International Space Station鈥檚 use of tabular AI and expanded partnerships demonstrate the art of the possible with 麻豆原创 AI

BERLIN 鈥� (NYSE: 麻豆原创) today announced a series of AI innovations at its annual 麻豆原创 TechEd conference that expands its portfolio for developers.

The enhancements to the Joule Studio and Joule Work solutions and 麻豆原创 Business AI Platform, including the latest tabular foundation model from 麻豆原创, offer new tools to build and extend AI agents, a richer data and business context for those agents to reason with, and strengthened governance controls to run them responsibly across the enterprise.

麻豆原创 Business AI Platform gives organizations one place to build, contextualize and govern AI across 麻豆原创 and third-party environments. With Joule Studio, business users and developers can use no-code and pro-code tools to build and extend agents, choosing from more than 70 AI models. Customers in the Joule Studio early adopter program also can design and run n8n workflows in Joule Studio, with broader availability to follow. The 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph solutions ground those agents in trusted business data and context, helping organizations select the right model for each use case and better manage AI value and consumption. Importantly, 麻豆原创 customers鈥� data remains their data. 麻豆原创 does not share customer data with anyone to train or improve large language models.

鈥溌槎乖� TechEd has always been where builders shape what comes next,鈥� said Philipp Herzig, CTO of 麻豆原创 SE. 鈥淭his year, we are showing how we are moving from AI learning your business to running it. Joule Work is where you put your intent and get work done, in one experience across the entire 麻豆原创 landscape and beyond. That is the shift we are delivering at scale. Everything underneath runs on 麻豆原创 Business AI Platform, delivering faster time to value through out-of-the-box business understanding, higher accuracy, enterprise-readiness that is secure, governed and compliant from the first deployment, and a unique context layer built on over 50 years of process and industry expertise, encoded into every agent action. This is AI built where real work happens.鈥�

Joule Work and Joule Studio Make the Autonomous Enterprise Real

New capabilities for Joule Work will help make AI agents more effective at handling complex business processes via open protocols including Agent2Agent (A2A) and Model Context Protocol (MCP). These innovations advance Joule Work to better understand context, make decisions and execute multistep business workflows across enterprise systems and business domains:

• New agentic thinking capabilities that plan and orchestrate the steps assistants and agents need to reliably complete complex, multistep business workflows across 麻豆原创 landscapes.
• New capabilities in 麻豆原创 Knowledge Graph will incorporate data from additional 麻豆原创 and non-麻豆原创 sources that are unique to each customer. Users can query and explore their customized data in natural language and get accurate answers grounded in their own information.
• Advancements in interoperability allow third-party agents to securely invoke Joule Agents through a governed agent gateway. This enforced access control enables agents from different providers to collaborate across business workflows without sacrificing governance or control.
• New 麻豆原创 Enterprise AI Gateway solution will extend governance to AI interactions, offering a centralized way to manage AI traffic across the enterprise that will consolidate authentication, policy enforcement, routing, monitoring, usage tracking and auditing.
• New Joule Assistant and 麻豆原创 AI Agent Hub expand governance capabilities, and a new AI Governance Assistant helps customers meet compliance requirements for the EU AI Act. These are designed to address critical barriers to scaling enterprise AI: keeping AI systems classified, compliant and operating within defined organizational guardrails and context.

麻豆原创 also announced that Joule Studio has new observability and administrative controls to give developers and administrators visibility into agent behavior, model usage and spend. Joule Studio is 麻豆原创鈥檚 AI-first development environment that is accessible through Joule Work for building and extending enterprise agents. Joule Studio will run a time-bound promotion for select customers, where deploying and running custom agents on the 麻豆原创-managed Joule Studio runtime will remain free through December 2026. The design-time tooling for building and testing agents and n8n workflows with AI-assisted development will remain free under fair-use limits.

麻豆原创 Business Data Cloud as the Grounding Engine of the Autonomous Enterprise

麻豆原创 Business Data Cloud Ontology Manager in 麻豆原创 Business Data Cloud (麻豆原创 BDC) will provide an AI-native authoring experience for building ontologies across 麻豆原创 and third-party data. By helping AI understand how data from different teams, systems and applications relate to one another, AI agents can more accurately reason and make decisions across the enterprise. The solutions complement 麻豆原创 Knowledge Graph, which provides rich business context for 麻豆原创 data.

