SAP Business AI Use Cases: Real Examples Across Industries

Alexey Amelchenko, Head of SAP Practice at ACBAltica

Five to seven years ago, artificial intelligence was largely viewed as an experiment. It was a pilot project in a particular department, a proof of concept from a data science team, a chatbot for customer support, or a demand forecasting model.

Today, AI is becoming part of the business operating model. Companies are less likely to question whether to use AI. Instead, they are considering how deeply it can be integrated into key processes such as finance, logistics, human resources management, production, and strategic planning. 

Picture a typical manufacturing company. Procurement departments track changes in prices and delivery schedules. Sales depend on accurate demand forecasts. Human resources rushes to fill vacancies. And customer service handles a huge number of requests every day. All of these departments generate substantial useful data. Without effective management, this data can be stored inefficiently.

SAP Business AI brings artificial intelligence right into the heart of ERP, HR, supply chain, and finance systems. It goes beyond data analysis, offering real-time suggestions, automating monotonous work, reducing risks and errors, and accelerating every process.

In this article, you’ll discover real-world stories of companies using SAP and Business AI to transform their operations from smarter manufacturing planning and logistics to streamlined finance, measurable gains in speed, accuracy, and reliability. 

SAP Business AI in customer service & finance operations 

Cirque du Soleil: Automating invoice inquiries

The business problem

Cirque du Soleil processes around 70,000 invoices per year. The financial team was overwhelmed with invoice status inquiries. Each request required manual investigation and email drafting. On average, one inquiry took 30 minutes to resolve.

This resulted in growing backlogs, monotonous tasks, and little time left for meaningful, value-driven work.

The SAP Business AI solution 

To solve the problem, the company built a custom AI Invoice Assistant on SAP Business Technology Platform, powered by SAP Business AI and generative AI capabilities.

The assistant uses generative AI to:

• Review incoming emails;

• Translate messages across languages;

• Investigate invoice status in S/4HANA;

• Generate structured, contextual content;

• Route inquiries by urgency and language.

As a result, AI became an integrated part of the finance workflow, acting as a helpful assistant.

Implementation

Using SAP BTP, the company connected the assistant to ERP data and configured it to handle multilingual communication. Generative AI capabilities were applied to automate email review and response generation.

The system was trained to understand invoice context and supplier requests, whilst maintaining human monitoring for exceptions.

Business results

• Response time reduced from 30 minutes to 2 minutes;
• 25% reduction in accounts payable backlog;
• Multilingual automated supplier communication;
• Employees freed for higher-value activities;
• Improved supplier trust and experience.

AI in healthcare & intelligent document processing

DXC Technology: Supporting clinical decisions

The business problem

The DXC Technology case study illustrates the need for clinicians working with Parkinson's disease patients to have continuous, accurate visibility into patient symptoms without the dependance to the patients’ location and ability to attend the hospital.

Medical documentation included both structured and unstructured data:

• Structured: blood pressure readings, medication logs;

• Semi-structured: patient-reported symptom entries;

• Unstructured: spiral drawings used to assess tremors and motor control;

Manual processing limited speed and created an administrative burden.

The challenge was clear: unlock useful insights without adding to clinicians' already heavy workload.

The SAP Business AI solution

DXC Technology built a solution on SAP Business Technology Platform using SAP Business AI embedded in SAP Build Process Automation.

The solution includes:

• A mobile app for patients and caregivers;

• A desktop application for clinicians;

• AI-powered document processing.

Patients use the mobile app to log daily symptoms, upload spiral drawings, and enter data such as medication intake and blood pressure readings. This data flows into an AI engine that processes both structured and unstructured inputs.

SAP Business AI capabilities were used to:

• Extract data from structured and unstructured documents;

• Enrich medical data points;

• Analyse patient symptom patterns;

• Objectively classify “on” and “off” states;

• Automate document handling workflows.

Notably, the AI model continually learns from incoming data. Over time, it refines pattern recognition and improves its ability to identify symptom tendencies, enabling personalized and data-informed insights.

Implementation

AI models built with SAP Build Process Automation now automatically handle incoming data sorting, organizing, and preparing it for clinical review, removing the need for manual paperwork.

The mobile and desktop applications were integrated into a single workflow:

• Patients submit symptom data through the mobile app.

• AI analyses and classifies the data.

• Insights are presented in a structured dashboard for clinicians.

This integration reduces fragmentation and ensures that clinicians see all the necessary information in one place.

Business results

• Minimized administrative burden;
• Faster access to accurate patient data;
• Improved clinical decision support;
• More time for patient care.

AI in public sector & sustainability

City of Antibes: AI-driven green budgeting

The business problem

Under French law, the French city of Antibes, a leader in AI-driven smart city solutions, was required to "green" its budget, meaning it had to demonstrate how each expenditure item related to six environmental goals and 17 UN Sustainable Development Goals.
This meant analyzing approximately 6,000 budget transactions each year and categorizing them based on environmental and social criteria. Previously, the city relied on manual processes and Excel spreadsheets. These processes were time-consuming and increased the risk of errors, making it difficult to monitor spending transparently.
The municipality needed a solution that could automatically process large amounts of data, correctly classify expenses, and ensure complete budget transparency.

The SAP Business AI solution

The city of Antibes implemented an AI-powered green budgeting system based on SAP solutions:

• SAP Business Technology Platform;

• SAP HANA Cloud;

• SAP Business AI (including SAP AI Core and Joule).

