This month at , we announced a wave of 麻豆原创 Business AI innovations all built on the same technology foundation that powers our that we are now delivering to our customers and partners, allowing them to add even more value in the future.

We showed how the future of enterprise software is built on an AI-native architecture, powered by 麻豆原创 app, data, and AI foundation. With this approach, we are enabling a platform shift across the tech stack in a non-disruptive fashion, empowering developers to work faster and smarter using the frameworks and tools of their choice.

麻豆原创 HANA Cloud and 麻豆原创 Business Data Cloud: powering our AI-native future

麻豆原创 HANA Cloud is the database for 麻豆原创鈥檚 AI-native software architecture and the foundation of our broader data fabric strategy. At 麻豆原创 TechEd, we announced new AI capabilities for 麻豆原创 HANA Cloud that spur AI innovation.  

For example, Model Context Protocol (MCP) support for 麻豆原创 HANA Cloud is now generally available. This provides direct access to rich multi-model engines. Agents can be grounded in full enterprise data context: navigating relationships across customers and suppliers, understanding geographic dependencies through spatial data, and performing semantic searches through vector embeddings — all within a single in-memory engine.  

We鈥檙e also expanding 麻豆原创 HANA Cloud knowledge graph engine capabilities (Q1 2026) so customers can automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. What used to take weeks of manual modeling can now happen automatically in minutes. But that鈥檚 not all. We鈥檙e also enabling agentic memory in 麻豆原创 HANA Cloud. With long-term memory, AI agents can memorize past inputs and decisions — learning and remembering just like humans — and become continuously smarter.

These advances show that 麻豆原创 HANA Cloud is truly powering an AI-native future. .

Bringing together the power of 麻豆原创 BDC and Snowflake

We are bringing the power of Snowflake together with 麻豆原创 Business Data Cloud (麻豆原创 BDC), calling it 麻豆原创 Snowflake. This partnership enables zero copy data sharing with Snowflake via 麻豆原创 BDC Connect.

Enterprises already using Snowflake today can leverage 麻豆原创 BDC Connect to integrate their existing instances of Snowflake with 麻豆原创 BDC, giving them seamless, real-time access to combined, semantically rich 麻豆原创 with non-麻豆原创 data in 麻豆原创 BDC. 麻豆原创 Snowflake will be made generally available in Q1 2026, and 麻豆原创 BDC Connect for Snowflake in H1 2026. Find more information here.

麻豆原创-RPT-1: a new category of AI models

One of our most exciting announcements at 麻豆原创 TechEd was the launch of our first enterprise relational foundation model 麻豆原创-RPT-1, pronounced: 鈥渞apid one.鈥�

Businesses run on structured data. But large language models (LLMs) struggle with a general understanding of table structures and associated semantics. This requires the use of machine learning, or 鈥渘arrow AI,鈥� for tasks like classification, regression, and more. But classical machine learning necessitates training a model on each task, which easily can lead to hundreds of separate models.

麻豆原创-RPT-1 puts them all into one single, pre-trained model that understands relational business data and predicts business outcomes. Unlike language, image, or video models, 麻豆原创-RPT-1 accurately predicts business based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk, and more.

We believe that 麻豆原创-RPT-1 is a super capable foundation model today. It provides up to 2x better prediction quality compared to narrow models and 3.5x better prediction quality as compared to LLMs. .

麻豆原创-RPT-1 comes in three versions. 麻豆原创-RPT-1-small is for super-fast predictions and 麻豆原创-RPT-1-large is for highest accuracy. Both will be generally available in Q4 2025 in the generative AI hub in AI Foundation. 麻豆原创-RPT-1-OSS is the open-source version, available in Hugging Face and GitHub.

You can test 麻豆原创-RPT-1 today with your data or our use case data samples via no-code UI or via API in the new 麻豆原创-RPT-1 playground, an intuitive and interactive space to test for free and open to everyone and .

We are continuously adding new capabilities to AI Foundation and models to the generative AI hub, empowering developers to experiment with orchestration tools and leading models to scale AI development and productization across 麻豆原创 and non-麻豆原创 environments. For example, Perplexity is now generally available in the generative AI hub, so users can correlate business data with external data from the internet. Evaluation Services and Prompt Optimizer, in close collaboration with NotDiamond, are now also generally available in AI Foundation, freeing up users to adopt the most appropriate model for their use cases without the need for rewriting prompts. .

