It won’t be long before AI agents will write code and transform legacy applications for use in the SAP cloud. Sonja Liénard, head of ABAP platform at SAP, talks about the future of SAP’s iconic ABAP programming language and ABAP platform.

Liénard is an information scientist and business informatics professional who joined SAP in 2012. As senior vice president and head of ABAP platform at SAP, 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’t really know what it is? And why is ABAP so important for enterprise software?

A: ABAP has a very long history at SAP. It is the company’s first and only proprietary programming language and turned 40 in 2023—an 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—that 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 SAP calls a “clean core.” 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 SAP 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 SAP’s core solutions, from older installations such as SAP ERP Central Component (SAP ECC) to on-premise solutions, SAP S/4HANA Cloud Private Edition, and SAP S/4HANA Cloud Public Edition.

Q: Will ABAP continue to play a crucial role for SAP 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—because we have continuously refined it over the years—but it still forms the backbone of SAP’s 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’s 100 largest companies are SAP 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 SAP 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 “ghost texting,” a feature that generates code suggestions while the developer types.

In the coming years, AI agents will be able to generate code—including at the scale demanded of large enterprises—and 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 SAP 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 SAP 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—regardless of which system version they run. The aim is to bundle all of SAP’s 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—as “agentic AI.”

Q: What is agentic AI?

A: Agentic AI works with so-called “agents.” 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 “orchestrator,” 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—rather, its greatest strength lies in intelligently combining the agents in dynamic, adaptive networks.

So, it’s 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’re 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—it’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 SAP, 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’t worry: AI won’t 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’s 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’s 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 SAP-ABAP-1, 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 SAP News Center.

Nearly every enterprise leader today thinks about how to leverage AI to accelerate business outcomes—where to get started is another matter.

A great way to break through that roadblock is to listen to leaders who jumped in early to use AI to transform outcomes. Eli Lambert, a managing director of Finance in the Global IT division at Accenture, is one of those people.

The professional solutions and services company employs nearly 780,000 employees across 52 countries, who work with 350 partners to serve over 9,000 clients. The idea of transformation at Accenture’s scale might be intimidating to some, but not Lambert. He’s leading an ongoing transformation of Accenture’s finance function, which he calls “the heartbeat” of the company.

The results he’s achieved— including saving the finance team a combined 57,000 hours annually by having AI generate narrative summaries for reporting—shine a spotlight on what’s possible. And he’s just getting started.

I had a chance to speak with him about how he became a leader in AI-driven transformation, and what others can learn from his achievements. This is a lightly edited version of our conversation

Q: As you know, innovating with AI is about reshaping how a business delivers value. But not every business leader is leading the charge. Some are watching and waiting. Why did you roll up your sleeves and decide to be on the forefront?

A: Taking a leadership position on AI is important to keep moving forward and shaping new services and capabilities. For example, across a company our size, even though we’re hyper focused on emerging technologies, we can find small problems across our technology landscape. There are processes and data living in different places and silos develop over time. Most large companies have this challenge. But those are valuable processes, and the business data we have is especially valuable. AI opens up new opportunities to bridge those gaps and deliver more end-to-end outcomes, so that our finance function can meet the growing business expectations of our stakeholders.

For many companies, the key to getting impactful results from business AI is to start with one function that’s central to business performance. Why was finance the right place for you to begin, and what did you want to achieve?

I always say finance is the heartbeat of our organization. I heard one of our global IT leaders use that phrase, and while it was inspirational, it also made me think, “Let’s not accidentally cause a heart attack for the organization.”

Jokes aside; he was right. Your transactional and operational data flows through finance, and management decisions sit on top of it. Starting there gave us the ability to make end-to-end impact across processes that touch procurement, liquidity, forecasting, receivables, and more. And SAP gives us a digital core where all that transactional data is harmonized.

The bottom line is that finance is the natural starting point if you want to move from reactive reporting toward more proactive, AI-driven insights that you can use to help move the business forward. So, we set out to unify data and transform finance processes in a way that scales across the whole value chain.

Cash and liquidity are so important in the finance function, and to an entire company. But managing it requires bringing together data, forecasting, and decision-making across many teams. How did AI help?

If finance is the heartbeat of a company, cash and liquidity are the lifeblood of your systems. Here’s a great example: Accenture engages in a lot of acquisitions, and we run operational cash in 50-plus countries, so it’s easy for decisions to default to historical, manual reviews. That’s what was happening at Accenture before a forward-thinking leader stopped by and asked if we could apply machine learning to the problem. Great leaders often ask great questions, and that one really got us thinking.

[AI] freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth.

Eli Lambert

We took inspiration from retail: how stores treat inventory based on discounts and sales. If you treat cash like stock, you can apply those same learning models to figure out how much you really need to hold onto at any point in time. That’s how we built what we call “Intelligent Cash.” It brings all the business data together into a single data mart, a repository for structured data for a specific department or line of business, and uses machine learning to generate recommendations that our teams can act on.

