The Role of AI in Financial Bookkeeping Part 1/2: Abilities and Limitations

Introduction

Artificial Intelligence (AI) is transforming industries across the board, and financial bookkeeping is no exception. From automating repetitive tasks to detecting anomalies in financial data, AI is reshaping how businesses manage their finances. However, while AI offers significant advantages, it is not without limitations.

Ultimately, humans will need to learn how to use AI as a tool, and that means knowing how to supplement your own work with that of AI. In that spirit, this blog post will be a mixture of message and method. I have used an AI program to help write this blog post. Where you see black text, the AI is responding to my prompts. Where you see text in blue, I am personally writing to provide my thoughts on the AI responses. I hope that you will find this as informative and fun as I have. 

In this blog post, we will explore:

• The current abilities of AI in bookkeeping

• The limitations of AI in financial management

• How AI has changed bookkeeping practices in the past

• How bookkeepers can adapt to an AI-driven industry

• Whether small businesses will still need human bookkeepers in the future

By the end, you’ll have a clear understanding of how AI is revolutionizing bookkeeping and what it means for businesses and professionals in the field.

Also, this blog ended up being longer than usual, so I split it into two parts. Part 2 will be released later this month. 

Current Abilities of AI in Bookkeeping

AI has made significant strides in automating and enhancing bookkeeping tasks. Here are some of its key capabilities:

a) Automated Data Entry & Invoice Processing

AI-powered tools like QuickBooks can extract data from receipts, invoices, and bank statements with high accuracy. Optical Character Recognition (OCR) technology allows AI to read handwritten or scanned documents, reducing manual data entry errors.

Example: A small business owner can snap a photo of a receipt, and AI software will automatically categorize the expense and log it into the accounting system. 

I’ve used this tool in QuickBooks before, and I have to dispute the “high accuracy” claim. In my experience AI is often very bad at reading information from a document. The main reason for this is a lack of standardization. Not all documents are structured the same way. AI may be able to read numbers, but often struggles to differentiate between a total and a sub-total, for instance. Or between a date of purchase and a date of delivery. Unless there is a substantial standardization of formatting in everything from invoices to receipts, I don’t see this flaw going away any time soon. 

More to the point, this kind of technology is cool in theory, but not very practical in most cases. When was the last time your business got a handwritten note that had to be entered into your QuickBooks? And how often does that happen? The truth is almost all documents are already delivered digitally, which makes it very easy to copy and paste information into a system like QuickBooks.

Across the board digitization is typically more practical than using AI to integrate physical documents into a digital system. Even if this OCR technology improves significantly, it won’t save much time in the context of bookkeeping.  

b) Real-Time Transaction Categorization

AI algorithms can classify transactions based on historical data, learning from past entries to improve accuracy over time.

Example: If a business frequently pays for "Office Supplies" from a particular vendor, AI will automatically assign future transactions from that vendor to the same category.

While this is certainly true, it is also pretty easy to just set a rule manually to automatically categorize transactions. AI isn’t really needed. 

c) Fraud Detection & Anomaly Identification

Machine learning models can detect unusual spending patterns or duplicate payments by analyzing large datasets in real time.

Example: If an employee submits an expense claim that deviates significantly from typical spending behavior, AI can flag it for review. 

For review by a human, to be clear. Bookkeeping software typically can’t be used to stop payments. Even if you trusted AI to be 100% correct in detecting fraud (which it will never be) it is powerless to use that information. A person still needs to monitor the books and then reach out to freeze payments until accounts can be re-secured.   

d) Predictive Cash Flow Analysis

AI can forecast future cash flow based on historical trends, helping businesses make informed financial decisions.

Example: An AI tool might predict a cash shortfall in three months based on seasonal sales patterns, prompting the business to adjust spending.

However, AI may not have access to important context. The AI itself admits this a little later on, so I won’t harp on it here, but I want to give one example. 

Imagine that it is May of 2020, and you’ve been using an AI powered tool to project cash flow. Based on previous years data, it recognizes that May through July are your best sales months of the year. Therefore it projects that you will have more than enough cash flow for the next few months, and you have no reasons to worry about liquidity. Now think back to that time. Was there anything else going on that particular year? Maybe some reason why many businesses would be experiencing far fewer sales than normal and would be struggling with cash flow? 

If you were there, you know that the AI would be missing some important context. Granted this is an extreme example, but it does show that no AI tool has all the information that a human needs to make informed decisions. Even the best tools can only work within the limited scope for which they were designed.    

e) Integration with Banking & Financial Systems

AI-powered bookkeeping software syncs seamlessly with bank accounts, credit cards, and payment processors, ensuring up-to-date financial records.

I want to take this opportunity to discuss a rhetorical trick that has been used a lot recently. The conversation around AI at the moment is energetic, and any mention of it in some new industry or product is sure to draw attention. To that end, many companies have started inserting “AI” into the names of their products and services. But in some cases that is really just a buzz word, and the “AI” isn’t actually a different product. 

The above is a good example of what I’m talking about. QuickBooks has had the ability to import data from bank accounts and credit cards for years. This was not previously referred to as an AI powered tool. And why would it be? The system is connecting to a bank system, requesting specific information about transactions, and then importing that data into QuickBooks. That is a helpful process, but does that really sound like AI?

Not to get into a semantics argument about what exactly AI is, but to me that doesn’t sound like an industry shaking intelligence innovation. It sounds like a time saving tool. It’s great technology, but not all technology is AI. Next time you hear about something being “AI-powered”, it is worth taking a moment to consider if what is occurring is a technological innovation, or a re-branding strategy. 

Limitations of AI in Bookkeeping

Despite its advancements, AI still has several limitations:

a) Lack of Contextual Understanding

AI can misinterpret transactions if they lack clear context. Human judgment is often needed to classify ambiguous expenses correctly.

Example: A payment labeled "Amazon" could be for office supplies, software subscriptions, or personal purchases—AI may not always distinguish correctly.

I like the use of this example, because I literally have seen this mistake hundreds of times. That is why I always like to review transactions personally, rather than have them categorized automatically. 

b) Errors in Unstructured Data

While AI excels at processing structured data (like invoices), it struggles with unstructured data (such as handwritten notes or complex legal documents).

This calls back to my point about OCR. 

c) Dependence on High-Quality Data

AI models require clean, well-labeled data to function effectively. Poor data quality leads to errors that can compound over time.

d) Ethical & Compliance Risks

AI does not inherently understand regulatory changes (e.g., tax law updates) unless explicitly programmed to do so. Human oversight is necessary to ensure compliance.

e) No Substitute for Strategic Decision-Making

While AI can provide insights, it cannot replace the strategic thinking of a human bookkeeper when it comes to financial planning or tax optimization.

Admittedly, a human bookkeeper also wouldn’t typically be responsible for financial planning or tax optimization. But I do have some trusted contacts for those kinds of services that I am happy to refer to.

Well, that was a lot huh? I hope this has been a helpful primer on the subject of AI in bookkeeping.

Check back in later this month for part 2 of this blog. We’ll be discussing how AI has changed bookkeeping in the past, how bookkeepers may adjust to AI in the future, and the role of human bookkeepers for small businesses going forward.

See you then!