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 .
Following the presentation of the 鈥淪caling Innovation鈥� category finalists, we now turn to 鈥淓merging Ideas,鈥� which honors visionary concepts at an earlier stage鈥攑rojects that explore new architectural directions, challenge established models, and open long-term strategic opportunities for 麻豆原创 and its customers. The winners will be announced during the award ceremony on March 26, 2026.
麻豆原创 Cognitive Twin Enterprise (CTE)
Modern enterprises are very effective at monitoring their business and analyzing vast amounts of data, yet many still see untapped potential in safely testing complex scenarios end to end and turning insights into cross鈥慺unctional, policy鈥慳ligned options before making mission鈥慶ritical decisions. 麻豆原创 Cognitive Twin Enterprise (麻豆原创 CTE) addresses this gap by creating an AI鈥憄owered digital brain built on a continuously updated model of the whole organization. It runs what鈥慽f simulations and provides governed recommendations on 麻豆原创 applications and data across finance, spend, supply chain, HR, and customer experience, with selective, low鈥憆isk auto鈥慹xecution and human鈥慽n鈥憈he鈥憀oop control for higher鈥憆isk steps.
The business case is compelling. Organizations that combine digital twins with agentic AI at scale report double鈥慸igit improvements in efficiency and cost, plus materially faster decision cycles. For a global industrial enterprise with approximately 鈧�40 billion in revenue, 麻豆原创 CTE is modeled to systematically prevent margin leakage, excess working capital, and audit exposure, delivering an estimated 鈧�229 million or more per year in hard impact and risk-adjusted cash benefit. By maintaining a continuously updated representation of the business, companies can test scenarios before execution and dramatically reduce the risk of costly mistakes.
麻豆原创 CTE鈥檚 real differentiator is its enterprise鈥憌ide scope. It consolidates existing 麻豆原创 capabilities and builds on 麻豆原创 Signavio solutions, 麻豆原创 Business Data Cloud, and 麻豆原创 Knowledge Graph to maintain a shared semantic model of how the whole business runs. This cross鈥慸omain intelligence lets Joule and AI agents optimize complex trade鈥憃ffs鈥攕uch as cost versus service level versus carbon footprint versus operational risk鈥攁cross all functions, rather than pushing problems from one silo to another. At the same time, 麻豆原创 CTE provides a safe innovation environment: enterprises can trial new pricing strategies, network configurations, and workforce models in a production鈥慻rade twin before agents execute changes in live systems.
麻豆原创 CTE represents a strategic shift in how enterprises operate. It turns 麻豆原创鈥檚 deep process knowledge, rich transactional data, and mature governance tooling into a differentiated position in a cognitive twin market that analysts expect to accelerate from US$36 billion today to US$150 billion by 2032, with 30%-40% annual growth. As an extensible platform, 麻豆原创 CTE is designed to be the trusted operational brain for that future: new agents, scenarios, and data products plug into the same enterprise twin, allowing customers to expand autonomy and business impact over time without rebuilding their foundation.
鈥溌槎乖� CTE is more than an initiative: it鈥檚 our vision for a new era of connected intelligence. We鈥檙e bringing strategy, data, and execution into one continuous system of insight, so customers don鈥檛 just react to change鈥攖hey anticipate what鈥檚 next and shape it. That鈥檚 how we win and grow together,鈥� said Natalia Aksakova, Strategy & Portfolio at Global Finance and Administration.
Finalist fast facts
Submission Title: 麻豆原创 Cognitive Twin Enterprise (CTE)
Team: Natalia Aksakova, Silvina Guastavino, Cvetelina Dizova, Dorothee Hofstetter, Ekaterina Pechenina, Janine Weissenfels, Holger Handel, Michael Emerson
Project: It explores an AI-driven cognitive model of the enterprise that connects data, planning, simulation, and AI agents into a governed decision-and-execution loop. It enables organizations to test scenarios, anticipate risks, and act proactively across finance, spend, supply chain, HR, and customer experience domains.
Impact: It positions 麻豆原创 at the forefront of cognitive enterprise architecture by shifting from reactive systems of record toward predictive, simulation-driven, AI-supported decision-making and execution.
麻豆原创 Signavio Transformation Advisor
Organizations planning business transformations face a persistent bottleneck: identifying the right challenges to focus on and creating actionable initiatives is slow, costly, and heavily dependent on expert consultants and detailed knowledge of the organization. This traditional approach delays decision-making and increases risk in fast-changing markets, with analysis often taking weeks or months to complete.
麻豆原创 Signavio Transformation Advisor reimagines this workflow by using AI to extract business challenges and create actionable recommendations to solve them in minutes. The solution identifies business challenges in uploaded reports or via text input and instantly generates recommendations linked to process insights and best practices to make them addressable. By combining advanced language models with the 麻豆原创 Signavio portfolio鈥榮 process knowledge, it enables users to achieve in minutes what previously required weeks of manual effort while keeping users in full control.