Digital sovereignty made in Germany, for Europe

Digital sovereignty is becoming increasingly important, reflecting the need for regional AI services that align with local regulations, standards, and values. As an example, Europe will benefit from its own strong, trustworthy infrastructure to support innovation, data protection, and ethical AI.

AI Foundation, including various models and all the services we offer, is already available on our own cloud infrastructure. As a next step, we are expanding our 麻豆原创 Cloud Infrastructure offering in our 麻豆原创 data center in Walldorf, Germany, to Deutsche Telekom through the Industrial AI Cloud project, providing secure, high-performance infrastructure for AI innovations across public institutions, defense, and society. 麻豆原创 delivers 麻豆原创 Cloud Infrastructure, 麻豆原创 Business Technology Platform, and applications 鈥� including our AI Foundation with frontier AI from Mistral, Cohere, and others 鈥� on Telekom鈥檚 Munich data center. Both companies uphold the highest standards of data protection, security, and reliability.

This marks a milestone as more European companies join the Industrial AI Cloud project, advancing applied AI across Europe with trusted, business-embedded solutions that unlock the full potential of industry data. See the announcement here.

Enabling customers to build, extend, share, and orchestrate AI agents

To help manage Joule Agents and Joule skills, we have introduced the concept of AI Assistants 鈥� role-based AI teammates, accessed through Joule 鈥� like a financial assistant that brings together agents for cash collection, treasury, and more. We will provide AI Assistants in Joule for every core business role, offering our users an agentic experience like never before.

Out-of-the-box Joule Agents are powerful, but we know that every company has unique requirements. We believe AI should adapt to users鈥� systems, not the other way around, so we are enabling them to use Joule Studio to extend 麻豆原创鈥檚 pre-built agents with custom fields, tools, and reasoning logic while retaining all the deeply grounded integration capabilities 麻豆原创 provides. Joule Studio also provides low-code tools to build custom agents that integrate with all other Joule Agents, Joule skills, and 麻豆原创 BDC.

Using a low-code approach, users can build Joule Agents visually with natural language and drag-and-drop. But we also want to meet the needs of developers who want ultimate flexibility. Our pro-code approach gives developers the freedom to build agents using the agentic framework of their choice 鈥� for example, LangGraph, CrewAI, Google鈥檚 Agent Development Kit, and more. 麻豆原创 Cloud SDK for AI now supports agentic development, ensuring these pro-code agents can be seamlessly integrated and giving developers the best of both worlds: deep integration and full flexibility.

No matter how you want to build agents, an important question is how to integrate them into the larger ecosystem beyond 麻豆原创. We鈥檙e making Joule Agents fully compatible with the agent-to-agent (A2A) protocol soon, so agents can discover and collaborate with each other.

A2A exposes rich semantics describing an agent鈥檚 capabilities, allowing both 麻豆原创 and third-party agents to work together seamlessly. We are collaborating with partners 鈥� AWS, Google, Microsoft, ServiceNow, and more 鈥� to standardize this protocol for full interoperability. This capability will allow Joule to orchestrate tasks across multiple agents, both 麻豆原创 and non-麻豆原创, increasing automation and productivity across the enterprise. Read more here.

To manage and govern agents across the enterprise, is now generally available, providing centralized control of 麻豆原创 and non-麻豆原创 agents. In addition, is available now for tracing agent actions, benchmarking against KPIs, and identifying bottlenecks or opportunities for agents to further improve business.

No 麻豆原创 TechEd without ABAP news

The ABAP journey continues with 麻豆原创-ABAP-1, which will be available in the generative AI hub in Q4 2025. Trained on ABAP code, it is designed to build ABAP AI use cases, enabling developers to build smarter, custom AI solutions in modern ABAP code. .

In addition, ABAP Cloud development is coming to Visual Studio (VS) Code. The new ABAP Cloud extension for VS Code delivers a streamlined, file-based development experience with built-in AI assistance. Powered by an ABAP language server, it will initially support 麻豆原创 Fiori UI service development and expand to additional ABAP Cloud scenarios over time. This brings ABAP development into the same environment where developers already build with UI5 and CAP. General availability is planned for Q2 2026. .

