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AI is wonderful for automating manual work, but automating business processes is not just as straightforward as a 1-line prompt. Tools like ChatGPT are incredible at answering your questions. However, true automation looks different. Let's dive in:


There is no escaping that AI will be 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 can demonstrate superhuman proficiency in virtually every domain. AI promises to significantly transform many work areas, potentially impacting millions of jobs and careers.

Artificial intelligence is now being applied across professional domains ripe for automation - work areas such as software, law, accounting, consulting, etc. Within accounting, the accounting function 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 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 Accounting

In traditional accounting operations, companies often rely on manual processes, extensive paperwork, and repetitive tasks to handle their accounting function. These tasks include data entry, invoice processing, and financial analysis, which are crucial for decision-making, operational planning, and risk management.

However, these manual processes come with significant drawbacks:

Potential for Errors: Manual data entry introduces a high potential for errors, as humans can make mistakes when entering data in high volumes. Fields like invoice numbers, dates, and dollar amounts are particularly susceptible to errors, which can have significant consequences for accuracy and compliance.

Time-Consuming: Manual accounting work is time-consuming, requiring long hours to reconcile accounts, generate reports, and perform financial analysis. This can lead to delays in financial reporting and decision-making.

Synchronous Communication: It is heavy on synchronous communication. Have you encountered situations like the ones below?

a. Approvals don't happen until you get the CFO and Business Head on a call

b. Line items don't get resolved until the AP function schedules a meeting

All of this leads to delays in vendor payments, inefficient spend management, inadequate expense planning, challenges in netting multilateral payments, and difficulties in maintaining financial integrity.

AI for Accounting doesn't have to mean a complete overhaul

The problems listed above are well-documented - and when asked, most accounting teams will agree that introducing AI for financial automation will definitely help them out. Technologies such as machine learning and natural language processing can revolutionize accounting best practices the AP function in a very deep way - provided they’re implemented and integrated correctly.

However, this usually leads many to the conclusion that AI-based automation is not for them - it seems cumbersome, time-consuming, and expensive to implement.

The reality, though, could not be more different - today, it is possible to get started with using AI for your AP process within minutes. You can achieve this without compromising on your current process's reliability, security, and efficiency.

Put generative AI and LLMs aside for one moment - the reality is that even entry-level AI automation can help significantly in addressing these issues. Even the humble OCR - which 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.

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 Accounting process

So how exactly are you supposed to integrate AI into your Accounting process? Where do you start?

The first place to begin is to look at which part of the process really takes up most of the time. Typical bottlenecks that are reported by AP teams are activities like:

  1. Invoice coding
  2. General Ledger (GL) mapping
  3. Payment Details Verification (to check for fraud)
  4. Duplicate Detection
  5. Accounts Payable Reports

There is a very clear underlying theme here - manual data entry and verification are what causes these tasks to be tedious and time-consuming.

Automation Trends 2022 Report: The Speed of Change

This survey graphic above (from the Automation Trends 2022 report) reveals a lot - almost 70% of people have still not automated the most pressing issues in their AP process. The tasks listed above are all manual - someone needs to look at the actual data on the invoice and confirm that it is correct before proceeding further.

Automating these tasks might feel overwhelming since you're now trusting a machine to have the same level of discretion as a (trained) human.

The good news? AI can be trained equally well too! We go deeper into some use cases of this below.

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?

  1. Multiple GL codes often 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.
  2. 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?

Automated invoice coding based on LLM processing
  1. 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.
  2. 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 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:

  1. It gives a single point of failure (and bottleneck) for the process - while it is good to have an employee check every expense for errors, sometimes things can slip through the cracks.
  2. It ensures that only the person with the most context on vendor payments (CFO/AP head) can make corrections and no one else. All the knowledge and context is only with a few people and not spread across the organization.

How can AI help here?

Catching duplicates and wrong data is a piece of cake

  1. Smarter duplicate detection/wrong information - Basic file duplicate checks only verify if the two files are identical. With advanced AI duplicate checks, you can go one step further - checking if the contents of two different files are suspiciously similar.
  2. Multiple data validations on invoice data - Just auto-reading the invoice data is no use if someone has to log in 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 simple actions that are repeatable

Ask anyone what they REALLY want AI to do, and this is the answer that comes out on top - many people feel that AI's real value is learning patterns and saving time for them.

For example, there are many small tasks that are done exactly the same way for multiple types of invoices/receipts. Some examples:

  1. Assigning an invoice to the right category/class/project in your ERP
  2. Changing the GL mapping for one specific line item of an invoice
  3. Sending a particular vendor's invoice for approval to the same person every time

How can AI help here?

The first step is identifying the steps in the AP process that are ideally suited to iterated re-learning (i.e., activities that you keep doing daily that can eventually be memorized by the AI and automated 90% of the time).

Continuous learning will ensure your line items are automatically sent to the right GL

Good examples of this are:

  1. GL code assignment - The logic here is simple: if the application 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 automatic GL code assignment keeps getting better with every click you make.
  2. Category/Class/Project classification - If a particular vendor invoice can’t be auto-classified into the right category, AI can learn patterns in your selection (for instance, are you always classifying Uber receipts as "Project Costs" instead of "Travel"?). Over time, this becomes a rule-set within your platform, and is automatically applied.

Looking to add AI to your Accounting process? Book a 30-min live demo to see how Nanonets can help your team implement end-to-end AP automation.

How Nanonets can help you implement AI in your Accounting 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.