While Chat-GPT may be able to pass the CPA exam and generate incredible walls of text, true AI automation looks different (and isn’t as straightforward as a 1-line prompt).
Introduction
There is simply no escaping the fact that AI is probably the most talked about topic on the internet in 2023. Chat-GPT, the popular chat-based interface for exploring the LLM (Large Language Model) capabilities developed by OpenAI, was released to the public earlier in the year.
Play around with it for just a few minutes, and you can begin to understand why everyone and their dog is talking about this – Chat-GPT is able to demonstrate superhuman proficiency in virtually every domain. AI clearly promises to significantly transform many areas of work – while potentially impacting millions of jobs and careers.
Artificial intelligence is now being applied across professional domains that are ripe for automation – areas of work such as software, law, accounting, consulting, finance and so on. Within finance, the accounts payable function is one that comes into the spotlight as somewhat unique – especially as there seems to be an equal amount of noise on both sides of the argument, with AI advocates and naysayers both having a raging debate on what will (or won’t) happen.
The jury is still out on how exactly this rapid transformation will be achieved – and this is where most discourses on the benefits of ChatGPT in particular (and AI in general) tend to draw the line.
The need for AI in Accounts Payable
In traditional AP operations, companies often rely on manual processes, extensive paperwork, and repetitive tasks to handle their payables function. These tasks are activities like data entry, invoice processing, and financial analysis, which are crucial for decision-making, operational planning, and risk management.
However, these processes involve spending time (and money). The major drawbacks are:
- Manual data entry introduces a high potential for errors, as humans can make mistakes when entering numbers or dealing with large volumes of data.
- It is also time-consuming, requiring significant man-hours to reconcile accounts, generate reports, and perform financial analysis.
This leads to delays in vendor payments, inadequate expense planning, and difficulties in maintaining financial integrity.
Put AI aside for one moment – the reality is that any sort of automation can help significantly in addressing these issues. Even the humble OCR – that has been around for decades – reduces the time taken to process an invoice by at least 60%, saving AP teams multiple days every month. And yet adoption of this technology is still not widespread.
Technologies such as machine learning and natural language processing have the ability to revolutionize the AP function in a much deeper way – provided they’re implemented and integrated in the correct manner.
Looking to integrate AI into your AP function? Book a 30-min live demo to see how Nanonets can help your team implement end-to-end AP automation.
Potential use-cases for AI within the Accounts Payable process
So how exactly are you supposed to integrate AI into your AP process? Where do you start? What’s the easiest way to 10x your productivity? We give a few examples below, with clear, concrete steps that you can take.
1. Invoice coding and General Ledger (GL) account mapping
Perhaps one of the most difficult tasks to automate is assigning invoices and receipts to the right category and GL code within your accounting system. Why is this particularly tricky?
- There are often multiple GL codes that apply to the same expense, split by line items/individual product codes. Assignment of these GL codes is usually manual, and must be done in consultation with business teams and the CFO.
- Assigning a GL code to an invoice is sometimes subjective – for example, while regular sales invoices might always be assigned to “Sales” in your chart of accounts, sometimes the exact same invoice format ends up being used for contractors and non-employees. This can lead to contractual expenses being incorrectly tagged as “Sales” by basic automation tools.
How can AI help here, in a practical way?
- Automate invoice coding based on LLM processing – here, the AI basically tells you which GL this invoice should be categorized in, and this can be configured to offer multiple suggestions that can be appropriate. This makes the user’s task somewhat easier.
- Learn and memorize user inputs – once a user actually selects the GL code, the system can remember the selection and automate it the next time for the same vendor.
2. Fraud detection and error handling
Another crucial task that an AP team has is catching errors before they happen. It might be as serious as wrong payment details and invoice fraud, or it might be as simple as a duplicate invoice.
Without a doubt, these problems are best prevented before they happen. Most organizations INSIST on making this process manual. However, having a human check each invoice makes things difficult because:
- It gives a single point of failure (and bottleneck) for the process
- It ensures that only the person with the most context on vendor payments (CFO/AP head) can make corrections, and no one else
How can AI help here, in a practical way?
- AI that detects duplicates/wrong information – Basic file duplicate checks verify only if the two files are the same. With advanced AI duplicate checks, you can go one step further – checking if the contents of two different files are suspiciously similar.
- Multiple data validations on invoice data – Just auto-reading the invoice data is no use if someone has to login and verify it anyway. Advanced AI tools can now carry out data validation to ensure hygiene checks (for example, if a new bank account number on an invoice doesn’t match the usual one for a vendor, you’ll get notified!)
3. Learning the company’s AP workflow
Ask anyone what they REALLY want AI to do, and this is the answer that comes out on top – many people feel that the real value of AI is when it can learn their patterns and save time for them.
How can AI help here, in a practical way?
The first step is identifying the steps in the AP process that are ideally suited to iterated re-learning (i.e., activities which you keep doing daily, that can eventually be memorized by the AI and automated 90% of the time).
Good examples of this are:
- GL code assignment – The logic here is simple: if the AI assigns the right GL code to an invoice, great! If not, you change it yourself, and the AI remembers this change for next time. As a result, the AI keeps getting better with every click you make.
- Category/Class/Project classification – If a particular vendor invoice can’t be auto-classified into the right category, the next-best thing that an AI tool can do is learn what you teach it.
How Nanonets can help you implement AI in your Accounts Payable Process
The examples above are probably just the tip of the iceberg – there is a lot more than AI can do for your AP process that is only limited by how deep you are able to go into the process of automation and machine learning.
Fortunately, today you do not have to be technically savvy in order to begin implementing AI capabilities into your AP process – there are tools that allow you to get started almost immediately.
For instance, Nanonets has an AI platform called Flow that can transform your current AP process, and add those vital AI elements to your workflow. It can do all that has been demonstrated above – and much, much more.
Simple to implement yet complex in its capabilities, this is the ideal starting point for those looking to really step up their AP process and scale their workload more efficiently. Get in touch today for a free demonstration of what this AI platform can do for your AP function.
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- Source: https://nanonets.com/blog/how-ai-will-transform-accounts-payable/
