Organizations rely on cloud solutions from SAP to power their most critical business processes. Yet too many IT teams find themselves caught in an endless cycle of maintenance, monitoring, and firefighting, leaving little room for the strategic work that truly moves business forward. SAP Cloud Application Services can change this equation.

Cloud solutions promise simplicity: turn them on, and they just work. But behind every successful cloud transformation lies a complex web of operational challenges. Businesses adopting cloud solutions, whether public or private, still need to address topics such as testing, application performance, security, monitoring, integrations, service and change requests, and issue resolution.

The reality? Cloud solutions eliminate hardware and infrastructure concerns, but the day-to-day operational workload remains. Without proper management, businesses face risks such as downtime, inefficiencies, and an inability to scale.

SAP Cloud Application Services can help put an end to this struggle. The offerings of expert-managed services are designed to help run, optimize, and innovate cloud applications—giving businesses the tools to help unlock the full potential of their business, human capital, and technology investments.

Why cloud application management matters

When businesses run on cloud solutions from SAP, our 24×7 cloud operations can provide more than just monitoring. We can also help to actively prevent issues before they impact your operations and to respond rapidly when minutes matter. Problems can not only be fixed but also anticipated—that’s the beauty of SAP Cloud Application Services.

Offered through an outcome-based, modular subscription model, the offerings can deliver hands-on support for smooth cloud operations. For IT teams, this can mean more time to focus on strategic innovation and business transformation, instead of being overwhelmed by routine maintenance. They can leverage SAP’s deep solution expertise to help ensure continuous security updates, proactive monitoring, intelligent automation, and data-driven optimization.

More importantly, SAP Cloud Application Services can help resolve key challenges associated with cloud-based operations, such as:

• Expert-supported application management: Comprehensive cloud application management from SAP helps ensure stable and reliable operations and business continuity across the cloud landscape.
• Interconnectivity and integration: Data and processes can flow with greater reliability and speed across cloud environments without losing business context.
• Intelligent monitoring and performance optimization: Cloud applications can work together in ways that proactively prevent downtime and performance bottlenecks.
• Issue resolution and incident management: Technical issues can be swiftly identified and resolved early.
• An AI-first approach: The fusion of built-in AI and cloud application management can enable companies to focus more resources on solving business challenges effectively.
• Security and compliance: Security, compliance, and governance standards can be maintained consistently and with confidence.
• Automated testing: Regression, performance, or integration testing can be automated to help make innovation less burdensome for IT teams and the overall business.

Where more freedom drives a more strategic focus

All over the world, companies, such as Atem Distribuidora de Petróleo S.A. (Atem), are already opening the door to integrated, automated operations in the cloud with SAP Cloud Application Services.

“The unified engagement between the SAP Cloud Application Services offerings and our partner helped us deliver our upgrade of SAP S/4HANA Cloud Private Edition in a very short timeframe,” shares Nayara Araujo, IT systems manager at Atem Distribuidora de Petróleo S.A. “This project is key to our innovation plan and will enable us to continuously enhance our software landscape for many years to come.”

The close relationship that Araujo describes led to a cloud environment that’s reducing the company’s energy consumption by 80% and data footprint by 87.5%. Business operations have also been improved—for example, monthly financial closing time has fallen by 40%.

Atem’s success underscores the importance of having a single point of accountability, supported by:

• Predictable costs: Annual service packages are transparent, fixed-price, and outcome-based.
• Guaranteed performance: The offerings are backed by contractually binding service-level agreements and 24×7 global support.
• Embedded AI and automation: Intelligent monitoring, proactive issue resolution, and continuous improvements are included.
• Continuous innovation: Cloud applications are aligned with the organization’s goals and SAP’s evolving road map.
• Comprehensive governance: SAP manages everything from monitoring to security and compliance.

Atem didn’t need to deal with multiple vendors. Instead, SAP’s and the partner’s expertise, global support, and standardized service delivery were immediately accessible through one channel. This advantage led to 25% faster upgrades, 30% better usability, and 100% AI readiness.

With cloud success comes AI-driven innovation

Cloud success is more than increased uptime; it’s also about unlocking what’s next. With AI redefining everything in our world, businesses need clean, connected, and continuously managed data and AI-ready, intelligent, and scalable IT environments more than ever.

SAP Cloud Application Services can transform cloud operations into a foundation for AI-driven innovation. With intelligent monitoring, automation, and built-in AI capabilities, businesses can accelerate AI adoption, improve critical business KPIs, and scale dynamically with confidence, control, and continuous innovation.

