4 Types of Artificial Intelligence and How it Can Support your Accounting Department!

It is no secret that artificial intelligence (also known as AI) is a hot topic these days — and it’s here to stay. While it may be tempting to think of AI as futuristic robots taking over our lives, you are already using AI in your everyday life in small and significant ways, even if you don’t yet realize it. For example, if your email service has started offering predictive text as you type, that’s AI at work. And if AI is helping you tackle the mundane task of drafting your emails, what other ways might it be able to benefit you and your business?

When it comes to your accounting department, it may already be doing more than you think. Most businesses, no matter how large or small, have their accounting software tied into their bank feed. Artificial intelligence is what manipulates the data between the two interfaces to turn it into a form that your accounting software, such as QuickBooks, can recognize, and once the data is in that software, artificial intelligence is what auto-tags things as “office supplies” versus “fuel” versus “utilities” and so on, helping to categorize for line-item budgets.

At its core, AI allows you and your team to focus your energy on higher-level activities. AI tackles the mundane, repetitive tasks so that you can spend your time else where to better your business. Embracing artificial intelligence can lead to increased efficiency, improved accuracy, and more time and energy to spend on the high-level decision-making necessary to run the best business that you can.

The Four Types of Artificial Intelligence

The term artificial intelligence encompasses more than just predictive text and data entry manipulation. Currently, AI is classified into four main types:

Reactive AI – This type of AI is the most prevalent in today’s technology but is also the most limited. Reactive AI has no ability to reference historical context to inform itself on its outputs, has no memory, and can only work on a perfect dataset. While it’s extremely limited in its capabilities, there are many uses for reactive AI. Common examples include:

● Macros in office productivity software

● Automation rules in accounting software

● What-if scenarios and formulae

● Ability to use import templates to import large datasets at one time

Limited Memory AI – This is the most advanced AI currently available. It can do more complex tasks because it can use historical data to predict future outcomes. It is still limited in that if outliers or challenges occur, the AI won’t be able to produce a desired outcome; the slightest change to the AI’s environment would cause the entire model to fail. Common examples include:

● Self-driving cars

● Chatbots

● Virtual assistants

● Natural language processing

Theory of Mind AI – This is also known as Emotion AI and is currently under development. It aims to be able to understand and analyze human emotional states, but it’s not particularly close to being developed at any level yet.

Self-Aware AI – This would be AI that is aware of not only the state of others, but also of itself. Those sentient robots we all imagine would be this type of AI, but the technology is so far off into the future that we do not even know for sure yet if it’s actually possible to create AI with these abilities.

These four types of AI help break down how much technology this sphere of AI encompasses, but for the rest of this article, we’ll be focusing on the first two types — Reactive AI and Limited Memory AI — and how they can help your business run more efficiently.

Common Applications of AI to Consider for your Business

There is almost no end to the ways in which AI can be used to streamline your workflows and impact your productivity in every area of your business. Since we’re a business all about accounting and finance, we’ll focus in on the applications that make the most sense in that arena.

Here are some of the ways in which AI can be incorporated into your business to help your accounting and finance team:

‍Automated data entry

● AI-powered software can automate data entry, saving time and reducing the risk of human errors. For instance, tools like Optical Character Recognition (OCR) and Natural Language Processing can extract information from invoices and receipts, populating financial systems seamlessly. Another example is when you convert files between a PDF format and a word processor(like Microsoft Word) or spreadsheet (Microsoft Excel) format. This helps reduce errors in data entry and save time, giving you and your team a chance to focus on the bigger picture or more complicated tasks.

Expense management

● Automated approval workflows for accounts payable can remove the paper trails and bottlenecks around physical signatures, moving payments through your system faster. There are numerous entire A/P systems dedicated to this, with cost and functionality ranging from simple automation to highly complex and customizable. At their core, all these systems are designed to reduce the control risk around your A/P function by not letting cash leave the business until the bills have been carefully reviewed and approved, all without you needing to push things along manually. This is exponentially valuable in a company with a decentralized workforce where people aren’t in the same location and struggle to get actual signatures in real life.

