麻豆原创 has announced a series of AI innovations at its annual 麻豆原创 TechEd conference, expanding its portfolio for developers. The enhancements to Joule Studio and Joule Work, as well as the 麻豆原创 Business AI Platform 鈥� including 麻豆原创’s latest tabular foundation model 鈥� offer new tools to build and extend AI agents.

麻豆原创 said its strategy is to advance the development and adoption of agentic AI by providing richer business and data context for agents, and governance controls to manage their use across organisations.

Philipp Herzig, CTO of 麻豆原创 SE, said the 麻豆原创 Business AI Platform, along with 麻豆原创 Knowledge Graph, 麻豆原创 Business Data Cloud, Joule Studio, Joule Work and agentic orchestration and integration capabilities, is now generally available.

鈥淭his entire platform is now generally available with all its capabilities, starting at the top with Joule Work, available on every device, every computer, in every browser. 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.鈥�

Asked how 麻豆原创 connects with external AI agents entering the market and supports customers already using agents from other vendors, Herzig said the company follows an open strategy.

鈥淐ustomers say that if 麻豆原创 provides an agent in the core domains where they have 麻豆原创, there鈥檚 no need to build and own one. There are tons of agents out there, and we have made sure, via our agent gateway, that there is full interoperability. Our AI agent hub allows you to discover the agents, 麻豆原创 agents and non-麻豆原创 agents, and you can document them and make them compliant.鈥�

Herzig also addressed vendor lock-in and the challenges organisations face when using AI models from different vendors, discussing 麻豆原创鈥檚 approach to letting customers select models for specific use cases.

He said that tailoring a use case for optimisation inevitably brings some lock-in, but that model switchability remains viable for organisations keeping their AI use cases fairly generic.

AI to serve autonomous enterprise operations

Florian Kunzke, global director of AI strategy at 麻豆原创, echoed Herzig鈥檚 view that the company is moving from AI that learns how a business operates to AI that runs it, with the objective of mobilising AI to serve autonomous enterprise operations.

He said 麻豆原创鈥檚 AI strategy continues to evolve, particularly from a user interface and user experience point of view, with agents embedded in its technology suite.

Kunzke said Joule Work and Joule Studio, alongside 麻豆原创 Knowledge Graph 鈥� which provides a structured semantic foundation for Joule 鈥� are central to 麻豆原创鈥檚 AI strategy. The company aims to help organisations become autonomous enterprises while addressing challenges such as the impact of geopolitical tensions on supply chains, the need to improve productivity through AI investment, and the requirement to generate ROI.

He cited , released in August 2026. According to the research, 80% of respondents said AI had improved individual productivity, while 50% said it helped them make better decisions.

However, he stressed the need for perspective on how AI is being adopted and used across organisations. 鈥淚t is important to distinguish between what happens at organisational level and what happens at individual level. There is a gap, and that is what 麻豆原创 is looking to address,鈥� said Kunzke.

He also acknowledged that shadow AI, rogue AI, cyber security threats and the maturity of digital infrastructure continue to affect AI adoption. However, he added that 麻豆原创 is confident its technology empowers customers to select the right AI for a job, as well as the necessary level of control.

鈥淵ou can improve what you are able to observe and measure. With agentic solutions and processes, and the adoption of these processes, the question is what to aim for in terms of governance, what tools will the organisation need. It makes sense to have tools be as close as possible to the applications being used.鈥�

Independent AI expert Johan Steyn, founder of , said the shift to agentic AI marks an inflexion point. “AI strategy stops being about which tools you buy and becomes about how you orchestrate and govern agents,” he said. “Capability is becoming abundant; governance is the scarce advantage.”

Latest tabular AI model

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.

According to 麻豆原创, these native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data.

In partnership with HPE, 麻豆原创 also announced plans to deploy 麻豆原创-RPT-2.0 on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS) in 2027.

The model is scheduled to run directly on the ISS to support anomaly triage, predict potential problems before failures escalate, reconstruct missing sensor values using historical data, and generate mission-health reports for astronauts and ground teams.

