Six teams are competing for the highest employee recognition at SAP: the Hasso Plattner Founders’ Award. Starting this year, the Hasso Plattner Founders’ Award comes with a modified, more focused approach. It now consists of two categories: “Scaling Innovation” and “Emerging Ideas.” Both reflect a different type of breakthrough thinking and the various ways in which innovation drives SAP’s success. This year’s award theme is AI.
Following the presentation of the “Scaling Innovation” category finalists, we now turn to “Emerging Ideas,” which honors visionary concepts at an earlier stage—projects that explore new architectural directions, challenge established models, and open long-term strategic opportunities for SAP and its customers. The winners will be announced during the award ceremony on March 26, 2026.
SAP 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‑functional, policy‑aligned options before making mission‑critical decisions. SAP Cognitive Twin Enterprise (SAP CTE) addresses this gap by creating an AI‑powered digital brain built on a continuously updated model of the whole organization. It runs what‑if simulations and provides governed recommendations on SAP applications and data across finance, spend, supply chain, HR, and customer experience, with selective, low‑risk auto‑execution and human‑in‑the‑loop control for higher‑risk steps.
The business case is compelling. Organizations that combine digital twins with agentic AI at scale report double‑digit improvements in efficiency and cost, plus materially faster decision cycles. For a global industrial enterprise with approximately €40 billion in revenue, SAP CTE is modeled to systematically prevent margin leakage, excess working capital, and audit exposure, delivering an estimated €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.
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SAP CTE’s real differentiator is its enterprise‑wide scope. It consolidates existing SAP capabilities and builds on SAP Signavio solutions, SAP Business Data Cloud, and SAP Knowledge Graph to maintain a shared semantic model of how the whole business runs. This cross‑domain intelligence lets Joule and AI agents optimize complex trade‑offs—such as cost versus service level versus carbon footprint versus operational risk—across all functions, rather than pushing problems from one silo to another. At the same time, SAP CTE provides a safe innovation environment: enterprises can trial new pricing strategies, network configurations, and workforce models in a production‑grade twin before agents execute changes in live systems.
SAP CTE represents a strategic shift in how enterprises operate. It turns SAP’s 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, SAP 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.
“SAP CTE is more than an initiative: it’s our vision for a new era of connected intelligence. We’re bringing strategy, data, and execution into one continuous system of insight, so customers don’t just react to change—they anticipate what’s next and shape it. That’s how we win and grow together,” said Natalia Aksakova, Strategy & Portfolio at Global Finance and Administration.
Finalist fast facts
Submission Title: SAP 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 SAP at the forefront of cognitive enterprise architecture by shifting from reactive systems of record toward predictive, simulation-driven, AI-supported decision-making and execution.
SAP 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.
SAP 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 SAP Signavio portfolio‘s process knowledge, it enables users to achieve in minutes what previously required weeks of manual effort while keeping users in full control.
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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 SAP 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,” said Alex Cramer, product manager at SAP Signavio Next.
Finalist fast facts
Submission Title: SAP 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 SAP 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 Transport for New South Wales (TfNSW), 300 users report 400 to 1,000 asset faults monthly through SAP 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 SAP HANA Cloud vector engine, SAP 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—all 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. SAP Cloud Application Programming Model provides the secure foundation, SAP HANA geospatial content supports asset location intelligence, and AI models process text and images using SAP HANA Cloud vector engine for similarity matching.
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Results demonstrate substantial operational impact. AURA delivers 80% faster fault reporting, fewer data entry errors and misclassifications, and improved response times. For TfNSW, this translates to safer infrastructure, reduced operational costs, and a future-ready foundation for predictive maintenance. The customer response validated the approach: TfNSW 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 SAP 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 TfNSW-specific historical data to prevent bias, includes human review before submission, and explicitly handles uncertainty to avoid hallucinations. It ensures transparency and compliance with SAP’s responsible AI framework while empowering human decision-makers.
“We believe the future of AI is not replacing people, but elevating them,” said Ruth Peng, AI specialist from SAP HANA ANZ. “AURA equips every engineer in the field, from junior to expert, with the confidence to perform at their best.”
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 SAP 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.