Additionally, 麻豆原创 is expanding its lakehouse capabilities with Dremio, adding native support for Apache Iceberg and Apache Polaris plus a high-performance, federated query engine that will enable unified data access without moving or copying the data. 麻豆原创 expands its partnership with Databricks, bringing the full scope and latest innovations of data engineering and enterprise AI capabilities into 麻豆原创 Business Data Cloud as 麻豆原创 Databricks. Core innovations such as Lakeflow, Agent Bricks, Unity Gateway, and Lakebase will help customers to simplify data management and provide a strong data foundation for their AI journey. With a deeper integration to 麻豆原创 Business Data Cloud, a new 麻豆原创 Application Data Foundation, as well as an integration between Joule and Genie, 麻豆原创 and Databricks are helping customers establish an enterprise-wide lakehouse as the foundation for the Autonomous Enterprise and Joule Work.

The 麻豆原创 Reltio solution, recently announced at 麻豆原创 Connect, will bring multidomain master data management to 麻豆原创 BDC. It unifies and enriches customer, supplier and product records across 麻豆原创 and non-麻豆原创 systems into trusted, reusable data products that can power applications and AI agents.

Tabular Foundation Model Trusted for Business, Validated in Space

麻豆原创 announced its latest tabular AI model, 麻豆原创-RPT-2.0, which will expand beyond classification and regression to support anomaly and outlier detection, and richer AI-powered analysis through embeddings. These native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data. In addition, a new 麻豆原创-RPT-TS model will help customers predict values across time-indexed business metrics for key forecasting use cases using the 麻豆原创 Integrated Business Planning solution without requiring custom machine learning.

In partnership with HPE, 麻豆原创 also announced the deployment of 麻豆原创-RPT on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS), one of the most significant edge-compute validations of any enterprise AI model to date. 麻豆原创-RPT is planned to run directly onboard the ISS to support anomaly triage, predict issues before failures escalate, reconstruct missing sensor values from historical data, and generate explainable mission-health reports for astronauts and ground teams 鈥� all without requiring data to be sent back to Earth for analysis.

鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains also can work in space,鈥� said Herzig. 鈥溌槎乖粹�檚 deep-rooted, proven expertise has led to the International Space Station鈥檚 use of our tabular foundation model to predict potential mission-critical failures in orbit. With 麻豆原创-RPT and TabPFN from Prior Labs, we bring the unique capabilities of tabular AI into the enterprise and across every frontier.鈥�

Out-of-the-Box Assistants and Agents Define Our Advantage

For five decades, 麻豆原创 has built the systems of record that run your business. We will deliver, out of the box, a suite of assistants and agents across five horizontal domains all running on 麻豆原创 Business AI Platform 鈥� with industry specialization 鈥� that provides a baseline for systems of intelligence and systems of action.

Ecosystem Partners Help Expand Platform Capabilities

The global 麻豆原创 partner ecosystem brings new capabilities to the ongoing operation and extension of solutions on 麻豆原创 Business AI Platform, helping customers advance toward the Autonomous Enterprise. At 麻豆原创 TechEd, the following partner announcements were highlighted:    

A collaboration with Intel closes the loop between enterprise workflows and autonomous robots, bridging informational technology and operational technology through physical AI.

In addition, new 麻豆原创 solution extensions with , and extend autonomous capabilities for customers, helping them run their core processes end to end, without handoffs between systems.

Beyond technology, 麻豆原创 is empowering its strong developer community with new enablement and engagement with tens of thousands joining for leading up to 麻豆原创 TechEd, new reference architectures, tutorials and hands-on opportunities attendees can try onsite at the event and at home.

To learn more about today鈥檚 announcements visit our .

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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 .

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Space is unforgiving. Vacuum, radiation, orbital debris, and solar storms can produce mission-critical crises at any moment. Adding to the complexity, onboard sensors and mission system telemetry produce too much time-sensitive data to be routed back to Earth for analysis. Latency has to be reduced to ensure the safety of the spacecraft. 

Enter 麻豆原创’s tabular AI foundation model, , which makes predictions directly from structured data鈥攅ven with some data missing鈥攁nd without requiring the usual model training or fine-tuning. Running directly onboard the ISS on HPE Spaceborne Computer-2, it supports anomaly triage in real-time telemetry data, predicts issues before failures escalate, reconstructs missing sensor values based on historical data, and generates explainable mission-health reports for astronauts and ground teams.

