BTP as an integration layer

SAP BTP handles the integration, deployment and control of AI services (e.g. via AI Core). This creates a controllable side-by-side structure.

Controlled innovation

On-premise often means higher operating costs. In return, you retain data sovereignty, compliance control and architectural sovereignty.

Data under control

Not all data needs to be included in AI scenarios. What matters is which data is used, where it is processed, and how compliance is ensured.

Using SAP Business AI strategically – with REALTECH aiLAB

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.

Innovation space

We provide orientation for SAP teams: target picture, priorities and a realistic starting point for Business AI.

Use case validation

Together, we identify and evaluate the use cases with the highest value contribution for your organization.

Basis for decision-making

We provide a framework for AI governance that combines the EU AI Act, data protection and SAP best practices.

Sparring partner

Not a sales pitch, but a strategic dialog at eye level – from SAP architects for SAP decision-makers.

Ready for the next step?

Realize your AI vision together with REALTECH. Let us examine together which SAP AI use cases have the greatest leverage for your company.

REALTECH aiLAB

FAQs: SAP Business AI

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.

With Autonomous Enterprise, SAP describes a vision for enterprise software in which people, AI agents, data, and processes work more closely together. AI is not only intended to provide information, but also to support business processes in a context-aware manner and, within clearly defined limits, to prepare or execute tasks. For companies, this means that Business AI is more closely linked to governance, data quality, architecture, and process ownership.

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.

SAP Business AI encompasses SAP’s AI functions, services, and platform capabilities — such as Joule, Embedded AI, and AI services via SAP BTP. The Autonomous Enterprise is the overarching vision: business processes are intended to become smarter, more interconnected, and partially autonomous. SAP Business AI is thus a key building block on the path to the Autonomous Enterprise.

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: 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.

Further topics

SAP BTP at a glance

The most important BTP modules for integration, side-by-side and AI in SAP.

REALTECH aiLAB

Prioritize use cases, clarify feasibility, set guidelines – in a practical way in the aiLAB.

SAP Clean Core

Less complexity at the core, more room for innovation and expansion.

Create your AI Strategy

From AI ambitions to a clear, realistic roadmap.

Table of contents

What is SAP Business AI?
Autonomous Enterprise
Advantages
Challenges
Strategy
Use cases
On-premise AI
How to start
FAQs