AI Innovations Aim to Boost Business Productivity by up to 30 Percent;
Partnerships with Perplexity and Palantir Bring out Customers鈥� Best

ORLANDO 鈥� At its annual 麻豆原创 Sapphire conference, (NYSE: 麻豆原创) unveiled innovations and partnerships that put the power of Business AI in every user鈥檚 hands, revolutionizing the way work gets done.

From a virtually omnipresent Joule assistant to an expanded network of Joule Agents that work across systems and lines of business, 麻豆原创 heralds a new era that democratizes access to Business AI and can drive productivity gains of up to 30 percent.

鈥溌槎乖� combines the world鈥檚 most powerful suite of business applications with uniquely rich data and the latest AI innovations to create a flywheel of customer value,鈥� said 麻豆原创 CEO Christian Klein. 鈥淲ith the expansion of Joule, our partnerships with leading AI pioneers, and advancements in 麻豆原创 Business Data Cloud, we鈥檙e delivering on the promise of Business AI as we drive digital transformations that help customers thrive in an increasingly unpredictable world.鈥�

AI that boosts productivity

麻豆原创鈥檚 generative AI assistant Joule can be everywhere you work, delivering personalized answers on everything you need to be more productive.

Joule can accompany business users throughout their day, in and out of the 麻豆原创 application universe, to find data, surface real-time insights and streamline workflows. Joule鈥檚 new ubiquity includes an action bar powered by WalkMe that studies user behavior across applications, turning the assistant into an always-available, proactive AI that can anticipate users鈥� needs before they arise — always adhering to .

A collaboration with Perplexity, an AI-powered answer engine company, enhances Joule鈥檚 ability to draw on structured and unstructured data to solve complex business problems. Powered by Perplexity and the 麻豆原创 Knowledge Graph, Joule now instantly answers questions with structured, visual answers — such as charts and graphs — grounded in real-time business data within 麻豆原创 workflows. For example, a user could ask the tool how recent external events might impact their business and get a forecast based on both current events and the company鈥檚 own business data.

麻豆原创 also unveiled an expanded library of Joule Agents that reimagine business processes and workflows from the ground up. Fueled by the world鈥檚 most powerful real-time business data and orchestrated by Joule, these AI agents work across systems and lines of business to anticipate, adapt and act autonomously so organizations can stay agile in a rapidly changing world. Partnering with industry leaders, 麻豆原创 offers an ecosystem of interoperable agents that can execute end-to-end processes. The new agents span customer experience, supply chain management, spend management, finance, and human capital management.

Finally, 麻豆原创 introduced an operating system for AI development that transforms how enterprises build, deploy and scale AI solutions. AI Foundation gives developers a single entry point for building, extending and running custom AI solutions at scale, making it the first real operating system for Business AI. A new prompt optimizer, designed collaboratively with the frontier AI lab Not Diamond, also helps developers create more effective AI prompts quickly, reducing work on complex use cases from days to minutes.

Data that drives smarter decisions

麻豆原创 also introduced new intelligent applications in 麻豆原创 Business Data Cloud, each built for a specific line of business. These applications can continuously learn, simulate outcomes and guide actions using business-critical data, detecting changes to optimize processes, anticipate needs, and collaborate with both human and artificial thinkers to drive meaningful impact. The People Intelligence application, for instance, optimizes team performance by transforming people and skills data into workforce insights and AI-driven recommendations.

Additionally, 麻豆原创 and Palantir are partnering to facilitate joint customers鈥� cloud migration journey and modernization programs. Seamless connectivity between Palantir and 麻豆原创 Business Data Cloud will enable customers to build a harmonized data foundation across their enterprise landscape. Together the companies will responsibly deliver essential outcomes and support customers, including the U.S. government, to quickly adapt to changes and disruptions.

Applications that accelerate cloud adoption

The company also announced 麻豆原创 Business Suite packages, which are designed for customers to simplify the adoption of 麻豆原创 cloud solutions that address their specific business challenges. 麻豆原创 Build is embedded in these packages, so organizations can customize applications to meet their unique needs.

