SAP Meets AI: 8 Use Cases That Redefine Your Processes

Artificial intelligence is changing the way companies work, but only when it can actually communicate with the systems where day-to-day operations live. Orders, inventory levels, supplier data, maintenance histories, financial postings: all of this resides in ERP systems, and in most mid-sized and large enterprises that essentially means SAP. Anyone using AI agents without connecting them to this data foundation is automating past reality. An AI agent that cannot retrieve order data, trigger SAP transactions, or update master data remains an expensive demonstration tool. The Model Context Protocol (MCP) closes exactly this gap and makes SAP not only readable for AI systems, but controllable.

This article shows you, using eight concrete use cases, what the connection between AI agents and SAP can look like in practice, from sales to maintenance to customer service.

What Is SAP MCP – And Why Is OData No Longer Enough?

MCP is an open protocol that allows AI systems to use external tools and data sources in a structured way. In the SAP context, this means: an AI agent can read SAP tables, call BAPIs, execute ABAP reports, and even control SAP GUI transactions in the background, all via a standardized interface.

OData is currently the most widely used approach to make SAP data accessible for AI systems. The problem: before an AI agent can even access SAP data, OData services must first be developed, configured, and maintained, a process that consumes SAP development resources and a significant amount of time. And even after this effort, OData hits structural limits in an AI context: it cannot map complex transaction logic, cannot access non-standard SAP objects, and cannot integrate custom developments such as Z function modules and Z reports. In short: even after significant implementation effort, OData only covers a fraction of what AI agents need for real process automation.

Another often overlooked aspect: while cloud-based SAP assistants are mostly limited to S/4HANA Public Cloud, MCP enables the same AI-driven access for SAP ECC and S/4HANA On-Premises, where most business-critical data and processes still reside.

The real value often emerges only through the combination of SAP with other systems. Many companies operate additional platforms alongside SAP – such as Salesforce for CRM, ServiceNow for IT processes, or specialized industry solutions. Since MCP is an open protocol, multiple systems can be connected through the same architecture. An AI agent can combine SAP inventory and financial data with Salesforce customer data or service information from other sources in a single workflow. Only this cross-system integration creates truly end-to-end value chains – and elevates AI automation from point solutions to process-level transformation.

From AI Enthusiasm To A Concrete Question: Which Use Cases Actually Work?

Enthusiasm for AI in enterprise contexts is high – and disillusionment often follows quickly. Not because the technology is not powerful, but because one key question remains unanswered: what can actually be implemented in my specific SAP landscape?

Pilot projects often fail not because of AI, but because of missing or insufficient ERP integration – exactly the gap MCP fills.

Another issue is that many common use cases are either too abstract to support an implementation decision, or they refer to SAP environments that have little in common with the real-world systems of typical companies. The question “will this work for us – with our ECC system, our Z developments, our existing processes?” often remains open.

The following eight use cases are not conceptual sketches, but practical scenarios with concrete problem contexts, technical classification, and measurable value – developed from real customer requirements. They are meant to help you identify the most sensible entry point in your organization.

8 Use Cases For SAP & AI With MCP

1. Intelligent Sales Assistant: From SAP Lookup To Instant Answer

The problem: Sales employees spend an estimated one third of their working day searching for information in SAP: has the order been shipped? Are 500 units in stock? Which special price applies for customer X? Each of these questions costs time and delays customer responses. If Salesforce data is also connected via MCP, the agent can combine SAP inventory and pricing data with current opportunity status or customer segments from CRM in one step – without switching systems.

The solution: An AI agent, embedded for example in Microsoft Teams, answers these questions in real time. It accesses SAP BAPIs and tables via MCP, delivers answers in natural language, and can even generate and send a pro forma invoice directly from SAP on request.

The value: Faster customer response times, noticeably higher customer satisfaction, and more capacity for actual sales conversations.

2. Automated Supplier And Employee Onboarding

The problem: Creating a new supplier or employee in SAP requires manual entry into complex transactions with dozens of mandatory fields. Input errors directly affect downstream accounting and compliance processes.

The solution: Users upload a document, such as a tax form or ID card. The AI extracts relevant fields, validates the data, and fills the SAP transaction automatically in the background. No manual entry, no transfer errors.

The value: Higher master data quality, significantly faster onboarding processes, and noticeable relief for HR and procurement teams.