Herzig added: 鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains can also work in space. 麻豆原创鈥檚 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�

This article first appeared on .

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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 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 Devtoberfest 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.

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麻豆原创 was founded in 1972 by five former IBM employees. In a publicity shot from the time, they鈥檙e posed around a table in a nondescript office, all holding pens and hunched over a large blueprint. The hair is bouffant, the suits dark, and no one is smiling. These are clearly men who take their jobs very seriously.

As the story goes, Xerox wanted its systems migrated to IBM, and the latter firm put five engineers on the project, who all worked in Mannheim, Germany. The project ground to a halt for reasons unclear, but the five had seen the gap in the market, and left to form their company, which they called Systemanalyse und Programmentwicklung, or System Analysis and Programme Development. It has been a long haul since the 鈥�70s, with the company constantly tinkering with its solutions while persuading many of the world鈥檚 largest firms that they can run their businesses better with its suite of solutions.

At the core of this suite is . This is an in-memory database, and聽data聽is stored in RAM instead of disk. These databases can handle spikes in traffic, such as at telcos or聽banks, and allow for real-time analytics. Customers can have the platform installed in their own server room, or in the public or private cloud, or both. And with each passing year, more features are added, such as GenAI assistant ; its sparkly icon now appears on seemingly every page of the platform. Joule, on which 麻豆原创 partnered with , can run queries on all kinds of data, produce forecasts, book meetings, and make graphs and charts on the fly.

There was still an impression in the market that 麻豆原创 was extremely expensive, very complicated, and not very pretty.

Garth Ridgway, NTT

All this work has borne fruit, and in March 2025, the company overtook Danish drugmaker Novo Nordisk to become the EU鈥檚 most valuable company, with a market cap of 鈧�313bn. It reported strong results for its third quarter in October 2025, seeing revenue rise by 7% to 鈧�9.08bn. Cloud revenue saw growth of 22%. But there are challenges ahead, not least of which is convincing customers that it鈥檚 now past the time to migrate their workloads from the old system called ECC, which is usually run on-prem, to S4. The company would like nothing better than for all its customers to move to S4, but many are proving to be recalcitrant. Support for ECC, introduced in 2015, is meant to come to an end in 2027, but customers can buy extended support until 2030, which will cost them an extra 2% in addition to their annual maintenance costs. Many companies contend that the current systems seem to be working just fine, and many are also cognisant, and terrified, of the upheaval the聽migration聽is going to cause. To this, the company dangles the prospect of new features with S4, along with the constant refrain that support for the old systems is, at some point, going to grind to a halt.

An estimated 40% of 麻豆原创 customers in North America are yet to start migrating, according to a survey run by the Americas鈥� 麻豆原创 User Group. About 60% of the 173 members are already live on S4, or are in the process of moving over, according to the research in November. Gartner said in March 2025 that most ECC customers had still not bought S4 licences. It said that in Q4 in 2024, 29% of 35 000 ECC customers had bought or subscribed to S4 licences.

Christian Hestermann, Gartner senior director and analyst for business applications, believes this is likely to remain the status quo. Speaking to聽The Register, he said that since September 2023, the company鈥檚 messaging had been about AI and Joule. 鈥淭he message now is, ‘You have to have AI, or you’ll not be able to survive’. So far, that did not have a major influence. So none of those big events or big announcements has made a massive change up to now; the [migration] progress has remained fairly steady.”

No such third-party survey has been run in South Africa, and the company doesn鈥檛 make public either its customer numbers or how many have migrated.

I asked , CTO, 麻豆原创; , MD of 麻豆原创 South Africa; and , senior director, 麻豆原创, at NTT, for their views on how this migration conversation is progressing, and they spoke as one voice. Pillay says 麻豆原创 had 鈥渕ade a clear line in the sand鈥� with the date past which it won鈥檛 continue with maintenance. 鈥淐ustomers are aware of that and are making their plans to transform.鈥�

Pillay was appointed to the top job in August 2025, and says she鈥檚 going to ensure the local operation is well equipped to have these conversations with its customers, as well as partners.