Take the next step toward a smarter, AI-powered future today. Learn how SAP Cloud Application Services can maximize your cloud investment and contact your local service account executive to get started.

Arndt-Alexander Boehnert is global vice president for SAP Cloud Application Services at SAP.
Stefan Krampen is lead architect for SAP Cloud Application Services at SAP.

The prowess of generative AI with text has brought immense value — from writing emails and answering questions to generating wedding speeches. AI models trained to deal with text, like large language models (LLMs), have powered this value and are only getting better at natural language.

However, there are challenges when we move beyond text to apply these models to structured, tabular data, which is essential for enterprise business operations. This imbalance comes partly because of the availability of training data. Text used to train models is plentiful, often consisting of text scraped from the internet, whereas tabular data, especially data with multiple linked tables, is scarce.

To bring AI advancements to the enterprise sector, researchers working on training and benchmarking the performance of these models in an enterprise setting need realistic tabular data. That’s why SAP developed “Sales Autocompletion Linked Business Tables” (SALT), a curated dataset that includes anonymized data from a customer’s enterprise resource planning (ERP) system.

SALT is specifically designed to support researchers working on AI models for real-world business contexts and can be accessed on Hugging Face and GitHub.

Challenges of getting and working with enterprise data

Providing the research community with realistic enterprise data like SALT has been challenging. Data privacy, confidentiality, and commercial interests make obtaining large, clean, high-quality enterprise datasets difficult for training models and benchmarking them for specific use cases. This means there is a growing gap between what researchers are working on and what actual enterprise data looks like.

In addition to the problem of availability, enterprise data is complex. First, business data is usually stored in multiple interconnected tables. For example, a sales order entry may be linked to numerous tables, such as customer IDs connected to a supplier table containing address information. Second, tables are inherently heterogeneous in the data type they can contain. One field may be text, while the other contains numerical or categorical values. Finally, business data frequently shows significant column imbalances, meaning that, for example, a specific product category makes up 90 percent of all sales orders while others are rarely used.

The best way to help researchers develop enterprise models for these challenges is to provide accurate enterprise data.

SALT dataset

Accurate enterprise data is a bottleneck in AI research. The SALT dataset alleviates this bottleneck by providing the research community with the first real ERP dataset. It uses actual industry data collected by an ERP system that records sales orders. It has been minimally processed to protect privacy.

“There is a gap between academia and industry in terms of data. It cannot be closed easily because of privacy,” says Tassilo Klein, one of the SAP researchers behind the dataset. “But we want to enable the research community to work on real problems, not just simulated problems.”

ERP systems help organizations manage core business operations like finance and spending. With millions of entries and extensive, interconnected relational tables focused on sales, the SALT dataset replicates customer interactions in an ERP system. SALT’s realistic enterprise data means it is a perfect basis for helping models understand the characteristics of business data and validate their performance through benchmarking. It also should help researchers develop better foundation models for linked business data.

Getting this right will advance enterprise automation, as many enterprise business processes are heavily centered around data in structured tabular formats. Even though this data plays a crucial role in enterprise day-to-day activities, the generative AI revolution has yet to tap into them.

“SALT is a first step to providing researchers with authentic representative industry data that gives a glimpse into actual enterprise data; for now, we are starting with just one customer and use case,” shares Johannes Hoffart, CTO of Business AI at SAP. “However, we plan to publish more datasets that cover a diverse set of customers and use cases that, along with SALT, can serve as a basis for pre-training, adapting, as well as benchmarking models.”

Collaboration with academic institutions is also a motivation for publishing this data.

“At SAP, we hope to collaborate with academic partners who usually can only publish their results on open repositories,” Klein says. “Another hope for the dataset is encouraging more people to explore and validate new methods that help foundation models better deal with tabular enterprise data.”

What SAP is doing

Alongside its investment in the open research community with SALT, SAP is building SAP Foundation Model to handle enterprise tabular data. This table-native AI model aims to accelerate time-to-value for predictive tasks on tabular data, offering a model that can work with tabular data out-of-the-box with little or no additional training data. The PORTAL paper, published alongside SALT, provides a first glance at how this model could look.

Knowledge graphs are critical here. They work by exposing metadata — the who, what, and when of data — making relationships between information accessible. This provides a structured, interconnected representation of the data that AI models can easily understand and utilize. With the help of SAP Knowledge Graph, SAP Foundation Model can be scaled and adapted to a wide array of diverse use cases with some lightweight fine-tuning.

Learn more about:

• SAP Knowledge Graph
• SAP Foundation Model
• PORTAL
• SALT