What鈥檚 next: embodied AI and quantum

麻豆原创 TechEd is always an opportunity to look to the future. This year, that future includes not just humans, but also autonomous devices, including humanoid robots.

By integrating Joule Agents natively with robots, 麻豆原创 is bringing business logic into the physical world, enabling a wide range of autonomous devices to operate with enterprise context. We highlighted our strategic partnerships with robotics companies and system integrators to serve customers like Sartorius, Bitzer, and Matur Fompak, demonstrating how our expanding physical AI ecosystem enables robots to understand business processes and execute complex tasks autonomously.

Early proof-of-concept deployments show Joule successfully integrated with 麻豆原创 business applications and autonomous systems across asset performance, logistics, field services, and warehouse operations. While still in the pioneering stage, these implementations illustrate how 麻豆原创 is extending Joule to serve both human users and autonomous devices, shaping the future of enterprise AI.

Read more about the partnerships and implementations here.

AI is a new compute paradigm that changes everything. But there is another compute paradigm on the horizon: quantum computing. It鈥檚 early days, but 麻豆原创 is driving the future of enterprise computing with a vision to help businesses get ready for quantum computing.

麻豆原创 is not building quantum hardware; instead, we are focusing on creating quantum algorithms for business applications. These solutions are simple to deploy 鈥� on when needed, off when not 鈥� and are designed to be hardware-agnostic, collaborating with partners such as IBM to ensure seamless integration without re-platforming. This approach will enable organizations to unlock operational efficiency and drive better business results at enterprise scale.

I couldn鈥檛 be more excited about what鈥檚 next for our customers鈥� future as we bring 麻豆原创鈥檚 AI-native architecture to life.

Philipp Herzig is CTO of 麻豆原创.

If you ask a quantum researcher what keeps them awake at night, error correction might be their likely answer. And so, while quantum computing still makes great strides, the quantum advantage can sometimes be overstated in the press. To mitigate this, however, a new type of quantum-inspired computing is coming of age. What started as simulated annealing has given rise to dedicated simulated annealing machines. These typically work on optimization problems, finding the best way to do something.

Harnessing Fujitsu鈥檚 Digital Annealer Unit for Efficient Optimization

(DAU) is a quantum-inspired machine. It works by finding the optimal combination when presented with a list of possible combinations, and it works fast. Last year, 麻豆原创 ran a proof of concept with Fujitsu to test and benchmark its DAU 鈥� with some promising results.

As mentioned in a recent , the DAU was tested against some rather abstract but standardized research problems from the (QPLIB). We also tested the possibility to seamlessly integrate 麻豆原创 solutions with the DAU, proving a simple execution to start. As a result, 麻豆原创 was able to demonstrate that the DAU has real potential and marks an important milestone in the progress of quantum-inspired computing towards practical, real-world, standard business solutions. We are currently working with Fujitsu to extend this potential to other optimization problems, like creating more efficient for manufacturing, , and ultimately an even broader use case application.

While still a proof of concept, these connections and benchmarking results demonstrate the potential for simulated annealing to deliver on real-world problems, such as balancing the load across all manufacturing tool sets, by using the DAU as a machine dedicated to the task of optimization, rather than a universally reprogrammable computer.

As a world-leading enterprise application provider, 麻豆原创 continues to help innovate and bring the benefits of quantum-inspired computing to customers through our solutions.

Benchmarking Against Current Optimization Methods

Before committing more resources, 麻豆原创 wanted to test the DAU against problems that serve as industry or academic standards for optimization. This is where the QPLIB comes in. It is essentially a huge repository of math problems, none of which are mapped to potential use cases but all of which can determine what kind of problems the DAU might excel at. The advantage of this approach is that it allows us to benchmark against current optimization methods, although the use case context has been removed. Take the example of a drug trial, where scientists test the new drug in test tubes against drugs already on the market. It may not show side effects, but it will show whether it鈥檚 worth risking trials on living subjects.