Why haven鈥檛 they moved yet?

It鈥檚 a matter of context, she says. 鈥淲e have customers who have been with us for 20, 30 years, and that鈥檚 a godsend. But they bring a lot of legacy with them, and not just technical legacy. They have unarchived environments, non-optimised processes, or a lot of custom-built systems. They may have implemented ERP back in the day, and, in most cases, it was a project with a long duration, with a lot of blood and sweat going into it. Most companies understand they need to take that technology transformation journey, but they鈥檙e trying to balance the timing and effort required to bring a legacy system across [to S/4HANA]. There鈥檚 hesitation, because in the back of their minds, they remember that when they did it [last], it was really very difficult.鈥�

Financial services is one vertical where customers have realised the imperative of the move, she says. The move was a 鈥渘o-brainer鈥� for Pillay, and offered data analysis, AI, agentic AI, and GenAI capabilities. She says if a customer isn鈥檛 speaking to 麻豆原创 about the move, it may well be too late to get a system installed before the cut-off date.

鈥淲e鈥檙e prioritising helping those who are speaking to us to make that decision. We have to work with every customer individually to understand how they can do that transformation. There are different routes, based on capacity, budget and risk appetite. If you delay a decision, it鈥檚 going to be too late.鈥�

The slow movers are also going to bring pressure to bear on system integrator partners, as Pillay envisages them being at capacity, or overcapacity, doing 麻豆原创 implementations as the deadline draws ever closer.

鈥淭hen you鈥檒l pay for skills, either locally, or you鈥檒l have to look abroad. There鈥檚 going to be a material impact if a business takes this decision later on, and that鈥檚 what we鈥檙e trying to discuss with our customers and help them plan properly, so they don鈥檛 get into a position where they say, 鈥榃ow, we鈥檝e left this too late, and now this is a monster鈥�.鈥�

麻豆原创 NTT鈥檚 Ridgway says he鈥檚 spent the better part of three decades implementing 麻豆原创 at customers, during which time the ERP landscape has changed dramatically. In the past, it was a two-horse race, the other being Oracle, and both companies served the large enterprise almost exclusively, he says. Large implementations took a lot longer than they do nowadays, two to four years, on average. He says 麻豆原创 now comes more pre-configured, or more off-the-shelf than in the past. He and his team will do a quick discovery at a customer, and will then do what he calls a 鈥渧anilla鈥� implementation to get the system up and running. Any innovation can come later, post the go-live date.

Another change in the market is that 麻豆原创 is now competing for business with companies such as Microsoft and Sage.聽Ridgway says he鈥檚 seen a shift in the attitude and sales pitch from 麻豆原创.聽鈥淚n South Africa, there was a time when 麻豆原创 owned 60%, 65% of the large enterprise ERP market, and Oracle was lagging behind. Every single large enterprise in South Africa has some form of ERP. There was still an impression in the market that 麻豆原创 was extremely expensive, very complicated, and not very pretty.鈥�

As for looks, he says the new interface is more user-friendly, and 鈥渕uch nicer to play with鈥�. The company has also realised that there aren鈥檛 any R100mn projects out there anymore, which has meant its gone looking for customers among medium-sized businesses. 鈥淭hese aren鈥檛 small businesses, but they鈥檙e just not the $1bn organisations. I don鈥檛 think 麻豆原创 wants to play in the small space, because that鈥檚 where Sage and QuickBooks are playing. But there鈥檚 a massive market in the mid-tier, which wasn鈥檛 untapped, but which 麻豆原创 wasn鈥檛 focused on,鈥� he says.

