Li茅nard is an information scientist and business informatics professional who joined 麻豆原创 in 2012. As senior vice president and head of ABAP platform at 麻豆原创, she is responsible for ABAP and all matters related to ABAP platform. In this role, she is also the head of ABAP AI and thus globally responsible for the latest developments and innovations in this domain.

In this interview, she discusses ABAP, the role of AI in development, how agentic AI will transform legacy applications, and what’s next.

Q: What is ABAP, exactly? How would you explain it to someone who might have heard of it but doesn鈥檛 really know what it is? And why is ABAP so important for enterprise software?

A: ABAP has a very long history at 麻豆原创. It is the company鈥檚 first and only proprietary programming language and turned 40 in 2023鈥攁n unusually long run in the fast-changing world of software.

What sets ABAP apart from other programming languages such as Java or C++ is that it was specifically designed for building and optimizing the business applications that large enterprises rely on every day. Among its many features is a high level of abstraction, which makes it very easy for developers to write or extend business software. It also reduces complexity because security concepts, authorization checks, and quality controls are already embedded in the language. This allows developers to focus entirely on the business logic鈥攖hat is, on the tasks they want the program to perform.

Over the years, ABAP has evolved to keep pace with how companies deploy software. The newest version is ABAP Cloud, which has a restricted language scope and is designed to support development in what 麻豆原创 calls a 鈥渃lean core.鈥� This is essential for running our cloud products. Enterprises still operating in a non-cloud environment can use ABAP Cloud to prepare the code in their on-premise systems or in 麻豆原创 S/4HANA Cloud Private Edition in such a way that it can also be run in the cloud.

Beyond its role as a programming language, ABAP is also a platform. ABAP platform is the foundation that underpins all of 麻豆原创’s core solutions, from older installations such as 麻豆原创 ERP Central Component (麻豆原创 ECC) to on-premise solutions, 麻豆原创聽S/4HANA Cloud Private Edition, and 麻豆原创聽S/4HANA Cloud Public Edition.

Q: Will ABAP continue to play a crucial role for 麻豆原创 customers?

A: Yes, both in terms of the programming language and the platform ABAP is still highly relevant. The programming language looks very different to the way it did 40 years ago of course鈥攂ecause we have continuously refined it over the years鈥攂ut it still forms the backbone of 麻豆原创鈥檚 core ERP solutions and extensions. There are roughly five million registered ABAP developers worldwide today, with around two million actively developing.

Through ABAP Cloud and our dedicated ABAP AI team, ABAP has evolved into a modern development language for business solutions. I don’t know of any other programming language that covers this scope. It is used globally. Almost all the world鈥檚 100 largest companies are 麻豆原创 S/4HANA customers, and underneath it always runs ABAP platform.

Q: How will AI shape ABAP development going forward?

A: For me as head of ABAP platform, this is one of the questions that intrigues me most. AI has completely disrupted the technology market. This of course also impacts the 麻豆原创 developer portfolio and how we customize and extend our solutions. We have therefore invested in AI-powered efficiency tools, such as a chat assistant that explains code on the fly. Another is 鈥済host texting,鈥� a feature that generates code suggestions while the developer types.

In the coming years, AI agents will be able to generate code鈥攊ncluding at the scale demanded of large enterprises鈥攁nd even build entire solutions. We believe that the next wave of AI will not just assist programmers but take on many of the routine tasks they perform today.

A crucial question for 麻豆原创 is: how can we leverage AI to translate legacy code into modern code without losing the underlying business logic that makes each system unique? A lot of our customers are still operating older solutions, including those based on 麻豆原创 ECC. So, we need to provide a clear migration strategy and the right tools to simplify and accelerate their move to the cloud.

That’s why we’re currently developing a service that will work for everyone鈥攔egardless of which system version they run. The aim is to bundle all of 麻豆原创鈥檚 ABAP AI capabilities into a single offering that can boost developer efficiency and allow custom code to be migrated. Ideally, this service will be agent-driven鈥攁s 鈥渁gentic AI.鈥�

Q: What is agentic AI?

A: Agentic AI works with so-called 鈥渁gents.鈥� Agents have specialized capabilities, can communicate with each other, exchange results, and thus solve highly complex tasks together. How they collaborate varies based on the complexity of the use case.