AI is so good at this, and here’s what’s incredible: It freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth. Now what used to take months, or even more than a year to build, we can now do it in days or weeks because SAP’s data cloud brings [SAP] Datasphere, Databricks, and our machine-learning workloads into one place. The result is faster decision-making, better visibility, and much more accurate forecasting.

I love hearing about how you were able to use gains, delivered through strategic AI innovation, and then channel those gains into a high-value activity for the organization.  I know you also worked on receivables, something that impacts cash flow and customer relationships. What pain points did you face, and how did automation and machine learning transform the process?

Receivables were highly manual compared to payables. Clearing was inconsistent, and reconciliation took a lot of time because payments often come incomplete or with partial data. Anyone who works in or near finance knows exactly what I’m talking about. So, we co-developed on the SAP platform a machine-learning-based receivables solution. It more than doubled the automation rate for receivables processing and tripled automatic reconciliation, about a 300% improvement.

As part of that, we introduced high-confidence, one-click matching recommendations that reduce errors and cut down the manual work. We saw a seven percent uplift in auto-clearing with a cash application scheduler built on the SAP platform that delivers matches about 77% faster. All of that adds up to a more efficient receivables process, improved cash-flow visibility, and better productivity for the team.

In a global organization like Accenture, reconciling financial data and surfacing meaningful insights can be a huge amount of work. You turned to generative AI to help, which is really smart. What led you to that approach, and how is it changing your team’s day-to-day experiences?

We were dealing with balance sheet reconciliations across 50-plus countries, and the process was decentralized. I know a lot of companies face this problem. So, first, we moved everything online. Then we brought in machine learning and generative AI to analyze cost categories, summarize data, and surface important shifts.

[Our] Intelligent Financial Advisor, built on the SAP platform, can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That’s saved about 57,000 hours globally. Our teams can focus on higher-value analysis instead of manual reconciliation.

Eli Lambert

We then deployed an Intelligent Financial Advisor built on the SAP platform that can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That’s saved about 57,000 hours globally, just in controllership work, and helped us move to a three-day global close instead of five. The insights come faster and clearer, and the teams can focus on higher-value analysis instead of manual reconciliation. It’s also helping create more consistent roll-ups across regions and letting us use our talent more strategically.

I’m hearing this theme of not only measurable business gains from outputs, but the ability to better allocate time from manual, rote tasks to ones that deliver far more value for the business. That also applies to planning and forecasting. How did you bring AI into that part of the finance function?

Our planning work had grown too complex. Remember, we’re a large-scale, multifaceted global business. So, we replaced old models with SAP Analytics Cloud, which gives us multi-year planning models enhanced by AI.

We applied it first to merger and acquisition modeling, where accuracy really matters. It lets us model very complex data sets and helps our finance team collaborate more easily across the business. The results have been more accurate forecasts, reduced risk of errors, and much better collaboration between executives and practitioners. Early results were strong, and that encouraged us to expand AI use in planning more broadly.

What advice do you have for leaders who are not as far along in using AI to supercharge business results?

First, start with a high-impact function tied to real outcomes. Then focus early on data quality and harmonization; it’s the foundation for everything that comes after. Then get your cadence right and your team working together. Hone in on the use cases that really matter to you—the best vendors can help you identify those—and make sure to get the help you need from those vendors and their partners.

Use AI to spur growth. At Accenture, we’ve been able to use AI to save significant cash in one area, which we then invest in another, high-growth process—acquisitions in our case. That’s how you use AI to really rethink your business and move it to the next level.

Eli Lambert, on advice to other enterprises

As you go, take a crawl-walk-run approach: start slow then increase the pace of scale and adoption over time. Be sure to invest in change management and upskilling as you go to spur learning and adoption. And partner closely with technology providers and system integrators who’ve been there before. That accelerates everything.

The final suggestion I have is to use AI to spur growth. At Accenture, we’ve been able to use AI to save significant cash in one area, which we then invest in another, high-growth process—acquisitions in our case. That’s how you use AI to really rethink your business and move it to the next level. And that’s possible today in ways that were not, even five years ago. Seize that opportunity.

I couldn’t agree more with Lambert. AI really does provide an opportunity to re-imagine entire business processes for greater impact.

To keep exploring what’s possible, learn more about what Lambert’s team has done with AI at Accenture. Then see more AI use cases in finance and across all your key functions, including procurement, supply chain, manufacturing, and more.

Brenda Bown is chief marketing officer for SAP Business AI.

In this expert talk, discover how Service with Advanced Execution in SAP Cloud ERP Private transforms complex service operations into a streamlined, highly coordinated end‑to‑end process.

Join the host Anastasia Zvonov, with SAP experts Abhishek Shimoga Ramesh and Gert Tackaert, as they explore how organizations can connect technical planning, commercial service management, and real‑time financial insights into one powerful, integrated framework.

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Whether you operate in manufacturing, industrial machinery, energy, or any field with resource‑intensive service scenarios, this deep‑dive will clarify how SAP’s service capabilities help you run smarter, faster, and more profitably.