Customers have also become 鈥渟marter than they used to be鈥�. 鈥淚n the old days, we would go and sell 麻豆原创 to them, explaining what it did. Today, the CIO knows everything about 麻豆原创 and what it can do for them. It鈥檚 less about consulting around the roadmap, and more about, 鈥楾his is what I want. Can you get me there?鈥� CIOs are m ore well-read, and they know what they want. It鈥檚 no longer about selling the technology; maybe it wasn鈥檛 ever about selling the technology. It鈥檚 more about selling the business solution. Once it鈥檚 implemented, what are the benefits that they鈥檙e going to realise? What is the innovation that we can bring to it, and what will be the ease of integration?鈥�

Ridgway says the biggest part of its business is its application managed service (AMS) offering, and it鈥檚 serving around 100 clients on that platform.

As for the transition to S4, he thinks 麻豆原创, 鈥渢ried to use a stick to force people to move. It realised that there weren鈥檛 enough consultants and capacity worldwide to move every single 麻豆原创 customer from ECC to S4. It found a lot of resistance from clients, and it had a couple of clients that left them because they felt irritated by the stick and the threat of having support cut off. Coca-Cola Beverages Africa moved off 麻豆原创 and went onto [Microsoft] Dynamics, which I think it found quite difficult. It鈥檚 a massive change and huge investment to start all over again. So 麻豆原创 did lose some clients, but it eventually moved the date out to 2027. It also changed the sales approach from a stick to more of a carrot.鈥�

The carrot appears to involve talking up the benefits and exciting new features of S4. Ridgway says 麻豆原创 has dramatically changed the way it does business. 鈥淚n the past, I think 麻豆原创 was seen as very arrogant, very expensive and very complex. Today, it seems less arrogant and is playing in a different market.鈥�

The company is now far more flexible with its pricing and subscription models. 鈥淚t鈥檚 also not as complex as it used to be. I鈥檝e been involved in projects that took 24 months from start to go-live. We鈥檝e just completed a project that was five weeks from start to go-live.鈥�

Do you still have customers who are on ECC? 鈥淟ots,鈥� he says. 鈥淭he biggest part of our consulting business is trying to convince customers who are on ECC that we get to the cloud, and we can get them to S4, with all the innovations and advantages.

鈥淪ome customers will say, 鈥榠f it鈥檚 not broken, I don鈥檛 need to fix it. It鈥檚 giving me what I need鈥�,鈥� he says, but he believes this will add business risk because if something does go wrong, 鈥渃ome January 1 2028, you have no support from 麻豆原创鈥�.

This conversation with customers reminds him of those held when the cloud model was nascent around a decade ago, and 鈥渆verybody said they had to move to the cloud鈥�, which he says is similar to where AI is at the moment. 鈥淣obody actually knows what it means. A lot of clients did rush to the cloud, and realised two years later that it was quite expensive, and it鈥檚 more complicated. But we鈥檙e a lot more mature now, 10 years in. There are a lot of clients who are hybrid. I think the way 麻豆原创 designed its solution allows for that.鈥�

He says with the application managed services part of the business, NTT will provide support for customers, many of which will sign up for a 36-month contract that will give them access to a call centre with certified, senior agents. As for what the customers are struggling with, he says the majority of the calls are related to password resets and authorisations.

Herzig, 麻豆原创 CTO, speaking at this year鈥檚 Saphire conference in Madrid, said the company doesn鈥檛 disclose the official numbers, but it has around 10 000 customers worldwide that are on the cloud path. 鈥淭he majority of customers are now making the cloud decision, specifically because the maintenance will definitely be over in 2028. And with all the innovation, there鈥檚 a very clear reason why they need to move.鈥�

Why don鈥檛 they want to move?

鈥淚t depends on the customer,鈥� he says. 鈥淭heir systems have grown, historically, and they鈥檝e been heavily customised. Back in the day, you could code left and right, because the code was open. Every table and every function module, and the systems integrators added their part to it. They did a lot of custom code. Maybe they were adding functionality, but they didn鈥檛 know [what effect it would have]. You鈥檇 start off with a very clean version of what 麻豆原创 delivered, and then we innovated with the product. We built capabilities, and we changed the technology, and the customer did the same. And the longer this branching took, the harder it is to reconcile.鈥�

* Article first published on聽

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The timing could hardly have been more pointed. Across the world, executives are losing patience with AI experiments that overpromise and underdeliver. Boardrooms have heard enough about pilots and proofs of concept, and now want systems that improve margins and forecast outcomes.

