Six teams are competing for the highest employee recognition at 麻豆原创: the Hasso Plattner Founders鈥� Award. Starting this year, the Hasso Plattner Founders鈥� Award comes with a modified, more focused approach. It now consists of two categories: 鈥淪caling Innovation鈥� and 鈥淓merging Ideas.鈥� Both reflect a different type of breakthrough thinking and the various ways in which innovation drives 麻豆原创鈥檚 success. This year鈥檚 award theme is .
The first category, “Scaling Innovation,” honors project teams that excel in innovation, align with 麻豆原创’s strategic priorities, and demonstrate significant and sustainable impact on the company. The winners will be announced during the award ceremony on March 26, 2026.
麻豆原创 Joule for Developers, ABAP AI capabilities
As organizations worldwide accelerate their transition to 麻豆原创 S/4HANA, one reality remains unchanged: ABAP continues to power the core of 麻豆原创鈥檚 technology and the mission鈥慶ritical business processes that run on it. At the same time, developers working with this backbone technology have long lacked the modern AI鈥慸riven tooling available in other programming ecosystems鈥攁n issue that becomes even more pressing during large鈥憇cale transformation projects.
This gap is precisely what 麻豆原创 Joule for Developers, ABAP AI capabilities addresses. As part of the broader Joule generative AI portfolio, it brings AI capabilities tailored for ABAP development directly into the hands of development teams. Its mission: to modernize the developer experience of writing, understanding, and maintaining ABAP code and to dramatically accelerate innovation at enterprise scale.
麻豆原创 Joule for Developers, ABAP AI capabilities is natively integrated into the ABAP development tools. It supports developers in a hybrid approach that utilizes a large language model fine-tuned by 麻豆原创 data scientists on millions of lines of ABAP code, along with commercial models augmented with context derived from decades of 麻豆原创 expertise. This equips developers with specialized capabilities covering everyday tasks like predictive code completion, unit test generation, real-time explanation, and chat-based assistance鈥攕ignificantly boosting productivity and developer satisfaction.
Complementing this, ABAP AI for custom code migration redefines how organizations approach the complex task of revamping legacy custom code from 麻豆原创 ERP Central Component to 麻豆原创 S/4HANA. What once required weeks of manual analysis can now be accomplished in hours, with AI explaining legacy logic, highlighting needed adaptations, and generating migration proposals. Integrated into the Custom Code Migration app on 麻豆原创 Business Technology Platform (麻豆原创 BTP), it empowers project managers and consultants to better scope work packages and plan code migration project timelines with far greater accuracy.
麻豆原创 Joule for Developers, ABAP AI capabilities already serve thousands of developers across more than 280 customers, 470 partners, and 6,500 internal ABAP developers, with adoption growing rapidly inside 麻豆原创 and across the ecosystem. Evolving from a 2023 proof鈥憃f鈥慶oncept to enterprise availability in 2025, the project stands as a testament to what cross鈥憃rganizational collaboration between ABAP, AI, and 麻豆原创 S/4HANA teams can achieve鈥攂ringing innovation to one of 麻豆原创鈥檚 most essential developer communities.
Team lead Jasmin Gruschke, AI architect and project expert, describes the extraordinary team spirit: 鈥淯nited by a shared vision and customer dedication, we poured our collective energy and dedication into bringing an extraordinary idea to life, demonstrating that, together, we can turn visionary concepts into remarkable realities.鈥�
Finalist fast facts
Submission Title: 麻豆原创 Joule for Developers, ABAP AI capabilities
Team: Jasmin Gruschke, Hasan Al Abed, Manuel Berning, Cristina Buchholz, Thomas Alexander Ritter, Ashok Veilumuthu, Amey Tathawadekar, Tobias Melcher, Cristina Diana Popa, Steffen Bickel
Project: Delivers advanced AI capabilities that modernize and accelerate ABAP development. It supports developers with intelligent code assistance, automated analysis of legacy logic, and fast generation of modernization proposals. By embedding AI directly into development and migration workflows, it reduces manual effort and helps teams modernize systems with greater speed and confidence.
Impact: It shortens modernization timelines by turning weeks of manual code analysis into hours. It is widely adopted across more than 280 customers, 470 partners, and 6,500 麻豆原创 developers, improving productivity, code quality, and migration accuracy.
麻豆原创 Document AI
Modern enterprises face a growing obstacle in an increasingly data鈥慽ntense world, reflected in the rapid proliferation of unstructured business documents. From invoices and purchase orders to contracts and shipping papers, companies are drowning in information that demands time鈥慶onsuming manual processing. 麻豆原创 Document AI tackles this challenge head鈥憃n by transforming the way enterprises extract, process, and act on document鈥慴ased data.
Today, more than 30,000 customers rely on the solution to process billions of documents, embedded seamlessly across 麻豆原创鈥檚 core applications. 麻豆原创 Document AI delivers enterprise鈥慻rade automation without costly integrations or extensive model training, enabling businesses to accelerate workflows, reduce errors, and improve decision鈥憁aking at scale. Real鈥憌orld customer data shows the tangible impact: automated document processing powered by 麻豆原创 Document AI generates an estimated 鈧�2.6 billion in annual business value.