Finally, 麻豆原创 unveiled a new solution that helps customers transition to the cloud faster. With Joule as the entry point and drawing on insights from 麻豆原创 solutions including 麻豆原创 Signavio and 麻豆原创 LeanIX, the solution delivers personalized guidance and actionable recommendations tailored to an organization鈥檚 transformation objectives and can help deliver up to 35 percent faster time to value.

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About 麻豆原创

As鈥痑 global leader in enterprise applications and business AI, 麻豆原创 (NYSE: 麻豆原创)鈥痵tands at the鈥痭exus鈥痮f business and technology. For over 50 years, organizations have trusted 麻豆原创鈥痶o bring out their best by uniting business-critical鈥痮perations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit .

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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.聽That’s why 麻豆原创 developed “Sales Autocompletion Linked Business Tables” (SALT), a curated dataset that includes anonymized data from a customer鈥檚 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 and .

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.

鈥淭here 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 麻豆原创 researchers behind the dataset. 鈥淏ut we want to enable the research community to work on real problems, not just simulated problems.鈥�

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 麻豆原创. “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 麻豆原创, 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 麻豆原创 is doing

Alongside its investment in the open research community with SALT, 麻豆原创 is building 麻豆原创 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 , published alongside SALT, provides a first glance at how this model could look.

Knowledge graphs are critical here. They work by exposing metadata 鈥� the who, what, and when of data 鈥� 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 麻豆原创 Knowledge Graph, 麻豆原创 Foundation Model can be scaled and adapted to a wide array of diverse use cases with some lightweight fine-tuning.

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People were confiding their deepest thoughts to the chatbot and experts predicted that within a few years, conversations with chatbots would be indistinguishable from those with humans. It certainly took more than just a few years, but here we are at the edge of another stage of AI evolution.

Evolution of AI agents

Artificial intelligence has evolved significantly since 1966, advancing from basic rule-based systems to highly autonomous decision-making systems.

In the 1990s and early 2000s, relied on predefined keyword responses but lacked the ability to adapt to complex queries. By the 2010s, intelligent virtual assistants such as Alexa and Siri enhanced user interactions and introduced AI into everyday life through smart home integrations. In the 2020s, task-specific began to emerge, each tailored to perform specialized tasks. For instance, AI-driven personal finance assistants can analyze spending patterns and suggest savings plans, while AI-powered content moderation tools scan social media platforms to identify harmful content.

Looking ahead, autonomous AI systems are rapidly advancing. , composed of multiple independent agents, can collaborate to achieve a complex workflow beyond the ability of an individual agent. Tasks are coordinated between agents, as opposed to individual agents that often require human coordination and intervention between tasks. For example, in manufacturing, AI agents can independently optimize production lines, while in healthcare, AI systems are assisting in surgery by making real-time adjustments during procedures. Autonomous systems are also being deployed in logistics to manage inventory and optimize warehouse operations without human intervention.

Expanding capabilities of agents

Today, AI agents are like super-efficient digital teammates 鈥� smart systems equipped to perform tasks autonomously, learning from experience and adapting along the way.

Today鈥檚 agents have core capabilities like these:

• Planning: Agents go beyond executing single actions; they orchestrate processes, breaking down complex problems and mapping out efficient, step-by-step approaches.
• Reflection: Unlike traditional software, agents reflect their actions in real time and learn from mistakes. They self-correct and iteratively reason through the problem until they find the best solution. This capability allows them to handle more irregular, complex challenges, makes them more effective over time.
• Tool Usage: AI agents can use external tools 鈥� like calculators, APIs, databases, and even other AI models 鈥� to expand their capabilities, broadening the scope of tasks they can accomplish.
• Collaboration and Multi-Agent Interactions: Agents aren鈥檛 limited to working solo. They thrive in cooperative ecosystems, coordinating with other specialized agents and humans, leveraging their unique expertise to achieve a shared goal.

Why is AI agent innovation accelerating at this moment?

