3 common problems in finance that can be solved with AI

Everyone is talking about artificial intelligence, and many CFOs are asking exactly the same question: Where are the real use cases that can improve our department? The technology may seem complex, but identifying its value does not have to be.

When the conversation turns to AI in finance, it is easy to get lost in theoretical blue-sky thinking. However, the most successful implementations always start with everyday operational challenges. If you are looking for a place to start, you should take a closer look at these three areas:

If large parts of your controls are still primarily based on spot checks, you are leaving blind spots in your processes. AI automation offers a significant improvement here, creating value without adding unnecessary administrative work.

AI’s ability to analyse data means that the technology can quickly evaluate, cross-check and enrich documents. For example, you could run an invoice and the corresponding contract through an AI engine. The technology flags discrepancies across both known pitfalls and unexpected deviations that it identifies itself. By adding metadata, AI effectively acts as an objective “second opinion”.

Setting up these automations is relatively straightforward. However, one important caveat remains: An alert only creates real value if you have defined a process for who is responsible for responding to it in day-to-day operations.

For reasons that are difficult to explain, finance functions rarely have a proper service management system like the ones commonly used in IT or HR. As a result, key employees are often overwhelmed by routine emails, and valuable time that should be spent on strategic business partnering is instead consumed by questions about unpaid invoices.

If you invest in a service management solution for finance and let AI act as the engine, you unlock significant optimisation potential:

• Triage: Enquiries are automatically routed to the appropriate specialist.

• Data collection: The system analyses the enquiry using AI, retrieves information from your ERP system and may prepare a draft response.

• Full automation: AI responds independently to routine, policy-based questions.

And what happens when an employee still needs to take over and respond manually? You can use AI to turn the manual response into a proposed update to your policies and guidelines, continuously improving the system’s coverage. The time savings can be substantial, particularly during peak periods around month-end close.

In most companies, the biggest time drains, errors and discrepancies occur in the gaps between major IT systems. Inadequate system coverage is too often patched with a maze of spreadsheets, emails, lengthy PowerPoint presentations and unstructured manual workarounds.

This is where AI coding—or indirect AI—can help you build tailored “go-between” applications. By leveraging the technology’s ability to build solutions faster than ever before, you can connect your core systems, reduce organisational noise and streamline operations. In some cases, it may even make sense to replace legacy solutions with these lightweight apps.

Just like a skilled tailor, AI allows you to create solutions that are precisely tailored to your unique business processes, instead of forcing you to adapt to an inflexible system.

This could involve specialised applications for month-end close, invoicing complex cases, cross-functional and historical project controlling, and much more. These are processes that sit between systems and across people, silos and priorities—and that can rarely be fully accommodated by a standard system.

Tip of the iceberg: These three examples only scratch the surface—especially when we begin to explore the opportunities that arise when non-financial data is included in the equation.

But where do the good AI ideas actually come from? They all stem from one central principle: Focus on the problem, not the technology.

In fact, you do not need a polished “AI strategy”. What you need is a strong problem strategy combined with the expectation that today’s technology can solve these challenges through AI. The rest is for us as consultants and subject-matter experts to prove in practice.