Early results demonstrate significant impact. The tool cuts analysis time by up to 80%, enabling faster decision-making and reducing reliance on scarce consulting resources. Since launch, approximately 200 customers have tested the transformation advisor, validating its value across organizations at different maturity levels. The solution has proven valuable both for customer engagements and for internal use in preparing sales pitches.
The innovation lies in bridging strategic business challenges and operational processes in a way no existing tool does. It automatically identifies organizational pain points and links them to targeted process flows, best practices, and improvement opportunities within the 麻豆原创 Signavio ecosystem. This seamless integration empowers leaders to move from insight to action in just a few clicks, aligning transformation initiatives with company strategy.
The team embraced a proactive and entrepreneurial mindset: it started with a pure technical proof of concept then moved to a prototype for internal demonstrations, general accessibility and testing, and ultimately a releasable feature. The team demonstrated both transparency and customer focus by responding early to pull from go-to-market and sales teams while clearly stating tool limitations at each stage.
“The real fun in developing such a solution lies in seeing your idea and your knowledge grow at the same time and getting a clear pull from the market early on. The best customer sessions were those where the tool was improved live during the interview. That combined is a clear signal that we are on the right track,鈥� said Alex Cramer, product manager at 麻豆原创 Signavio Next.
Finalist fast facts
Submission Title: 麻豆原创 Signavio Transformation Advisor
Team: Alexander Cramer, Matthias Wiench, Shehab Shalan, Rolan Badrislamov
Project: It is an AI-powered solution that analyzes business inputs and generates structured, actionable transformation recommendations connected to 麻豆原创 Signavio Process Insights.
Impact: It significantly reduces transformation analysis time, lowers reliance on manual consulting efforts, and enables organizations to move from strategy to execution faster and more confidently.
AURA (Asset Understanding & Reliability AI)
Field engineers maintaining critical infrastructure face a frustrating reality: reporting asset faults requires completing complex forms on mobile devices, scrolling through endless dropdowns and codes. At one heavy equipment and infrastructure customer, 300 users report 400 to 1,000 asset faults monthly through 麻豆原创 S/4HANA, but the process is slow, manual, and error prone. A single classification mistake can send the wrong maintenance crew and delay urgent fixes.
AURA (Asset Understanding & Reliability AI) revolutionizes this workflow by combining 麻豆原创 HANA Cloud vector engine, 麻豆原创 AI Core, and generative AI into a single intelligent solution. Instead of completing eight or more complex form fields, engineers simply upload a photo of the fault; review an AI-generated report automatically populated with asset type, location, and recommended classification; and confirm submission鈥攁ll within seconds.
The technology uses embedded text, semantic search, and geospatial data to analyze both images and historical fault reports. AURA cross-references similar cases in the knowledge base, suggests the most accurate fault category, and learns from user corrections over time. 麻豆原创 Cloud Application Programming Model provides the secure foundation, 麻豆原创 HANA geospatial content supports asset location intelligence, and AI models process text and images using 麻豆原创 HANA Cloud vector engine for similarity matching.
Results demonstrate substantial operational impact. AURA delivers 80% faster fault reporting, fewer data entry errors and misclassifications, and improved response times. For the customer, this translates to safer infrastructure, reduced operational costs, and a future-ready foundation for predictive maintenance. The response validated the approach: the customer loved the proof of concept and agreed to proceed with AURA as an official project.
Beyond defect detection, AURA lays the groundwork for scalable AI asset intelligence. Future phases include building a knowledge graph to link asset relationships, a data product integrated into 麻豆原创 Business Data Cloud for advanced reporting, and a self-learning model that continuously improves accuracy. This creates a repeatable, cost-efficient framework adaptable across industries.
The solution embeds responsible AI principles from inception. The model uses customer-specific historical data to prevent bias, includes human review before submission, and explicitly handles uncertainty to avoid hallucinations. It ensures transparency and compliance with 麻豆原创’s responsible AI framework while empowering human decision-makers.
鈥淲e believe the future of AI is not replacing people, but elevating them,鈥� said Ruth Peng, AI specialist from 麻豆原创 HANA ANZ. 鈥淎URA equips every engineer in the field, from junior to expert, with the confidence to perform at their best.鈥�
Finalist fast facts
Submission Title: AURA (Asset Understanding & Reliability AI)
Team: Ruth Peng, Shuba Dutta, Shonali Kellogg
Project: It uses AI-driven image recognition and enterprise integration to automate fault reporting in 麻豆原创 S/4HANA. Engineers can upload photos of faulty assets and the system generates structured reports automatically.
Impact: It reduces reporting time by up to 80%, lowers classification errors, and improves operational efficiency in asset-intensive environments.