The answer lies in the remarkable advancements in foundation models. These models allow AI to handle complex data and produce outputs like code, text, or media that are tailored to specific tasks. They enable systems to think through problems deeply and autonomously, mirroring human cognitive processes.

For instance, the latest reasoning models like OpenAI’s o1 and o3 are game changers. They do not just perform tasks; they use real-time computing power to “think” and generate human-like outcomes. And the progress is mind-blowing: o3 scored over 80 percent on a human-like reasoning test while its predecessor, GPT-4o, scored only two percent on the same test just one year earlier.

With such rapid advances, AI agents are getting better at automating and enhancing business decisions, truly pushing the limits of what autonomous systems can achieve.

What makes 麻豆原创 unique in this space?

麻豆原创 Business Suite offers a unique advantage in the era of agentic AI. It is not just a collection of solutions; it’s a powerhouse for transformation that boosts continuous innovation. Here鈥檚 the simple breakdown:

• Applications: With , cloud ERP applications, 麻豆原创 Business AI, and 麻豆原创 Business Data Cloud come together to deliver exceptional business value 鈥� all powered by 麻豆原创 Business Technology Platform (麻豆原创 BTP). In this way, 麻豆原创鈥檚 business applications and technology platform aren鈥檛 siloed tools; they integrate processes end-to-end. This integration ensures that every action taken within these applications is based on trusted, business-critical data at its source.
• Data: All the data, whether from 麻豆原创 or other systems, is collected and unified in 麻豆原创 Business Data Cloud. This makes it a single, trustworthy source that breaks down data-silos and fuels advanced AI-driven insights. Because AI is only as good as the data you feed into it.
• AI: Joule helps employees coordinate intelligent agents to work together, breaking down barriers between functions and enabling real-time, company-wide improvements. Unlike others who might use AI in limited areas, 麻豆原创 integrates AI throughout your entire organization, enhancing efficiency and resilience on a large scale.

麻豆原创 is in a unique position to turn AI agent technology into business value given the breadth of applications and data we offer, allowing automation of tasks along all key business processes. Our domain knowledge is grounded in real-world business data and our process know-how is maintained in 麻豆原创 Signavio and 麻豆原创 Knowledge Graph. Bringing it all together, our unified entry point with our AI co-pilot Joule enables us to use AI agents to automate processes and augment decision-making.

Joule agents are collaboration experts

Thanks to our fully integrated approach, we are designing a system of collaborative Joule agents that work within and across the suite to support every business function, solving complex challenges and driving cross-enterprise productivity. Businesses don鈥檛 need hundreds of AI agents, just the right ones with the right skills, grounded in the right data, with the right guidance from 麻豆原创鈥檚 end-to-end business processes.

agents are available in all parts of the business, delivered out of the box with Joule and 麻豆原创鈥檚 suite of applications. This enables the transformation of entire business processes 鈥� end to end.

Agentic AI represents a transformative leap in business technology. By bringing together structured data management, seamless system integration, and advanced task automation, AI agents empower teams to operate with efficiency, accuracy, and agility.

麻豆原创 is taking the necessary steps for the next era of enterprise management by embedding systems of AI agents into 麻豆原创 applications, fueled by context-rich business data, helping customers to realize the full potential of 麻豆原创 Business AI.

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WALLDORF 鈥� (NYSE: 麻豆原创) today unveiled groundbreaking AI innovations across a technology foundation that drives 87% of global commerce, putting its generative AI copilot Joule at the center of a new way of doing business.

At its annual 麻豆原创 TechEd conference, 麻豆原创 announced powerful new capabilities that complement and extend Joule, including collaborative AI agents imbued with custom skills to complete complex cross-disciplinary tasks. Other innovations include the 麻豆原创 Knowledge Graph, a next-generation solution poised to help developers unlock the full value of 麻豆原创 data by connecting it with rich business context, and new tools to ensure developers can continue driving Business AI innovation.