Most approaches involve an 鈥渙rchestrator,鈥� a lead agent that manages other agents to complete a particular task. The orchestrator does not have to call on the individual agents in a fixed order鈥攔ather, its greatest strength lies in intelligently combining the agents in dynamic, adaptive networks.

So, it鈥檚 no longer just about making human developers more efficient. When agents are powerful enough, they can build entire applications and thus take on part of the developer’s tasks. In our case, agentic AI can support the very complex task of transforming code, accelerating it significantly and reducing complexity.

This approach relies on different agents that focus on different aspects of the task: for instance, one agent specializes in explaining custom code; another makes code changes; and a third estimates the effort of a transformation project. When these agents collaborate, that’s when the real magic of agentic AI happens.

AI will radically change the role of developers. Despite continuing to set the direction, they will increasingly focus on business logic rather than on the coding itself. They will work with the code generated by AI systems, checking that it is correct, secure, and aligned with the problem they鈥檙e trying to solve. Thought leadership, however, will remain firmly with people. Developers will continue to decide what matters and communicate their instructions to AI through good prompts. The entire AI domain is extremely dynamic and evolving at astounding speed. Powerful solutions are already available today, so this isn’t a distant vision鈥攊t’s already upon us.

Q: How do customers benefit from agentic AI?

A: Agentic AI will deliver significant value in transforming legacy applications and custom extensions into cloud solutions from 麻豆原创, and thus the latest ERP versions. In February 2026, we extended our existing custom code management app with AI features that help developers understand what the code is doing and what changes are needed to future-proof it. And, of course, AI also provides recommendations on how the code can be extended. In the future, we will complement all this with agents. However, this will take some time, as we refuse to compromise on quality and security.

We are also investing in the developer experience with ABAP platform to make it as easy to use as possible. Here, agentic AI will help reduce the complexity that has built up over decades of development.

Q: Should we be worried about security?

A: No, we deliberately allow sufficient time before any release to make sure that quality and, above all, security meet a high bar. Don鈥檛 worry: AI won鈥檛 take control and generate or integrate solutions unilaterally or unchecked. Humans will remain in charge every step of the way and will always have the last word when it comes to ensuring that code complies with our standards.

Q: Where are we now and what鈥檚 next?

A: ABAP AI tools aimed at boosting developer productivity have been available since February 2025, and we are now building agentic AI in the ABAP context. However, it鈥檚 early days and agentic AI still must prove itself in practice. As I see it, though, it will transform the market.

As part of our road map, we released , a custom-trained, specialized AI model, on the generative AI hub in early January 2026. This model is specifically designed to explain ABAP program code.

Next, we plan to make all ABAP AI tools available as an independent side-by-side service. In a subsequent phase, we will transition the use cases embedded in those tools to agents.

In addition, we are expanding our cloud-based ABAP development into additional development environments (IDEs), especially ABAP development tools for Visual Studio Code. So, the team will also tap into the AI tools available there as part of our push toward agent-driven development.

This first appeared on the German 麻豆原创 News Center.

Over decades, our customers have built powerful digital foundations on 麻豆原创 to run end-to-end business processes across their enterprises鈥攐ften extending and customizing these systems to support their unique business needs. Now, many are entering the next phase of transformation: modernizing their 麻豆原创 landscapes to unlock the full potential of AI.

As companies move to cloud-based 麻豆原创 environments and clean-core architectures, they are preparing to embed intelligence directly into business processes. This enables new forms of automation, with AI agents that operate across enterprise systems and execute increasingly complex tasks.

Modernizing these systems while introducing AI at scale is a significant undertaking. It requires technologies that integrate with existing applications, operate reliably within mission-critical workflows, and meet the governance standards enterprises demand.

That鈥檚 why, over the past few years, we have partnered with to combine advanced AI technology with deep business context. Our goal is to help organizations accelerate modernization and apply AI across the applications and processes key to their success. This collaboration will be showcased at NVIDIA GTC.

Building the foundation for enterprise-grade AI

Through our collaboration with NVIDIA, we are accelerating the entire life cycle of enterprise AI鈥攆rom model development to high-performance runtime execution鈥攁nd powering AI scenarios across our portfolio. ,  which consists of open libraries such as and , helps accelerate large-scale model training across distributed RL environments. It enables teams to build and refine enterprise-grade AI models faster.

Models are hosted through  and , where our customers and partners leverage those best suited to their use cases. microservices optimize inference performance, and we have observed up to a 20% improvement compared to another popular open source serving engine. Enabled by NVIDIA GPUs and NVIDIA NIM, the increased performance allows organizations to combine advanced AI models with trusted 麻豆原创 business data and processes to ensure that AI operates within the workflows that drive business operations.