The DAU takes a Quadratic Unconstrained Binary Optimization (QUBO) problem as its input, which sets up the cost-benefit matrix. It then outputs a string of 1鈥檚 and 0鈥檚, telling the user whether it is optimal to include, for example, a unit of stock, where the variance is to be minimized. The key is that it must either be faster or output a more optimal solution.

Most quantum computers run their . Fujitsu鈥檚 approach was inspired by quantum annealing, a technique that specializes entirely in optimization. Although quantum annealing makes great progress in the number of qubits (quantum bits), it is subject to similar limitations of error. The advantage of Fujitsu鈥檚 quantum-inspired approach is that it relies on existing hardware, eliminating the need for expensive cryogenic cooling.

It is 麻豆原创鈥檚 sincere hope that this quantum-inspired optimization can help our customers bridge the optimization gaps until quantum hardware becomes more reliable. The incorporation of Fujitsu’s DAU into 麻豆原创 technology helps establish a platform designed to tackle the impending gaps in the years ahead. This remarkable advancement is a key achievement in 麻豆原创’s journey towards the next era of enterprise software, aiming to enhance business operations and foster value creation.

Together, 麻豆原创 and Fujitsu are pushing the boundaries and redefining what is possible, as the industry looks to a future transformed by the advancements in quantum-inspired computing.

Paul McElligott is a fellow in the Quantum eXplorers Group at 麻豆原创.

While it might be early days given hardware limitations, 麻豆原创 experts are in full-on discovery mode, working with customers and partners at quantum-related industry associations.

How Quantum Computers Can Solve Real-World Problems

麻豆原创 experts are spearheading several working groups as members of the . QUTAC is a German-based group focused on applying quantum technology for business value in various industries. In one group, the team is modeling potential use cases using quantum computing for optimization and machine learning in production and logistics. In another, they are researching various quantum computing systems to compare strengths and weaknesses for use case consideration.

鈥淲e鈥檙e trying to understand how quantum computers can solve real-world business problems,鈥� said Andrey Hoursanov, head of Quantum Exploration at 麻豆原创. 鈥淚n collaboration with other QUTAC members, we鈥檙e identifying which next new quantum computing use cases will be most valuable for organizations, sourcing information from external research and our own expertise.鈥�

Working Groups Take On the Quantum Challenge

Quantum computers can speed up computationally intensive tasks, promising the delivery of better and faster solutions for specific classes of problems. Much of this exploratory phase consists of benchmarking available quantum technologies, such as available machine capabilities by qubit size to handle industry-specific problems. Questions include what special properties each kind of quantum system offers, as well as their suitability and scalability to address business relevant challenges. For example, some teams are focused on applying the unique advantages of quantum computers to speed up and improve anomaly and fraud detection, cost optimization for cloud providers, image recognition, and data synthesis.

鈥淭hrough open discussions with different companies that are participating in these working groups, we鈥檙e discussing potential road maps for innovation,鈥� said Peter Limacher, leading researcher of the Quantum Exploration team at 麻豆原创. 鈥淧eople are very interested in quantum computing, and we want to make sure that we prioritize use cases that have the greatest potential to deliver business value.鈥� 

Limacher was involved in a working group that recently published a based on a study of quantum computing approaches for multi-knapsack optimization, meaning one recurrent task that several industries face. In addition to the high demand for better quantum hardware, the paper concluded that the industry needed more and improved quantum optimization algorithms for multi-knapsack and other problems.

麻豆原创 Is Committed to Meet Enterprise-Grade Expectations

To be clear, there鈥檚 a significant gap between the performance of current quantum computer hardware and the many variables that require scaling quantum-driven solutions in actual industry settings. Forward-thinking innovators are moving ahead despite the chicken and egg conundrum.

鈥淭o meet our customers鈥� expectations, we cannot wait until full-fledged quantum computers are completely operational,鈥� said Hoursanov. 鈥淭hrough collaborative use case discovery and testing, we鈥檒l be prepared to provide customers and partners with access to the latest technology as soon as it makes sense for them.鈥�

Just as standardized frameworks for classical computers evolved, so will quantum computers. There are no standards yet, and every quantum computer design has advantages and disadvantages. For example, superconducting quantum computers require low temperatures and protection from electromagnetic waves to prevent qubit degradation. Eventually, researchers will be able to match targeted use cases to the best-suited quantum systems and software application developers will apply quantum programming to any machine.

