AI in Finance Departments: Between Ambition and Reality

03.06.2025

Topic: AI and Machine Learning | AP Automation

Few topics are creating as much buzz today as Artificial Intelligence (AI). Expectations are high—even in finance departments, whether large or mid-sized. AI is expected to take over routine tasks, speed up processes, and minimize errors. Accounts payable in particular is seen as an ideal field of application. And rightly so—because AI has the potential to unleash its full transformative power when applied to repetitive, document-based processes.

But a closer look reveals a stark contrast between lofty promises and actual benefits. The hype surrounding AI carries the risk that decision-makers blindly trust the technology without understanding its limits. Those who dive headfirst into AI projects full of enthusiasm but with little technical knowledge are setting themselves up for disappointment.

Buzzwords and Black Boxes: Between Progress and False Hopes

The pace of innovation around AI is undeniably rapid. Terms like “Agentic AI,” “Generative AI,” “Large Language Models,” or “Retrieval-Augmented Generation” represent new ways to analyze and utilize data. These technologies have the power to fundamentally reshape processes in accounts payable—through smart text recognition, context-aware decision support, or dynamic workflows.

But this is also where problems begin. The excitement around new tech often leads to inflated expectations. Buzzwords create the illusion that AI effortlessly delivers results. What’s often overlooked: success depends on many factors—data quality, process understanding, and integration into existing systems.

Additionally, the term “AI” is used far too loosely. Not everything sold as AI is actually based on learning algorithms. In many cases, it’s just simple rules or traditional text recognition. For users, this isn’t easy to see—because AI typically functions as a black box. The method remains hidden; only the outcome is visible. This lack of transparency makes it difficult to make informed decisions—and can lead you down the wrong path.

AI in Invoice Processing: Big Opportunities—with the Right Know-How

Invoice processing is one area where AI truly shines. Automated data extraction, intelligent account assignment suggestions, anomaly detection—these use cases are proven and effective. Here, AI can speed up processes, reduce errors, and relieve employees from repetitive tasks—freeing them up to focus on more strategic work.

However, for AI to deliver results, the conditions need to be right. One major hurdle is data quality. Unstructured, flawed, or incomplete invoices, as well as poorly maintained master data, can seriously hamper AI performance. Even the best technology needs a solid data foundation to deliver reliable results.

And let’s be clear: AI doesn’t replace human expertise. Automated suggestions always need to be reviewed. The black-box nature of AI—opaque and hard to interpret—can be especially problematic when it comes to compliance. Only those who understand and control their processes can use automation safely and sustainably.

This means every AI-supported decision must remain transparent and traceable. Success comes not just from smart software, but from the interplay of technology, data, and domain knowledge.

SAP Expertise as the Key to Smart Automation

One critical success factor for AI in accounts payable: a solid understanding of the ERP systems in use—especially SAP. SAP environments tend to be highly customized, having evolved over many years, and are often complex. Standard solutions usually fall short here.

To use AI effectively in this context, deep SAP knowledge is essential. Only then can AI generate meaningful and practical suggestions. This includes:

• Customizing: What individual settings control the system?
• Company codes and document types: What structures define the process flow?
• Workflow logic: Which processes trigger when? How do approvals and checks work?

Only those who understand these connections can evaluate AI results properly and integrate them into existing processes. Without this knowledge, automation remains superficial—bringing risks like incorrect postings, inefficiencies, or compliance issues.

Even if AI systems promise a lot, they are no substitute for system understanding. On the contrary: the more complex the SAP landscape, the more important it is for business units and IT to work hand-in-hand. That’s the only way to ensure automation is efficient, secure, and sustainable.

Conclusion: AI—Driving a New Era in Finance

The use of AI comes with its challenges and is by no means an automatic win. Nevertheless, Artificial Intelligence is far more than just a tool for further automation. It’s the key to a fundamental transformation of accounts payable. When used properly, AI elevates this traditionally manual field to a whole new level: faster, more accurate, more intelligent. It has the potential not just to automate routines, but to rethink them entirely—transforming processes at their core, not just optimizing them.

Organizations that invest in a strong combination of technology, domain expertise, and data quality open up entirely new possibilities for their finance departments. AI doesn’t just relieve teams from monotonous tasks—it creates space for strategic thinking, data-driven decision-making, and real value creation. Instead of managing rows of numbers, finance teams can actively help shape business success.

The future of finance is digital, connected, and intelligent. Companies that have the courage to embrace AI not just as a supporting tool but as a catalyst for a new way of working will gain a decisive competitive edge—through transparent, dynamic, and future-ready processes. xSuite supports this transition by delivering the digital, intelligent solutions that define tomorrow’s finance function.