“This is proof that tabular AI on 麻豆原创 works at the edge of possibility,” said Mayank Shrivastava, vice president of Generative AI and Foundation Models at 麻豆原创 SE. “Space is the ultimate stress test for AI. If a foundation model can predict mission-critical failures in orbit, under radiation, energy, and connectivity constraints, imagine what it can do for a global enterprise. This is the same model that will predict supplier risk, cash flow anomalies, and equipment failures across your enterprise. What works in orbit works in your supply chain.” 

Imagine this: in orbit, data arrives in fragments, connectivity comes and goes, and you cannot send a technician when something starts to fail. You have to anticipate problems with a variety of signals. Enterprises face the same challenge on the ground: a supplier whose deliveries are quietly slipping, a payment pattern that鈥檚 drifting, a compressor that鈥檚 running hot. Predictive models that are able to spot early warning signs in a satellite are also built to find those same signals in your business data, so you can act before small problems become critical ones.

How 麻豆原创 strengthens business functions with models for structured data 

麻豆原创-RPT is part of 麻豆原创’s portfolio of tabular foundation models, or TFMs, that includes the model by Prior Labs, and , which orchestrates, manages, and governs TFMs, predictions, and workflows. Large language models (LLMs) excel at language and reasoning, but TFMs are built for the structured tabular data that underpin real-world operations, such as the sensor-driven data used in this project.  

麻豆原创-RPT is about 50,000 times more energy-efficient than state-of-the-art LLMs. This is especially important in space, where every watt is constrained. 麻豆原创-RPT is also highly adaptable. It requires no pre-training as conditions evolve. 

Outcomes that will be delivered onboard include real-time predictions for computer and mission health status, an overall mission risk score, an assessment of whether manual intervention is needed, human-readable explanations of AI-driven findings, and ad hoc reports for crew and ground teams. 

鈥淗PE Spaceborne Computer-2 continually demonstrates the advantages of having a high-performance compute solution aboard the ISS, supporting advanced technologies and on-going research in space鈥� said Scott Wiest, Vice President and Global Sales Chief Technologist, HPE Fellow. 鈥淒eploying 麻豆原创鈥檚 tabular AI on HPE Spaceborne Computer-2 shows what鈥檚 possible with real-time detection and prediction using AI in the ultimate edge environment.鈥� 

From orbit to enterprise 

The deployment of 麻豆原创-RPT on HPE鈥檚 Spaceborne Computer-2 on the ISS, by early 2027, will show how it strengthens business functions on earth. The constraints of space, including extreme energy limits, intermittent connectivity, radiation interference, and zero tolerance for failure are relevant to challenging use cases on earth. These include disaster relief operations, agriculture in remote areas, distributed manufacturing, and spread-out supply chains.  

Every insight aboard the ISS accelerates 麻豆原创’s road map for enterprise edge AI and key use cases that leverage structured data for the most important analytical and operational processes, even in the most challenging settings.  

Looking ahead, the mission lays the groundwork to support space-based data centers, deep space exploration, and other collaborations on space missions. Our long-term vision for space-based compute infrastructure is anchored in proven, efficient, and accurate AI. 

Explore how to deliver tabular AI on 麻豆原创, learn more about the specialized capabilities behind it, and try the 麻豆原创-RPT AI model with your own data on the . 

Brenda Bown is chief marketing officer for 麻豆原创 Business AI Platform and Joule at 麻豆原创.

• How can this gap be closed? The answer lies in helping companies detect risks, defaults, losses, and liquidity pressures sooner鈥攁nd manage them more effectively.

For Germany and Europe, it is not an abstract challenge, but the key to future prosperity. Our economic strength lies in industry: in mechanical engineering, in the automotive supply chain, in complex manufacturing and logistics processes. According to German Economic Institute estimates, AI can generate around 鈧�440 billion of additional gross value in Germany by 2034.

Good, but not in every case

To get the economy moving again, we need a different kind of AI. Because it is not enough to process and generate language; AI must also be able to deal with data that companies work with the most. And this is where common large language models (LLMs) reach their limits. Structured data鈥攆or example, numbers in tables鈥攈elp companies understand what is happening right now and what may come next.