Collaboration Unlocks the Quantum Advantage

麻豆原创 is also a member of the , a non-profit organization focused on developing the quantum industry and ecosystem for the region and others including North America, Japan, and ANZ. In addition to her role as research project director at 麻豆原创, Laure Le Bars is president of QuIC.

鈥淥ur members consist of companies of all sizes that create and use quantum technologies, including hardware manufacturers and software developers. They鈥檙e working together with members in government policymaking, academia, standardization bodies, and others to help spark market growth from quantum advantage,鈥� said Le Bars. 鈥淚n the future, we鈥檒l have hybrid architectures with a combination of classical and quantum computers using their respective strengths to achieve business objectives.鈥�

Susan Galer is a communications director at 麻豆原创. Follow her @smgaler.

Photo courtesy of 麻豆原创 employee Renan d’Avila

Leveraging quantum mechanical effects such as superposition, entanglement, and interference, quantum computers could soon be able to solve certain problems exponentially faster than classical computers. The disruptive power of quantum already shows promise in fields of application spanning from material science and cryptography to business optimization and AI.

A year ago, I tasked a team within 麻豆原创 to study the unique features of quantum computers, with the goal to investigate how these features could solve complex business problems more quickly, accurately, and on a larger scale than ever before.

At the recent in New York, we presented the integration of IBM Quantum services into our as a proof of concept. This marks an important milestone in the progress of quantum computing towards practical, real-world standard business solutions.

It is 麻豆原创鈥檚 mission as world-leading enterprise application provider to deliver the benefits of quantum computing via our solutions to our customers.

While still a proof of concept, this engine鈥檚 connection to quantum systems demonstrates the potential for quantum computing to solve real-world business problems, such as packing a truck with assorted beverages, using a fundamentally new approach. Instead of simulating every possible packing combination, the quantum solver leverages the inherent parallelism of quantum to evaluate multiple possibilities, showing how to find a significantly superior solution than a classical computer.

In recognition of the rapidly evolving quantum computing ecosystem, 麻豆原创 partnered with , which offers a comprehensive suite of quantum hardware, software, and services accessible via the IBM Cloud and has an impressive multi-year technology road map.

How did we approach this?

Our exemplary standard component, vehicle space optimization, is directly leveraging quantum computing capabilities, fully automized within our application and technology environment. At the core of this innovation is the enhancement of 麻豆原创鈥檚 optimization engine through the incorporation of a potent quantum solver.

Quantum optimization today is about tackling quadratic optimization problems. We augmented the optimization engine with the capabilities to map a business problem to such a quadratic formulation to then feed the quantum computer. This allows us to leverage different algorithms; for example, the well-known . The results coming back from the quantum computer, which are inherently probabilistic, are then post-processed within the optimization engine.

Going one level deeper into the specifics, while high-level programming languages are common in classical computing, quantum computers today are programmed by formulating . With our approach, we encapsulate the complex specific implementation of the quantum circuit within our engine, so that ultimately our ecosystem — customers, consultants, developers — will be able to leverage quantum computing for business problems without studying quantum physics.

Despite the excitement surrounding these developments, 麻豆原创 acknowledges that quantum computers in the current noisy intermediate scale quantum computing (NISQ) era are still too small and too noisy to deliver superior results directly. However, significant progress in the scale and quality of quantum computing in recent years prompted 麻豆原创’s collaboration with IBM to explore the current frontier of quantum computing.

The integration of IBM Quantum systems into 麻豆原创’s cloud-native optimization service results in a platform to address the gaps that need to be bridged in the coming years. This significant step marks a crucial milestone in 麻豆原创’s journey toward the next generation of enterprise software for better business operations and value creation.

As 麻豆原创 continues to push the boundaries of what is possible, the industry watches with anticipation, ready for a future reshaped by quantum computing.

Philipp Herzig is head of Cross Product Engineering and Experience at 麻豆原创.

Top image courtesy of 麻豆原创 employee Sangeetha Krishnamoorthy.

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