According to a recent report, 35% of companies are currently using some form of artificial intelligence (AI) and 42% are exploring ways to implement it in the future.

As AI becomes more powerful and accessible, companies are applying the latest technology to a variety of functional areas. This includes finance and accounting.

What does AI in accounting look like today, and what might it look like in the future? Here’s everything finance departments need to know.

Types of AI Used in Accounting

The use of artificial intelligence in accounting isn’t exactly new. However, it’s rapidly growing in application and scale. Here are a few of many types of AI that accounting teams are using:

1. Intelligent Process Automation

Intelligent process automation (IPA) uses AI to automate repetitive tasks. This type of automation is used by accounting teams to automate a variety of processes.

Examples include automating accounts receivable collections and streamlining financial planning & analysis (FP&A).

2024 Clash of the Titans

SAP, Oracle, Microsoft, and Infor each have a variety of systems that can support data-driven decision making. We surveyed customers of these four vendors to find out what their selection and implementation process was like.

2. Natural Language Processing

Natural language processing (NLP) algorithms allow computers to understand and process human language. NLP is being used in accounting to automate tasks such as extracting data from financial statements.

It’s also being used to automate invoice processing. NLP algorithms are trained to identify and extract data from invoices using patterns in the text. This means that the output will be accurate even if the invoice format isn’t consistent.

3. Machine Learning

Machine learning (ML) algorithms allow computers to learn and improve over time without being explicitly programmed. ML is being used in accounting to identify fraud and improve the accuracy of financial forecasting.

When it comes to forecasting, companies can train ML algorithms on historical data to identify patterns that can be used to predict future demand.

For example, by analyzing historical demand, inventory levels, and transportation costs, ML within a manufacturing company can identify the most efficient ways to move products from suppliers to customers.

(Learn about ai applications in manufacturing.)

4. Generative AI

Organizations across industries are exploring the potential of generative AI in financial forecasting.

This type of AI is trained on models like machine learning, but one thing that sets generative AI apart from traditional AI is its ability to create new information and imagine a variety of outcomes. In finance, this means companies can use internal and external data to prompt the system to generate variations of possible future scenarios.

For instance, a field service company might train an AI model on its historical revenue data, as well as economic and market data. The model can then generate information that helps the company make decisions about pricing, resource allocation, and other financial matters.

(Learn about generative AI in demand forecasting.)

Challenges to Understand

1. Unreliable Outputs

AI technology processes data by analyzing only what it can access. These literal interpretations don’t always incorporate semantic understanding or underlying meaning.

If a document. such as legal form, contains nuanced language, the software can’t always understand the intent. As a result, the outputs it provides can’t be trusted at face value. A human employee needs to review these outputs for accuracy and relevancy.

In short, accounting professionals, including CPAs, are still critically necessary.

2. Issues Migrating Legacy Data

While many organizations are already adopting AI technology, others are lagging behind simply because they’re perplexed by the data in their legacy systems.

One of the core challenges of adopting new technology is figuring out how to get clean, consistent data from old systems into new systems. To ensure trustworthy AI insights, companies must build strong data governance and develop a proactive data conversation plan.

3. Security and Privacy

Using AI in accounting may mean collecting personal data, which raises concerns about privacy. Companies must be transparent about how they collect and use personal data, and they must obtain consent.

Another concern is security, and this especially applies to generative AI. Companies that are using this technology within a secure ERP system need not be as concerned as companies haphazardly using ChatGPT. However, organizations should still be careful about the information they put into actual prompts.

Harness the Power of AI in Accounting

The role of AI in accounting is quickly changing. Once considered a nice-to-have function, it’s poised to become a mainstay. This technology is allowing CFOs to gain a deeper understanding of their business, which aids in every aspect of decision-making.

As you think about leveraging AI in ERP or integrating AI with your accounting software, it’s important to understand not just the benefits but the risks. Consider engaging ERP selection consultants with accounting software expert witness experience. This is the kind of team that can help you see through the ERP vendor hype.

If you’re interested in learning more about how automation technology, such as AI, can transform your finance processes, contact our enterprise software consultants below.

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