This requires AI models that understand the relationships among rows, columns, entities, and numbers. This is exactly what current research results show: from the year 2026 prove that even high-performance LLMs perform significantly weaker in table reasoning tasks鈥攆or example, in the ability to draw conclusions from tables and to recognize relationships鈥攖han in language tasks.

When data anticipates what’s next

Tables follow their own logic. Values do not stand next to each other in isolation, but in relationships between columns, rows, timeframes, and business transactions. Accordingly, tabular AI is designed to recognize patterns across columns as well as in the associated tables that make up a company system. The real added value lies in this networked context. Looking at a customer ID in isolation is one thing. Seeing which orders this customer places and predicting warehouse requirements from these data is a completely different thing.

A practical example: a manufacturer wants to identify early which customers are most likely to pay late. For this, a single piece of information is not enough. Among other things, relevant factors include payment history, order history, delivery data, and external industry information. Only the interaction of these data makes reliable predictions possible. That’s precisely what tabular AI models are built for.

What this means for AI investments

Classic machine learning methods are still used in many companies today for predictions based on structured data. They often deliver good results, but typically have to be developed, trained, and operated separately for each individual application: one model for demand forecasting, another for payment default risks or for prioritizing sales contacts, each with data preparation, training, monitoring, and ongoing maintenance.

Tabular foundation models change this equation. They do not have to be redeveloped for every business question but are universally applicable. This creates an immense scaling effect. In the future, instead of hundreds of specialized models, a single one鈥攕uch as 麻豆原创-RPT-1.6, which we have just made available to customers鈥攚ill suffice. With this, companies can build on historic examples and existing patterns from their business data in order to identify new risks and opportunities at an early stage.

For companies, this means a concrete shift: AI predictions will no longer have to be developed as complex individual projects in the future. In other words, the path from a specific question to the universal use of a model for any questions becomes shorter and cheaper.

In addition, since tabular foundation models are not pre-trained on individual use cases, but on large amounts of data, they can also recognize patterns across different use cases that specialized individual models, especially in limited data layers, often overlook. Therefore, in addition to the scaling effect, there is also a gain in knowledge, which previously did not exist in the analytical processing of tabular data.

New category for enterprise AI

At 麻豆原创, we are working on the next level of enterprise AI. With the acquisition of Prior Labs in July, we are further accelerating this path: combining Prior Labs鈥� basic research with the corporate reality of our customers, and the experience of 麻豆原创 in the application of AI in business-critical business processes.

With TabPFN-3, Prior Labs has developed one of the most powerful open-source models for tabular predictions. Together with the tabular foundation models of the 麻豆原创-RPT family and 麻豆原创 Knowledge Graph, this creates an infrastructure that securely generates structured predictions directly from the company-specific data with the individual business context.

In general, now we are seeing a trend toward various types of foundation models. In addition to tabular foundation models, models that have been trained on the basis of time series鈥攆or example, a large number of measurements鈥攁re being created. You can evaluate the measurements, recognize patterns, and thus also predict numerical values over time.

Conclusion

The first wave of generative AI is making employees more productive. The next one can make companies more forward-looking. The decisive factor here is not whether companies use LLMs or tabular foundation models. It is crucial to combine the two where they provide the greatest benefit: LLMs for interaction with language and knowledge, tabular models for forecasts based on structured business data.

Done right, AI becomes more than a productivity tool. It becomes a driver of better business decisions鈥攁nd a foundation for a stronger economy.

To learn more, read (in German).

This model marks the next leap in tabular AI, giving organizations access to unmatched tabular AI predictions on structured business data without model training or tuning. For 麻豆原创 customers, TabPFN-3.5 Plus is now available in 麻豆原创 AI Core.