● Within the more complex A/P systems, there are tools that can analyze expenses, identify discrepancies, and even suggest cost-cutting measures. Example: If you have an invoice that is more than $2,000, it'll require an additional approval step; there is no way to force the payment without it. The software sends a notification to the manager, and with a click of a button, it could be approved and processed automatically. This can save a spread-out workforce an enormous amount of time while maintaining the proper approval protocols to ensure compliance and accountability.

Automation rules in your accounting software

● These are “what-if” scenarios that can be set up to code activity automatically. An example of this would be having rules set up to code a bank transaction to a particular GL account if the description has a specific keyword in it. In non-accounting applications, you’ve seen this in the form of Amazon or Netflix providing suggestions that are based on your viewing history, ratings, purchase history, etc. By applying “what-if” scenarios, your software is essentially able to say that if they previously spent money at Office Depot and tagged it as “Office Expense,” then anytime they spend money at Office Depot, we can safely assume it’s an office expense and tag it accordingly. So, if you liked Lord of the Rings: Return of the King (2003), odds are you are going to watch and enjoy The Hobbit: Desolation of Smaug (2013). Or not — but take it up with Peter Jackson, not the AI suggesting.

Data manipulation & integrations

■ Macros can be set up to automate data manipulation when working between systems. A common example is when you need to take a trial balance dataset out of one accounting software and import it into another accounting software. You can set up a mapping template that will take the data from one software and convert it into the format necessary for the other while putting it into an importable template. This cuts out significant manual human processing time, especially if it is a task that accounting is going to do repeatedly —Every pay period? Every month for board packets? Every quarter for investor statements? There are so many applications available here.

Example: You have your ultimate accounting system and an operational system in your store that you love. But they don’t seamlessly integrate. If the in-store system provides reports, you can set up a functionality or workflow to manipulate those reports into the format that you need for your accounting system. Even if you must pull a report manually, it’s still saving you time.

Predictive analytics

● Algorithms can be used to analyze historical data to make predictions about items in the future. In this form, AI uses machine learning algorithms such as linear regression, correlation, and standard deviation, as well as initial manual inputs, to create informed decisions and predictions. When using AI in forms such as this, it is important to realize that data biases can be introduced, thus skewing the output.

example: If a model is fed data that says that all men named John order a burger and fries at a restaurant, but all men named Tim order a burger and side salad, it’s going to assume that someone named Tim never orders fries, which is clearly not the case in the world at large. The data sets used need to be as large and as accurate as possible to provide reliably accurate outputs.

Risk assessment & fraud detection

● Risk assessment and fraud detection are commonly used in the banking industry, where AI can detect unusual patterns in activity and catch fraudulent activity in real time.

Customer service

● Chatbots and virtual assistants can provide varying levels of efficient customer service and resolve problems around the clock when employees may not be available. Chatbots can provide that first-level assistance for customer service and can often times resolve issues without even needing human intervention. These can be set up to escalate issues for real people to make higher-level decisions.

Regulatory compliance

● AI can analyze data and transactions, generate compliance-related reports, and identify and alert you to potential compliance issues.

● This can be a significant change for non-profits, helping them to fill out their required compliance forms like the yearly 990, alerting you if there’s missing or incomplete information.

Tax preparation

● Like the automated data entry and automation rules discussed above, AI can take it a step further and categorize transactions, prepare tax returns, calculate liabilities, and generate tax-related reports automatically, saving your accounting team time and energy during what might be their busiest season of the year.

These are just some of the ways that integrated AI is changing the landscape of large and small businesses by reducing the manual labor once necessary in an accounting department. Implementing even one or two of these AI uses into your own business can drastically reduce the time spent on data entry and transaction categorization by your team and help to streamline accounting processes no matter what type of a business you run.

AI for AI’s sake is never the right answer. As you look to the future that includes AI and folding it into your workflows, remember that AI is meant to support your business and its mission, not the other way around.

Interested in discussing how AI implementations could support your growing business? Our experts at Accountinuity can help you sort out what systems could be advantageous to you as you look toward the future. Book a meeting with us here, or email us at hello@accountinuity.com. Spend an hour with our team of experts. No charge and no obligation.