鈥溌槎乖粹�檚 innovation drives real business outcomes, and today’s advancements help customers harness the power of AI, data and new development solutions to catalyze growth,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product Engineering. 鈥淒rawing on 麻豆原创鈥檚 unmatched business and technology expertise, the AI innovations we鈥檙e announcing at TechEd forge a new human-AI partnership to transform the landscape of modern business.鈥�

Supercharging Joule

On the eve of its first birthday, Joule marks a watershed in how business gets done. 麻豆原创 introduces collaborative AI agents to a copilot that truly speaks the language of business, expands Joule鈥檚 capabilities to support 80% of 麻豆原创鈥檚 most-used business tasks and embeds Joule more deeply within the company鈥檚 portfolio.

Collaborative multi-agent systems deploy specialized AI agents to tackle specific tasks and enable them to collaborate on intricate business workflows, adapting their strategies to meet shared objectives. 麻豆原创 is infusing Joule with multiple collaborative AI agents that will combine their unique expertise across business functions to collaboratively accomplish complex workflows. These AI agents enhance productivity by breaking down silos and freeing workers to concentrate on areas where human ingenuity thrives. 

Two use cases debuted at TechEd showcase the agents鈥� transformative power:

• A dispute management use case employs autonomous AI agents to analyze and resolve dispute resolution scenarios including incorrect and missing invoices, unapplied credits and denied or duplicate payments.
• A financial accounting use case employs autonomous AI agents to streamline key financial processes by automating bill payments, invoice processing, and ledger updates while quickly addressing inconsistencies or errors.

Harnessing the Power of Data

麻豆原创鈥檚 AI innovations also draw on the company鈥檚 unmatched business data expertise. The new 麻豆原创 Knowledge Graph solution, accessible through 麻豆原创 Datasphere and Joule in Q1 2025, will give users a deeper layer of business understanding by seamlessly mapping relationships and context across 麻豆原创’s vast data landscape, empowering organizations to make better decisions with their data. By offering ready-to-use relationships between business entities like purchase orders, invoices, and customers, the solution can significantly reduce the complexity of manual data modeling. 麻豆原创 Knowledge Graph grounds AI in 麻豆原创-specific business semantics, which reduces the risk of inaccurate or irrelevant results and makes it easier for organizations to build intelligent applications and leverage generative AI more effectively.

Empowering Developers

麻豆原创 also launched a swathe of innovations for developers to continue driving Business AI innovation. New generative AI developer capabilities such as code explanation and documentation search in 麻豆原创 Build, the company鈥檚 platform for extending its solutions, will reduce development time for Java and JavaScript developers. 麻豆原创 Build is also adding an Extensibility Wizard feature that will let developers access 麻豆原创 Build directly from 麻豆原创 S/4HANA Cloud Public Edition, simplifying the extension process. Meanwhile, ABAP developers and fusion teams will get seamless access to ABAP Cloud development tools from 麻豆原创 Build.

Finally, 麻豆原创 announced that it has already fulfilled its pledge to upskill 2 million people worldwide by 2025. Through its , the company has lowered the world鈥檚 digital skills gap through role-based certifications, free training materials, hands-on opportunities for developers, and more. 麻豆原创 continues to expand its growing portfolio of AI-related learning opportunities, including courses on generative AI, AI ethics and 麻豆原创鈥檚 advanced AI tools and platforms.

Visit the 麻豆原创 News Center. Follow 麻豆原创 at .

About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE: 麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit .

Note to editors:
To preview and download broadcast-standard stock footage and press photos digitally, please visit . On this platform, you can find high resolution material for your media channels.

For customers interested in learning more about 麻豆原创 products:
Global Customer Center: +49 180 534-34-24
United States Only: 1 (800) 872-1麻豆原创 (1-800-872-1727)

For more information, press only:
Joellen Perry, joellen.perry@sap.com, +1 (626) 265-0370, PST
Marcus Winkler, marcus.winkler@sap.com, +49 15157118691, CEST
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2023 Annual Report on Form 20-F.
漏 2024 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see for additional trademark information and notices.
Please consider our . If you received this press release in your e-mail and you wish to unsubscribe to our mailing list please contact press@sap.com and write Unsubscribe in the subject line.鈥