麻豆原创 Document AI is now natively integrated into 32 business processes across 麻豆原创 S/4HANA, 麻豆原创 Business Network, 麻豆原创 Concur solutions, 麻豆原创 Fieldglass solutions, 麻豆原创 SuccessFactors solutions, the 麻豆原创 Customer Experience portfolio, and 麻豆原创 BTP, with dozens more use cases in development. This deep embedding of 鈥渆veryday AI鈥� into products that are already in use by customers is a key driver of adoption across 麻豆原创鈥檚 global installed base.
The technology behind the solution sets new industry benchmarks. 麻豆原创 was among the early innovators in schema-based zero-shot document processing, an approach that enables AI systems to understand complex documents without task-specific retraining, and which is now widely adopted across the AI ecosystem. Even before the rise of large language models, 麻豆原创 researchers advanced the field with award-winning and trend-setting papers such as CharGrid, BERTgrid, and Charmer, AI methods designed to help computers understand documents.
The team continues to innovate with AI that learns instantly from user feedback, 鈥渟ees鈥� and interprets documents visually, and understands content well enough to take intelligent actions on it. Their next generation of generative AI models now support over 110 languages. Building on these innovations, platform usage on 麻豆原创 BTP for custom document automation has increased 285-fold since 2020, underscoring how developers worldwide are leveraging this technology to streamline business processes. Next, 麻豆原创 Document AI will launch reusable tools that empower AI agents to handle complex document workflows across industries. As unstructured data and diverse document types become central to business processes, demand for smarter, faster, and more adaptable document understanding solutions has never been higher.
Tobias Weller, chief product owner and team lead, states: 鈥淲e built 麻豆原创 Document AI to deliver measurable business value at global scale, securely, responsibly, and embedded in everyday processes, demonstrating 麻豆原创鈥檚 ability to operationalize AI at massive scale.鈥�
Finalist fast facts
Submission Title: 麻豆原创 Document AI
Team: Tobias Weller, Tomasz Janasz, Christoph Lenschow, Smita Naveen, Hongxin Shao, Ashish Kumar, Nay Lin Aung, Komal Narsinghani, Subashini Rengarajan, Sebastian Koebe
Project: It introduces scalable AI that automates the extraction and understanding of unstructured business documents across 麻豆原创鈥檚 portfolio. It streamlines end鈥憈o鈥慹nd processing, eliminates manual data entry, supports more than 110 languages, and embeds intelligent automation into 32+ 麻豆原创 processes鈥攎aking document handling faster, more accurate, and effortless for organizations of all sizes.
Impact: By automating billions of documents for over 30,000 customers, this solution generates an estimated 鈧�2.6鈥痓illion in annual business value. It reduces errors, accelerates workflows, and drives adoption of embedded AI across 麻豆原创 applications. Rapid scaling, multilingual coverage, and rising platform usage highlight its measurable enterprise鈥憌ide impact.
麻豆原创 SuccessFactors Learning: GenAI Content Generation
With AI reshaping work at unprecedented speed, organizations face a dual challenge: mastering new skills and managing overwhelming amounts of information. 麻豆原创 SuccessFactors Learning: GenAI Content Generation is a game-changing capability designed to streamline, accelerate, and scale how learning content is produced across the enterprise.
The new capability leverages advanced large language models (LLMs) to convert simple prompts or uploaded files into complete, compliant learning experiences in minutes. What previously required days, weeks, or even months, can now be accomplished almost instantly. The system generates course outlines, quizzes, interactive elements, summaries, and assessments鈥攁ll tailored to the user鈥檚 input and organizational context.
A key innovation lies in its multi鈥慙LM orchestration, enabling dynamic selection and combination of specialized models. This ensures high accuracy, domain relevance, and enterprise鈥慻rade content governance. Real-time multilingual translation allows learning teams to launch global courses simultaneously, while AI-based skill extraction automatically aligns content with workforce development strategies.
Early validation shows organizations can produce learning content five times faster, dramatically reducing costs and enabling teams to respond more quickly to shifting skill demands. Subject-matter experts throughout the organization can now create content and share knowledge more effectively鈥攚ithout requiring instructional design expertise. By transforming knowledge into structured, scalable learning experiences, the capability helps organizations strengthen agility, boost employee engagement, and ensure continuous upskilling across the enterprise.
Team lead Neha Dhawan, principal product manager, 麻豆原创 SuccessFactors Learning, describes the impact of the project: 鈥淲e鈥檙e not just building technology, we鈥檙e building possibilities鈥攆or admins to move faster, for managers to better support their teams, and for learners to experience content that feels personal and meaningful. If we can make learning more accessible and inspiring, then we鈥檝e created something that truly matters.鈥�
Finalist fast facts
Submission Title: 麻豆原创 SuccessFactors Learning: GenAI Content Generation
Team:聽Max Schneider, Neha Dhawan, Josh Passman, Michelle Duchow, Gregor Boltz, Madhavi Aji
Project:聽An AI-driven approach that transforms raw knowledge into complete learning experiences within 麻豆原创 SuccessFactors Learning. It generates courses, quizzes, translations, and skill tagging from simple prompts or files, speeding up creation and ensuring global scalability.
Impact:聽It cuts content development time by a factor of five, reduces costs, strengthens knowledge sharing, and enables organizations to upskill faster and stay agile in rapidly changing AI-driven work environments.