Modernizing the business logic that runs the enterprise

AI models trained on 麻豆原创 knowledge and accelerated using NVIDIA technologies are already helping customers tackle some of their most pressing modernization challenges. For example, evolving business logic embedded in the 麻豆原创 systems that run their operations.

For decades, organizations have extended 麻豆原创 applications with custom ABAP code that reflects how their businesses operate. That logic captures years of operational knowledge across finance, supply chain, service processes, and more. But modernizing these environments for the cloud and preparing them for the next generation of AI-driven innovation can be complex.

To help accelerate this journey, 麻豆原创 developed . a foundation model trained exclusively on real-world ABAP code and the business logic used across 麻豆原创 environments. The solution incorporates specialized models for code-related tasks, including StarCoder2 for code completion, and Codestral for deeper code understanding and explanations. These models are served through NVIDIA NIM microservices to deliver high-performance inference.

brings these capabilities into the developer experience, helping teams analyze existing ABAP code, understand how customizations interact with core business processes, and generate new code when needed. By making decades of embedded business logic easier to interpret and update, we help organizations accelerate modernization and preserves the knowledge that makes their operations unique.

Connecting AI to business operations

The collaboration between 麻豆原创 and NVIDIA also explores how AI can operate within enterprise workflows to help organizations apply intelligence across both physical operations and complex planning environments. One emerging area is embodied AI, in which intelligence extends beyond software systems into the physical world. By combining AI reasoning with sensors, robotics, and enterprise data, organizations can connect real-world observations directly with digital business processes.

For example, predictive maintenance alerts from can trigger robotic inspections that analyze equipment using thermal, visual, and acoustic signals. These signals are evaluated alongside asset histories and maintenance records to identify potential issues. then orchestrates follow-up actions through , prioritizing work orders and guiding technicians with the right operational context. By linking physical-world insights with enterprise workflows, organizations can turn physical-world signals into coordinated enterprise actions.

The same principle applies to complex planning environments. Supply chains today must manage a constantly shifting web of constraints, from supplier availability and transportation disruptions to evolving customer demands. With NVIDIA, we are exploring technologies, such as NVIDIA Metropolis and NVIDIA Cosmos, to bring the latest AI advancements into warehouse management, safety, and asset inspection.

Together, we are also bringing new capabilities to  that combine agent-based reasoning with the  GPU-accelerated optimization engine. This enables planners to simulate complex supply chain scenarios and evaluate alternatives with more speed and accuracy. By integrating advanced optimization with 麻豆原创鈥檚 supply chain planning capabilities, organizations can dynamically model constraints, adapt plans as conditions change, and make more confident decisions in increasingly complex environments.

Collaboration with large-scale 麻豆原创 customers helps identify real operational bottlenecks, paving the way for AI-driven solutions. . The Taiwan-based global electronics manufacturer and manufacturing solutions provider will work with 麻豆原创 to develop AI-powered innovations for manufacturing and supply chain operations.

By combining 麻豆原创鈥檚 enterprise applications and business context and Foxconn鈥檚 manufacturing expertise, organizations can enhance operational efficiency, increase resilience, and advance decision-making across complex production and supply networks.

Experience agentic AI at NVIDIA GTC

麻豆原创 is enabling Joule Agents across its application portfolio, helping organizations automate tasks and coordinate complex workflows within business processes. At NVIDIA GTC, visitors will see how these capabilities are extended using  on to build agents tailored to specific enterprise scenarios.

And because 麻豆原创鈥檚 AI architecture is model-agnostic, organizations can bring their own models into these workflows, in addition to those deployed through 麻豆原创 AI Core. The hands-on experience at NVIDIA GTC will demonstrate how organizations can build AI-driven workflows that operate directly within the enterprise systems that run their business.

It all happens at , taking place March 16-19, 2026. Join us to see how 麻豆原创 and NVIDIA are helping organizations modernize enterprise systems, accelerate AI adoption, and move toward the AI-native enterprise:

• Attend our session: on Tuesday, March 17, from 2:00-2:40 p.m.
• Visit the 麻豆原创 booth, #2001, to explore agentic AI in action, participate in hands-on vibe-coding with Joule Studio, and witness next-generation enterprise automation

.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI.