This is because, today, technology is transforming transformation, says , vice-president and global innovation analyst at 麻豆原创.

And today, transformative technology is all about artificial intelligence (AI).

鈥淓xperts agree that AI is the third great wave of technology after the personal computer (PC) in the 1980s and the Internet in the 1990s,鈥� Elliott says.

鈥淏ut if you are not careful, waves can come crashing down on top of you. AI is very powerful, and can have unintended consequences.鈥�

Although it is most obvious in the visual context, AI is progressing in all directions, Elloitt points out. 鈥淭he process of rapid technological change and disruption is coming to your industry and your line of business.鈥�

Today, what鈥檚 holding up AI disruption is the fact that the rest of the ecosystem isn鈥檛 able to change as quickly.

鈥淚t鈥檚 a bit like parachuting a Formula 1 car into a medieval village. You can鈥檛 use the technology because the rest of the ecosystem isn鈥檛 there.

鈥淚n fact, if AI stopped progressing right now, we couldn鈥檛 use it to its full potential for 10 years.鈥�

For this reason, Elliott says AI could be taken to stand for accelerate innovation. 鈥淲e should use AI to adopt AI.鈥�

Today, a lot of AI and innovation in general is handmade, with developers all starting from scratch and working on one thing.

鈥淭o accelerate the adoption, we need to move to modern assembly lines, so we can use the AI faster,鈥� Elliott says.

He adds that 麻豆原创 provides the machines to build the technology.

鈥淲e offer things like the S, where customers create data that is harmonised in the Business Data Cloud, used to ground AI, which then goes to making a better application. And, as you create applications, you can add customisation.鈥�

麻豆原创 embeds AI in all its applications, and these are available to customers right now, Elliott adds.

鈥淏ut innovation is not actually about technology,鈥� he explains. 鈥淲hat is holding us back is the time, skills, and resources needed to exploit the technology.鈥�

Again, the 麻豆原创 Business Technology Platform can help, with 麻豆原创 Build offering aided innovation 鈥� another possible use for AI 鈥� for agile application development and customisation.

鈥淲e have the resources that can multiply efforts, let developers do more, faster, with customisations and extensions in the cloud.

鈥淎nd now you can do it in natural language, because it is designed for professional developers and also business users.鈥�

AI could also stand for automated identity, Elliott says.

And it could represent automatic integration as well. 鈥溌槎乖� Integration Suite is about AI-powered connections. It will do most of the heavy lifting in connecting system to one another.鈥�

Meanwhile, the 麻豆原创 BTP AI Foundation is a generative AI (GenAI) hub that gives customers everything they need to orchestrate customised AI, with a wide choice of models, grounding, data masking, filtering, prompt management, and lifecycle management.

At the core of any transformation is data, which is everyone recognises is important. 鈥淚n fact, it is really important,鈥� Elliott says. 鈥淚t is the essential input for AI in particular.

鈥淪o AI has to stand for accurate information. AI is useless without accurate information in realtime.鈥�

But there isn鈥檛 a company on the planet that doesn鈥檛 have a data quality problem, he adds.

鈥淚t is not a new problem, we know it鈥檚 hard to get budget to fix data. But this is an opportunity to ask for money to do just that. Now is a great time to establish governance, data ownership, and all the things you have wanted to do for years.鈥�

The second big problem is integrating data 鈥� which is also not a new problem.

鈥淭his is where comes in,鈥� Elliott says. 鈥淎nd we offer it as a service, so we can do more of it for you 鈥� integrate data from 麻豆原创 applications and also external data.鈥�

The helps with relationship extraction. 鈥淵ou already have this information, now we are gathering it and exposing it, and putting it in a format that you can use for your AI,鈥� Elliott explains.

AI could also represent analytics improved, which is why 麻豆原创 Analytics Cloud has the GenAI-powered Just Ask feature.

鈥淚n analytics, there is a dream of total situational awareness. Of bringing in data from all sources, but having overall vision. AI can be used to connect data and deal with fuzziness.鈥�

AI could also stand for agents incoming. 鈥淭his is the next generation of AI, moving along from the current efficient processes with pre-defined steps. But these processes can鈥檛 deal with ambiguity. Agents, on the other hand, can reason through what it needs to do next. So the processes and data can be a lot more unstructured than before.鈥�

Another interpretation of AI could be agents for business, Elliott adds. 鈥淭his is the next evolutionary step 鈥� in fact, some companies are already doing it 鈥� using AI to get rid of a lot of manual work.鈥�

AI could be used for automatic innovation. 鈥淲e have incredible amounts of information and amazing intelligence,鈥� says Elliott. 鈥淪o why not let the systems tell us what we should do next?

鈥淲e are starting to get there, and you can already get great automated system insights. And 麻豆原创 also has best practices, so we can provide customers with that out of the box.鈥�

Once that is in place, and customers need to implement their systems, automated implementation could be the final meaning for AI.

鈥淥f course, technology is not everything,鈥� Elliott points out. 鈥淵ou need to get people to adopt new ways of working 鈥� and AI is good for adoption optimisation and change management.鈥�

In conclusion, Elliott believes that AI today is like a staircase. 鈥淵ou have to take one step after the other. But in the future it will be more like an escalator.鈥�

The key to successful AI adoption lies not in the technology he adds, but in the people, culture, and organisation.

鈥淣o matter how much you have invested in adoption, culture, and change management 鈥� invest more.鈥�

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Other innovations include the 麻豆原创 Knowledge Graph, targeted at developers to help them connect 麻豆原创 data聽with business context. Available via 麻豆原创 Datasphere and Joule in Q1 2025, it aims to enable organisations to map relationships across 麻豆原创鈥檚 data landscape for improved decision-making.

Speaking to ITWeb ahead of the conference, Jesper Schleimann, head of business AI for 麻豆原创 in EMEA, said, 鈥淲e’re really taking AI to the next level, and the transformative part is AI agents that are now in true business form. It also means our developers and our customers can accelerate their journeys to the cloud. We cannot forget that data is an important part in making all this happen.鈥�

Schleimann said this is a significant step in 麻豆原创鈥檚 AI journey, with Joule in the driving seat.

We’re really taking AI to the next level, and the transformative part is AI agents that are now in true business form.

鈥淛oule is celebrating one year now, and a lot has happened in the underlying models powering the AI revolution. The new graph service will enable it (Joule) to build the bridge and be grounded into the entire ecosystem of 400 000 different tables. That’s keeping all the 麻豆原创 data and things in check.鈥�

Users now have access to support across nearly 80% of 麻豆原创鈥檚 portfolio, meaning the same copilot can assist across the entire 麻豆原创 suite,聽Schleimann explained.

麻豆原创 is also enhancing Joule with new skills, allowing users to create their own custom micro-skills.

鈥淵ou can do things in a low code no code way and you tell Joule how to think of it like a mini workflow. So, if a user alters a part of a travel booking or purchase order process, for example, and you have your own way of doing it, you can simply, tell Joule what steps it should take, and then you can use its reasoning engine and the underlying tools to make that a reality.鈥�

Joule in action: Use cases

麻豆原创 will introduce two use cases to showcase the agent鈥檚 transformative power:

• A dispute management use case employs autonomous AI agents to analyse and resolve dispute resolution scenarios including incorrect and missing invoices, unapplied credits and denied or duplicate payments.
• A financial accounting use case employs autonomous AI agents to streamline key financial processes by automating bill payments, invoice processing, and ledger updates while quickly addressing inconsistencies or errors.

GenAI tools for developers

During the two-day TechEd Virtual, 麻豆原创 will聽introduce additional GenAI developer tools,聽including code explanation and documentation search in 麻豆原创 Build. These tools are expected to reduce聽development time for Java and JavaScript developers.

It is also adding an Extensibility Wizard to聽麻豆原创 Build, which will聽enable developers to access 麻豆原创 Build directly from 麻豆原创 S/4HANA Cloud Public Edition.

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Joule, 麻豆原创鈥檚 AI business assistant, has been enhanced with new features, including collaborative AI agents that expand the platform鈥檚 capabilities.

Announced during the online 麻豆原创 TechEd 2024 conference yesterday, these agents are designed to support 80% of 麻豆原创鈥檚 most-used business tasks. They embed Joule more deeply across 麻豆原创鈥檚 portfolio and enhancing productivity by breaking down silos, said the software maker.

鈥溌槎乖粹�檚 innovation drives real business outcomes, and today鈥檚 advancements help customers harness the power of AI, data, and new development solutions to catalyse growth,鈥� said Muhammad Alam, member of the 麻豆原创 executive board.

鈥淒rawing on 麻豆原创鈥檚 unmatched business and technology expertise, the AI innovations we鈥檙e announcing at TechEd forge a new human-AI partnership to transform the landscape of modern business.鈥�

Joule Studio, a development tool that allows users to build and customise applications using Joule鈥檚 AI capabilities, is now integrated into 麻豆原创 Build. A low-code app development platform, Build enables both business users and developers to create and deploy custom Joule skills.

Michael Ameling, EVP and chief product officer of 麻豆原创 said: 鈥淲e are making Joule even more flexible and useful with Joule Studio in 麻豆原创 Build. It allows partners and customers to create and deploy custom Joule skills.鈥�

Joule has also expanded to include collaborative AI agents that autonomously handle intricate business workflows, adapting their strategies to meet shared objectives. These multi-agent systems tackle specific tasks, allowing them to work together on complex business processes.

鈥淛oule now goes Agentic,鈥� said Ameling. 鈥淪pecialised AI agents work autonomously together to solve complex problems.鈥�

New improvements to the ABAP (Advanced Business Application Programming) large language model (LLM) were introduced. This LLM, trained on over 250-million lines of 麻豆原创 S/4HANA Cloud code, is now integrated into the open-source Eclipse framework, providing ABAP developers with enhanced tools for development. Currently available to select customers and partners in an early adopter version, the ABAP LLM is expected to be generally available in the first quarter of 2025.

麻豆原创 Build is launching new AI-driven features like code explanation and documentation search, aimed at reducing development time for Java and JavaScript developers. The platform鈥檚 Extensibility Wizard will let developers access 麻豆原创 Build directly from 麻豆原创 S/4HANA Cloud Public Edition, simplifying the process of extending 麻豆原创 solutions.

Two real-world use cases were highlighted during the event:

1. Dispute management: Autonomous AI agents analyse and resolve disputes related to invoices, unapplied credits, and denied payments.
2. Financial accounting: AI agents automate key financial tasks such as bill payments and ledger updates while quickly addressing inconsistencies.

麻豆原创 Knowledge Graph

麻豆原创 also unveiled the 麻豆原创 Knowledge Graph, a solution designed to unlock more value of 麻豆原创 data by connecting it with rich business context. Accessible through 麻豆原创 Datasphere and Joule in early 2025, it will help users map relationships across 麻豆原创鈥檚 vast data landscape, empowering better decision-making.

鈥淏y combining the knowledge graph engine with the vector engine, you can harness the power of structured and unstructured data,鈥� said Ameling. 鈥淭his is enabled through a triple store implementation graph data processing and analytical capabilities via SPAR-QL. As such, the knowledge graph engine can also enhance work scenarios and make them even more precise and relevant.鈥�

* Visit the 麻豆原创 website聽.

* 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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