TechEd took place in that atmosphere, amid assurances that performance could replace promise. 麻豆原创 used the event to demonstrate how deeply AI now runs through its own operations before unveiling its next leap forward.

Rather than another round of hype about possibilities, 麻豆原创 aimed to show a working example of AI at scale, handling everyday complexity inside one of the world鈥檚 largest software organisations.

麻豆原创 chief technology officer and chief AI officer, Philipp Herzig, told Business Times at TechEd that the clearest evidence of maturity came from within 麻豆原创 itself. The company鈥檚 AI assistant, Joule, acts as a conversational layer across its software stack, connecting data, applications, and agents to automate tasks and surface insights on demand.

鈥淚f you look at AI at scale, what is really real and what is working very well, just look at Joule,鈥� he said. 鈥淚t鈥檚 used by more than 30,000 employees every month, about a third of the 麻豆原创 workforce. We have more than 100,000 policies and documents in different languages, and depending on where you work, in Brazil or South Africa, you get the correct HR or travel policy surfaced to you.鈥�

Herzig said Joule had become the company鈥檚 single interface for daily tasks. 鈥淵ou can do your expense reports, indirect procurement, and financial tasks all in one place. It works, and it works at scale. We have a thumbs-down rate of only 1%, which is phenomenal when you think about the size of the company.鈥�

Innovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the
driver鈥檚 seat 鈥� Muhammad Alam, 麻豆原创 executive board member

That success set the stage for TechEd鈥檚 central announcement: an AI model called 麻豆原创-RPT-1, short for relational pre-trained transformer. The model introduces a new class of AI: the enterprise relational foundation model. It interprets structured business data and the relationships within it, mapping how orders, invoices, logistics, and payments interact to forecast what comes next.

麻豆原创 described it as a model that 鈥渃an make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion鈥�. Instead of producing text, it reads how data behaves across systems, turning business logic into predictive insight.

Herzig said 麻豆原创-RPT-1 marked the transition from incremental automation to full predictive architecture.

鈥淲e see a shift from what I call a cloud-native architecture to an AI-native architecture, as AI becomes an ever-increasing part of the software stack. 鈥淲hat we wanted to solve are the problems where we have a reason to solve them: because we have the data, the relational data, the structured business data and so on. We set out this research project two years ago, talked a little about it, but now it actually becomes a reality.

鈥淚t鈥檚 a shift that needs several things to come together: the knowledge graph, this predictive model now with RPT-1, and of course the large language models. So there are many elements in the software stack that change. Every day, each little piece adds to this picture and solves a particular challenge in the stack.鈥�

Herzig said the greatest challenge lay in making this intelligence work at enterprise scale.

鈥淎nyone can do a demo. Getting it enterprise-ready at scale 鈥� that鈥檚 the tough challenge. That鈥檚 why we鈥檙e solving one problem after another, each for a specific outcome in the overall stack.鈥�

麻豆原创 executive board member Muhammad Alam tied this philosophy to the developer community.

鈥溌槎乖粹�檚 announcements give developers the tools they need to deliver at the speed of AI,鈥� he said.

鈥淚nnovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the driver鈥檚 seat.鈥�

That 鈥渇lywheel鈥� anchored the narrative of TechEd:
鈥� applications generate data;
鈥� data trains predictive models;
鈥� models return intelligence to the applications.

Each loop strengthens the next, creating a flywheel effect of acceleration of innovation. 麻豆原创 announced that it would equip 12-million individuals worldwide with AI-ready skills by 2030 through a partnership with Coursera that provides hands-on certification in 麻豆原创鈥檚 ecosystem. The goal is to align those skills with the AI-native architecture now taking shape inside the company. It also sends the message that people remain at the heart of the AI journey.