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00:00 – Introduction to Service with Advanced Execution
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03:40 – When to Choose Advanced Execution
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💬 Join deeper conversations in the SAP Community: https://sap.to/6054B61leO
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About SAP:
As a global leader in enterprise applications and business AI, SAP stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit: https://sap.to/6059B61leT

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Niklas Siemer is joined by Sascha Vierlinger, Product Manager for the SAP Key Management service, to unpack customer-owned encryption in cloud environments, and why more organizations want control of the “last layer” of trust: the keys.

They walk through why encryption matters more in the cloud, the difference between SAP-managed and customer-managed keys, and what a modern Key Management Service (KMS) needs to deliver: secure key storage, lifecycle controls (enable/disable/delete), auditability, and operational safeguards such as the four-eyes principle. The conversation also covers the classic “red button” scenario (cutting off access quickly in an emergency) and how SAP’s new KMS is designed as a more scalable, central approach compared to the legacy SAP Data Custodian KMS.

In this episode, you’ll learn:
✅ Why who controls the key is effectively who controls the data (especially in regulated industries).
✅ The three models for key control: Sub-managed keys, Bring Your Own Key (BYOK), and Hold Your Own Key (HYOK).
✅ What “key lifecycle” really means (rotation, disabling, deletion—and the very real risks).
✅ How SAP’s new KMS supports stronger governance with audit logs and multi-party approvals.
✅ What to expect commercially (licensed product + connections) and how this fits with SAP BTP usage.

Chapters:
0:00 – Cold open: “If I remove the key… no one can access my data”
0:21 – Welcome + episode topic
0:38 – Meet the expert: Sascha Vierlinger
1:29 – Why encryption matters more in the cloud
3:03 – SAP-managed keys: what it means for customers
4:57 – Why

Read more about the new Key Management Service. 👉 https://sap.to/6057hdKWU

Real-time decisions require accurate data to be accessible at the moment action matters most.

Endress+Hauser technicians depend on equipment lifecycle data to service and optimize customer systems in real time, but fragmented systems made it difficult.

With SAP Business Data Cloud, Endress+Hauser is combining operational data dispersed across systems into a unified data fabric, enabling real-time insights and laying the groundwork for AI-driven optimization.

Find out more about SAP Business Data Cloud. 👉https://sap.to/6059hZgjx

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Move faster with AI, without adding risk.

In this video, see how Joule helps you confidently scale AI impact with trusted SAP security and governance built in, minimizing new risk and IT overhead so you can accelerate value without compromise. Joule works with your existing SAP environment to uphold role-based access controls for every AI action and provide robust protection for sensitive data.

Joule also connects to enterprise AI governance capabilities, including tracking for custom and third-party agents, to give you clearer visibility and tighter control. And with a focus on responsible AI, Joule helps safeguard against bias and inappropriate use by aligning with global standards, keeping humans in the loop, and providing transparency into sources and reasoning.

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Discover how AI-assisted MRO inventory analysis in SAP Integrated Business Planning (SAP IBP) 2602 streamlines the validation of MRO inventory planning results, helping planners build trust in key parameters like safety stock, reorder points, and target inventory positions with less manual investigation.

In this highlight tour, you’ll see how the new analysis experience brings results, drivers, and relevant inputs into a single view, so maintenance, repair, and operations (MRO) planners can move from “fact-finding” to confident decision-making faster, keeping maintenance supply chains running efficiently.

🤖 AI-assisted validation in one view — Get a clear summary of optimized results plus MRO-specific analysis that accounts for the factors behind the planning run, explained in business-friendly language.

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🔎 Less guesswork, fewer context switches — See key inputs and settings (like service level, lead time, and part demand) in one place, reducing time-consuming digging across windows, profiles, and configurations.

Chapters:
00:00 – Scenario: validating MRO planning results
00:54 – Introducing AI-assisted MRO inventory analysis (2602)
01:07 – Launch from Planner Workspace or Excel add-in
01:22 – Summary + MRO-specific analysis explained
01:44 – One view for settings and inputs
01:58 – Wrap-up: faster, more confident decisions

• What’s new (Help Portal): https://sap.to/6050hBRom

• Learn more about SAP Integrated Business Planning: https://sap.to/6053cGTA3

In a large article The Backnanger Kreiszeitung (BKZ) reports on our introduction of e-invoicing. Online Viewer and how this helps small and medium-sized businesses. With this, everyone can read their e-invoices for free!

By the way: The magazine IT-Zoom rated our viewer as „Tool of the Year“ excellent.

Contact:
conesprit GmbH
Steffen Kienzle
+49 7191 34 55 356
steffen.kienzle@conesprit.de

The post Zeitung berichtet über unseren Online-Viewer für E-Rechnungen appeared first on SAP Business One Consulting.

Contact:
conesprit GmbH
Steffen Kienzle
+49 7191 34 55 356
steffen.kienzle@conesprit.de

The post Unser E-Rechnungsviewer ist „Tool des Jahres“! appeared first on SAP Business One Consulting.