3. Ad-Hoc Executive Reporting Without IT Tickets

The problem: A manager needs short-term margin information for a specific product group, region, and time period. This data is not in a standard dashboard. Options: wait for controlling or manually navigate complex SAP reports.

The solution: The AI agent receives the question in natural language, identifies the appropriate SAP standard or Z report, executes it via MCP, analyzes the output, and delivers a structured summary – including trend visualization or charts.

The value: Faster data-driven decisions without waiting time, and reduced ad-hoc workload for IT and finance.

4. AI-Supported Maintenance And Warehouse Management: Trigger SAP Processes On Site

The problem: Employees in warehouses or production discover issues such as broken machines, damaged goods, or inventory errors. This creates a process break: the incident must be documented and manually entered into SAP at a terminal, often requiring system knowledge or help from others.

The solution: Employees describe the situation via voice or text from a mobile device. The AI identifies the appropriate SAP process and triggers it directly: inventory document, maintenance notification, or storage block.

The value: SAP processes are triggered where they occur, in real time, without SAP expertise, reducing delays and improving data quality.

5. Secure Customer Self-Service Portal With Real-Time SAP Access

The problem: Customers frequently request order status, invoices, or delivery dates. Simple chatbots fail either due to missing SAP integration or lack of secure data isolation.

The solution: After authentication, the verified customer ID is automatically passed as a mandatory filter to every MCP call. The MCP server enforces this filter for all data requests, ensuring that only data belonging to the authenticated customer is returned. Data isolation is enforced architecturally, not in the prompt.

The value: True 24/7 self-service, drastically fewer “where is my order” requests, and a modern transparency experience comparable to e-commerce platforms.

6. Material Master And Variant Management Without Excel Workarounds

The problem: Many companies manage product variants in excel and transfer them manually into SAP, causing inconsistencies and errors.

The solution: Engineers describe a new variant in chat, for example a connector variant with higher heat resistance. The AI identifies the base product, calculates differences, and creates the variant directly in SAP.

The value: Faster time-to-market, no data inconsistencies, and fewer process breaks in product development.

7. Automated Returns Processing And Credit Note Management

The problem: Returns are high-volume and low-margin processes requiring manual warranty checks, stock availability checks, and credit note creation in SAP finance.

The solution: An AI chatbot handles the entire customer interaction. Via MCP, it checks warranty status, inventory, triggers rma processes, and creates credit notes in SAP finance – fully automated.

The value: Scalable returns processing, lower service costs, and better post-purchase experience.

8. AI In The Call Center: Real-Time SAP Knowledge For Every Agent

The problem: Call center agents must navigate multiple SAP transactions during live calls to retrieve order history or contract data, increasing handling time and error rates.

The solution: An AI assistant identifies the caller, aggregates relevant SAP data via MCP, and presents it to the agent, along with suggested next steps.

The value: Shorter call duration, fewer transfers, and higher first contact resolution rates.

What These Use Cases Have In Common – And What Makes Them Possible

Across all scenarios, the core principle is the same: AI is not just an information layer answering questions. It becomes an active part of the process – reading SAP data, deriving actions, and writing results back into the system. Sometimes this is a simple table query, sometimes a full multi-step GUI transaction executed in the background.

The key enabler is full access to SAP objects: tables, BAPIs, ABAP reports, and GUI transactions. Only with this full spectrum can end-to-end process automation be achieved. The value increases even further when other systems are integrated through the same MCP architecture, creating cross-system value chains across ERP, CRM, and service platforms.

Another practical advantage: this architecture works even in complex SAP landscapes with custom developments, Z objects, and legacy structures – both on-premises and in the cloud.

Conclusion: MCP As An Enabler For The Next Stage Of SAP Automation

Companies that keep SAP and AI separate are leaving significant efficiency potential untapped. The use cases show that this is not future vision, but already implementable today using existing SAP infrastructure – without big-bang migrations.

The MCP server architecture transforms SAP from an isolated ERP system into a controllable part of an intelligent process landscape – in real time, with full data isolation, and without the complexity of traditional SAP development projects.

The right starting point is not a large transformation program, but a single clearly defined use case that solves a real pain point and delivers ROI.

‍Do you recognize yourself in one of these scenarios?

Talk to our Proofis and discuss your specific use case. We will show you live how your scenario works with MCP and SAP.

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