Rather than using large, general-purpose AI models, the team opted for specialized "tiny" LLMs (language models) with hundreds of millions of parameters. This decision was deliberate; the city required precise semantic processing and accurate interpretation of budgetary and administrative terminology, rather than a broad "general" knowledge base.

SAP Business AI was used to:

• Automatically extract key data from public procurement contracts using NLP and machine learning;

• Classify budget operations;

• Match budget items with six environmental goals and 17 Sustainable Development Goals (SDGs);

• Generate interactive visualizations for transparent analysis;

• Provide clear and informed support for management decisions.

Implementation

The solution integrated procurement and financial data into SAP HANA Cloud for instant analysis. 

AI handled semantic classification and automated allocation. Human monitoring remained for validation and final approval. 

Business results

As a result of the project, the city of Antibes has achieved measurable and strategically important effects:

• 6,000 budget operations automatically classified and matched to sustainable development goals;

• 138,000 automated decisions on the distribution and matching of budget lines;

• 100% of budget credit lines optimized and linked to one or more SDGs;

• A fully formed “green” budget without manual processing.

The case study demonstrates how Business AI can serve as a tool for “regulatory intelligence” that is organically integrated into urban management processes and administrative procedures.

AI in competitive data analytics

Team Liquid: Real-time esports intelligence

The business problem

Team Liquid is one of the most famous esports organizations in the world. A professional club, Team Liquid manages teams for games such as League of Legends and Dota 2. The organization signs players, prepares them for tournaments, and develops its own analytical expertise.

The organization works with over a billion pieces of game data from millions of matches. However, to answer strategic questions, such as which heroes and strategies provide an advantage, analysts had to manually collect data from different systems and write complex queries. This process was time-consuming and slowed down preparation for competitions.

Meanwhile, players, coaches, and business teams couldn't obtain the necessary insights on their own because working with the data required technical skills.

The main challenge was to transform a vast amount of data into a fast, comprehensible, and accessible decision-making tool for the entire organization, not just analysts.

The SAP Business AI Solution

In partnership with SAP, Team Liquid created the Next-Level Esports Center, which is based on the SAP Business Technology Platform (SAP BTP).

This platform compiles over 1.6 TB of gaming data from 10 million matches stored in SAP HANA Cloud.

The system features Joule AI Co-Pilot and SAP Business AI Agents. These tools allow players, coaches, and analysts to receive answers to complex questions in plain language without the need for SQL queries or technical expertise.

The solution provides:

• Data search via natural language queries;

• Automatic integration of data from different sources;

• Real-time comparison of strategies and statistics;

• Draft recommendations;

• Probability of victory calculations;

• Analysis of opponent behavior based on historical matches.

Additionally, the organization uses SAP AI Core to train a generative AI model on thousands of professional matches and millions of amateur matches. This model predicts opponent behavior during the draft phase and takes regular game updates into account.

Implementation

Data from various gaming and internal systems has been centralized in SAP HANA Cloud.

Joule AI agents understand natural language questions and automatically find the necessary information by combining data without analyst involvement.

The real-time draft recommendation system supports decision-making during match preparation.

Business results

• 1.6 TB of game data centralized in a single platform;
• Over 1 billion data points made accessible through AI-powered analytics;
• Insights that used to take hours are now available in seconds;
• 120 new casual users accessing insights;
• Real-time draft recommendations and win probability analysis.

What do these cases have in common?

At first glance, Cirque du Soleil, DXC Technology, the city of Antibes, and Team Liquid appear to operate in completely different industries. However, their challenges are identical at the strategic level.

1. Data has become a key asset, but not always a manageable one.
   Organizations accumulate vast amounts of information, but without built-in analytics, this data does not inform management decisions.

2. The speed of decision-making is becoming a factor in competitiveness.
   Whether it's processing invoices, making clinical decisions, budgeting for a city, or preparing for a tournament, delays mean losses of time, resources, reputation, and competitive advantage.

3. AI must be integrated into the operating model rather than existing separately.
   This is a systemic process transformation, in which AI works within ERP, financial, HR, and industry systems.

In all cases, SAP Business AI solves strategic problems, not local ones. It turns data into a manageable asset, reduces operating costs, increases the transparency and manageability of processes, and scales intelligent functions across the entire organization.

Conclusion

SAP Business AI helps companies streamline routine tasks, speed up decision-making, and improve data accuracy. Examples from various industries demonstrate the solution's capabilities: from finance and healthcare to city management and sports analytics.

These case studies show only a small part of how SAP Business AI can be useful. Every industry has processes that require automation for routine tasks, big data analysis, and accurate decision-making.

If you already have SAP, you are closer to implementing such solutions than you think. SAP Business AI integrates directly with your system and allows you to automate repetitive tasks, get instant insights, support data-driven decision making, reduce errors, and increase transparency.

Above all, SAP Business AI is designed to work directly within SAP solutions. It securely processes large amounts of data from SAP ERP, HR, finance, supply chain, and manufacturing systems without demanding complex integrations.

Ready to learn how SAP Business AI can benefit your company? Contact us, and we will analyse your processes, pinpoint growth opportunities, and suggest an implementation plan.

About the author

Alexey Amelchenko

Head of SAP Practice at ACBaltica. Expert in SAP S/4HANA, SuccessFactors, BPC, SAP Analytics Cloud, BW 4/HANA, and BOBJ with over 20 years of experience.

LinkedIn