Critical enterprise business decisions,聽such as cash flow forecasting, payment delays or聽supplier risk scoring, run on structured, tabular data. While LLMs聽excel at language and knowledge,鈥痶abular foundation models聽(TFMs)聽are purpose-built for聽structured data and聽can accurately predict business outcomes based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk and more.聽

With TabPFN-3.5, 麻豆原创 customers can work with data as it exists in their systems. Missing values, mixed data types and inconsistent fields are handled by the model, returning聽accurate聽predictions without preprocessing.聽This聽newest model from Prior Labs鈥� family of tabular AI models聽uses in-context learning to make predictions from raw tabular data, handling columns with thousands of distinct values, such as product codes or customer identifiers, and mixed data types natively, without the trial-and-error configuration that traditional models require.聽TabPFN-3.5 Plus聽is the most聽accurate聽and scalable tabular foundation model available today, based聽on聽TabArena聽and聽BeyondArena, two聽external聽benchmarks designed to evaluate predictions over real-world data.聽

鈥淩eal-world data is rarely perfect.聽Datasets often聽contain聽complex relationships and varying conditions that make traditional machine learning difficult. TabPFN-3.5 is聽specifically聽built for these challenges, providing industry-leading accuracy and scalability for tabular data with less manual effort鈥攎aking it the聽most effective聽tabular AI model in the industry聽to date,鈥澛爏aid Philipp Herzig, Chief Technology Officer, 麻豆原创聽SE.聽鈥淲ith TabPFN-3.5聽and the 麻豆原创-RPT model family, customers get聽accurate聽predictions from labeled business data in minutes, with no training聽required.聽For us,聽tabular AI is not a supporting feature of the Autonomous Enterprise, it is the foundation.鈥澛�

For more detailed information and to learn how to get started on 麻豆原创 AI Core, see the , and view the .

麻豆原创 completed its acquisition of Prior Labs in July 2026, bringing one of the world鈥檚 leading TFM research teams into the 麻豆原创 family. Prior Labs will continue to operate as an independent entity, with 麻豆原创 having previously committed to invest more than 鈧�1 billion to scale it into a globally leading frontier AI lab for the structured data that underpins the world鈥檚 businesses.

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Media Contact:
Alex Vaught, +1 (206) 678-5712, alex.vaught@sap.com, PST 
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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.鈥� 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 2025 Annual Report on Form 20-F. 
漏 2026 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.

The top team鈥檚 rowers focused entirely on maximizing individual power. If the boat slowed, they rowed harder in isolation, disrupting the oars鈥� synchronized rhythm and creating water drag. Meanwhile, the junior rowers knew they were individually weaker, so they rowed in harmony.

Enterprises have faced countless variations of this problem: implementing systems that maximize productivity at the individual or team level but actively hinder the wider enterprise. Many organizations are experiencing something similar with AI today.

AI and sub-optimization

Sub-optimization is a systemic failure that occurs when the performance of a specific part of a system is maximized, inadvertently hampering the performance of the entire system. There are three intertwined themes: intensity, context, and prediction, which, taken together, explain how AI can sub-optimize an organization by making individuals and local systems stronger while straining the broader organization.

Research reinforces this disconnect between individual or even company-wide AI adoption and the value it delivers. shows near-universal enterprise AI adoption: 89% of respondents report regular AI use in at least one business function, but only 37% report an earnings before interest and taxes (EBIT) impact from AI at the enterprise level. Even worse: only six percent of companies can be categorized as high performers that already capture significant organization-wide value from AI.

What makes it so hard to move from AI adoption to measurable value capture? I believe there are three themes that influence a company鈥檚 ability to benefit its entire organization.

Intensity

AI tools often don鈥檛 reduce work; they intensify it. A found that employees who heavily use AI worked faster, took on a broader range of tasks, and worked longer hours, often without being asked. So, what appears to be higher productivity in the short run is actually silent workload creep and mounting pressure as employees manage new AI workflows and do more with less. that the most mentally taxing form of AI engagement was oversight; AI tools that require direct monitoring increased feelings of being overwhelmed by the volume of information at work.

It is easy to see why AI can feel intense: tasks that once required days can now be prompted into existence almost immediately. People start more things; they do more analysis and write more memos. However, like an eight-lane highway that suddenly narrows to a single-lane toll booth, individuals must still consume all this output. This bottleneck only compounds at the organizational level, as all employees produce more than ever, leaving both individuals and the organization as a whole struggling to keep up. Creation has scaled. Absorption has not.

The solution isn鈥檛 necessarily to use less AI, but to change where and how AI shows up. AI should understand user intent and surface the insights needed to answer the question, rather than generating static assets or requiring you to switch between different apps and systems.

If your question creates more things, it鈥檚 not helping absorption. 麻豆原创鈥檚 answer is , a central workspace across 麻豆原创 and non-麻豆原创 systems that uses AI agents to handle tasks.