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business assistant, , is transforming how enterprises function by providing insights, custom-tailored outcomes, and a personalised experience for businesses. As a result, says 麻豆原创, the product with a name inspired by the unit of energy significantly improves productivity and efficiency.

鈥淲e are entering a new phase of AI with endless opportunities for all of us,鈥� said , CEO of 麻豆原创, at the Sapphire 2024 conference in Barcelona.聽 鈥淭he technical evolution continues by using the power of generative AI across our stack to revolutionise once again how businesses will run, and how your end users will work in the future.鈥�

麻豆原创 performed a detailed analysis of the tasks completed with cloud software by their 300-million end-users every day. Consequently, they announced, by the end of 2024 80% of the most used tasks will be managed via Joule.

鈥淎s a result, your end users will do their work 20% more productively and with higher quality,鈥� said Klein.

For example, Joule will manage tasks in all management areas to create and change orders. It will take over some sequential activities in billing and cash collections. In finance, books will be closed faster as the software manages standard compliance checks.

鈥淛oule will also turn into a very powerful analytical engine to create insight for the boards for finance, for HR, for all parts of your business.鈥�

The digital assistant will be an engine that offers strategic recommendations by having access to the broadest business-to-business data model in the technology industry, which includes finance, supply chain, HR, sales, or ESG data from thousands of customers. Based on this data, which allows 麻豆原创 to train algorithms and find certain performance patterns in one鈥檚 business, Joule will provide recommendations to collect cash faster and make smarter procurement and supply chain decisions.

During Klein鈥檚 keynote, he highlighted 麻豆原创鈥檚 recent partnership with leading AI chip-maker . A highlight was a video featuring a ringing endorsement of 麻豆原创 by , founder and CEO of Nvidia, in which he said: 鈥淲e have one of the most complex supply chains in the world. We build AI supercomputers, AI factories. Enormously complicated systems. Our latest Blackwell System has 600,000 parts.

鈥淎ll of this has to be delivered to some 100 different customers, and all of our entire supply chain, build materials, forecasting systems, build plans, those 600,000 parts, and some 40 different manufacturers are all managed under 麻豆原创.

鈥淭his is the beginning of a new computing age, the beginning of a new industrial revolution. Through Joule, we have created some amazing generative AIs that understand the languages of 麻豆原创, supply chains, and enterprise planning systems so that we have a specialist, an agent, an AI to help us to do all the things that we want to do to manage our business.鈥�

, chief AI Officer of 麻豆原创, said: 鈥淓arlier this year, we gave Joule internally to up to 4,000 service consultants within 麻豆原创. What we observed is that they can save up to 2 hours every day.鈥�

More than 50 of 麻豆原创鈥檚 certification programmes, more than 200,000 pages, and 2Tb of curated best practices and community content, have been put into Joule. On top of that: it is supercharged by Nvidia鈥檚 technology.

鈥淚f you take this productivity gain and multiply it by our ecosystem, we can altogether save up to 600-million working hours every year, accelerating our transformation to the cloud,鈥� said Herzig. 鈥淭his technology is based on more than 250-million lines of code out of S/4HANA based on Nvidia鈥檚 hardware and software, as well as other models.鈥�

麻豆原创 has also expanded its strategic partnership with Google to enhance supply chain risk prediction and mitigation in volatile global environments. This collaboration integrates 麻豆原创鈥檚 Joule and with Google鈥檚 assistant and 鈥檚 Data Foundation, boosting forecast accuracy and enabling quicker responses to demand signals.

麻豆原创 has partnered with , leveraging its advanced language model to create images in and generate scripts for highly customised analytics applications in . Meta鈥檚 AI model excels in language nuances and contextual understanding, making it ideal for transforming enterprise business requirements into actionable insights.

Read more articles covered at Sapphire 2024 on Gadget:聽听补苍诲听

* Jason Bannier is a data analyst at World Wide Worx and writer for聽. Follow him on聽听补苍诲听聽at @jas2bann.

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