Ask, 鈥淲hich stores run out of 65鈥慽nch TVs in the next 72 hours, and where is stock I can move?鈥� It will pull data across systems and orchestrate agents to act on the user鈥檚 behalf. In this case, 麻豆原创鈥檚 answer is autonomous action combined with a highly individual user experience for that specific situation, not more assets to be absorbed. If employees can avoid juggling systems and consuming assets, they can spend more time exercising judgment on actions that matter. This is how AI can alleviate intensity.

Context

Most AI systems understand the world, but not the enterprise in which they operate. There is a difference between a system of record鈥攖ransactions, master data, process logic鈥攁nd tacit knowledge鈥攅mails, chats, unwritten rules. And enterprises run on both. If AI only sees the system of record, its answers might be technically correct but contextually wrong because they don鈥檛 reflect the organization’s lived practice.

Even the most ostensibly basic questions require company context. Asking 鈥淲hich suppliers can I source coconuts from?鈥� requires knowledge of an organization鈥檚 process landscape across procurement, supply chain, compliance, finance, and other domains. This type of enterprise knowledge is usually scattered across process models, policies, chats, spreadsheets, and applications, so it鈥檚 tough to maintain. And even if they find it, agents cannot turn it into action without procedural knowledge of the involved people鈥攖he unwritten rules, decisions, and steps鈥攖hat make a process executable.

preview continuously captures institutional knowledge and makes it usable for both people and agents. It turns written inputs, chat inputs, process knowledge, policy guidance, and application logic into reusable building blocks that AI agents can consume. Blocks are captured once, governed centrally, and reused across the company. So when someone asks Joule Work about coconuts, the answer is driven by the company鈥檚 memory and reflects actual rules the process owners agreed upon鈥攆or example: “Only source from Brazil; others require formal exception approval.”

麻豆原创 Company Memory is not a one鈥憈ime implementation; it鈥檚 continuous. In this way, company knowledge behaves like infrastructure, ensuring agents act contextually, not just correctly, as policies and teams change.

Prediction

Business decisions are fundamentally prediction problems that rely on structured data. Most organizations use LLMs, which are great at unstructured data like text but for architectural reasons not so great at working with and generating the structured numerical data that underpins good predictions. Delay prediction, forecasting, anomaly detection, stock optimization, and credit risk are everyday operating questions that depend on structured, tabular data and forward-looking judgment.

Asking LLMs for reliable forecasts on enterprise tables is simply the wrong tool for the job. At the same time, traditional custom machine learning approaches are too slow for many real-time questions: after extracting data, sending it to specialists, and waiting weeks, the question often has changed by the time the answer comes back. This combination means predictive capabilities are either restricted to specialists or rendered inaccurate by generic LLMs; in either case, the organization鈥檚 decision-making is weakened.

Reliable forecasting and risk assessment should be a system property, not an individual hack. 麻豆原创-RPT-1.5 and TabPFN 3 are models that excel with tabular data and will integrate with Joule Work and 麻豆原创 Business Data Cloud for forward-looking questions directly on live tables.

麻豆原创-RPT-1.5 for 麻豆原创 data and TabPFN 3 for any tabular data are specialized prediction engines for structured data. They enable decision-makers working in the core systems to ask, 鈥淪hould I reroute volume? What鈥檚 the probability of on鈥憈ime delivery? What鈥檚 the cost delta across scenarios?鈥� and get answers grounded in real enterprise data.

Availability across the organization eliminates specialist bottlenecks and better equips the enterprise to handle uncertainty through prediction. This enables informed top-level decisions that really move the needle for a company.

AI for the benefit of the whole organization

AI has already proven it can make people more capable, but that does not automatically help the wider organization. AI shouldn鈥檛 be about optimizing isolated tasks; it should be about reshaping how work, knowledge, and decisions flow through the company.

Joule Work, 麻豆原创 Company Memory, and 麻豆原创-RPT-1.5/TabPFN 3 are great examples of how 麻豆原创 designs system-level capabilities. They offer a unified engagement layer, a living institutional memory, and a prediction engine for structured business data that elevate AI from individual-level hacks into a collective benefit for the enterprise.

This is AI that bridges the individual-to-institutional value gap, moving from simply getting AI into the company to generating value throughout the company.

Florian Kunzke is global director of AI Strategy at 麻豆原创.