Ask 100 experts what artificial intelligence is and you’ll get a variety of answers. What it’s not is a robot standing by the photocopier in each office.
We’re not at the point where we have the likes of 2001’s HAL or Forbidden Planet’s Robbie the Robot in our midst.
Today, AI is being used for image recognition, object identification, detection, classification and automated geophysical feature detection. These are underlying tasks that once required the input of a human.
Focusing on how artificial intelligence will impact accountants, AI will very soon help you to automate much of the routine and repetitive activities that are undertaken on a daily, weekly or annual basis.
It will also help you to:
- empower quick decision-making
- create smart insights
- examine huge quantities of data with ease.
With this in mind, here are five examples of how AI could be useful for your accountancy firm over the coming years.
Predictive and forecasting solutions
Helping your clients forecast the finances of their business is an extremely valuable element offered by your practice.
With AI integrated into the software, you will be able to provide a comprehensive and accurate insight for your clients without the usual “manual heavy lifting” and number crunching behind report creation.
On a day-to-day basis, being able to quickly and easily access up-to-date and accurate reports and forecasts can help you form a closer and more useful relationship with your clients.
This revolution will be empowered by one of the cornerstones of AI today: machine learning.
This is the ability of software to essentially program itself based on the data it encounters. The software is able to learn from what you do with data and can make its own suggestions for humans, if not act entirely autonomously.
Machine learning is everywhere. It allows mobile phones to enhance predictive text, use speech recognition, create route suggestions when navigating, and suggest places you might want to visit when you reach your destination.
At companies worldwide, 77% of businesses already say they’re completely or very reliant on machine learning technologies. Other research says the top use for machine learning (45% of respondents) is more extensive data analysis and insights.What machine learning needs—and what simultaneously makes it so useful—is access to data. Lots of data.
This is why machine learning is coming to the fore now, because technology such as cloud computing means all the data can be collated and is accessible, rather than being hived off within discrete systems that aren’t interconnected.
Additionally, cloud computing simply means we’re able to generate more useful data.
2. Smart assistants
Are you an accountant who, during crunch time when seemingly every client is sending through their accounts, considers turning off your phone or email so you can get some work done? You’re probably not alone.
Fortunately, smart assistants might be able to give you a helping hand.
They can form the first line of customer contact and might even be able to provide clients with the information they need, such as details about their current tax liability.
You might already know about smart assistants because you interact with Apple’s Siri or Google Assistant on your phone, or Amazon’s Alexa in your home.
There’s even a smart assistant that can be used for client accounting—users can just ask how much money they have in their payments account, and it will tell them. People don’t need to understand accounting terminology, or even what a ledger is.
In fact, smart assistants come in two forms: natural language bots and scripted bots.
Scripted bots are the ones that have been around for a long time—they’re easier to build and mostly used for mobile engagement strategies, so you might encounter them on a website.
They look out for key phrases and aim to provide a ready-made response. Sometimes these are called chatbots.
Natural language bots are referred to as smart assistants and this hints at how they’re more sophisticated.
They often involve speech recognition and accurate human voice synthesis, so they can respond to natural language queries. More than this, however, smart assistants learn the more you use them.
Both smart assistants (natural language bots) and scripted bots have their uses and it shouldn’t be seen that one is necessarily better than the other from a business perspective.
3. Automatic tagging and allocation of transactions
The next two areas where AI will help your accountancy practice are also enabled by machine learning.
This will save you time by correctly tagging transactions and assigning them to the right ledger account.
Put simply, your accounting software will learn from previous tagging decisions that are typically made according to rules that the accountant is aware of.
Some of these rules are intuitive but others can be surprisingly complex, at least from a computer’s point of view.
Over the coming years, the ability of technology to discover these rules and predictively plan will help to remove a significant component of your daily workload.
4. Anomaly detection
Computers love data, of course, and when machine learning is applied to massive amounts of data—such as the yearly ledgers of a large company—then there are clear benefits.
You will be able to discover anomalies that may exist, and the process will be much quicker and take significantly less effort.
If an audit is required, for example, it will be possible to audit all the data rather than merely a sample, yet without the huge resources typically required for what’s traditionally considered a “full” audit.
5. OCR solutions
Optical character recognition (OCR) isn’t new but AI enhances its accuracy significantly and opens it to new usage scenarios.
While it’s always been possible to extract information automatically from documents, this required a human to point out to the OCR software where the data was located—something that also meant the document layout couldn’t be altered without further instruction.
Computers have always known what numbers are, of course. That’s what defines a computer.
A printed receipt for a purchase is full of numbers but they’re certainly not all equal. Some are of particular importance to you as an accountant: the date, the total amount and perhaps the credit card number used to make the purchase.
A human can instantly identify all of these without even thinking but until now, all those numbers were indistinguishable for a computer. The digits 1-5-1-2 might be the last four digits of the card number, or it might be a date, or it might be the amount of one of the items on the receipt.
With the application of AI to OCR, the OCR software is able to recognise document types and things such as receipts, invoices or other printed financial documents.
This means the salient data can be extracted to allow information to be allocated and/or processed by the software rather than by human action—even if the software hasn’t seen a receipt like that before, or if the scanned document isn’t particularly high quality.
This reduces the human effort and time needed to allocate and assign information.
So what next for AI and accountants?
How will we get all these wonderful AI-enabled benefits? Well, the good news is you probably won’t have to do very much.
While in the past you might have expected to buy an add-on software package to gain revolutionary new functionality, the kind of tools discussed above will more than likely start to appear over the coming years in the software you’re using now.
Some of it, such as bank account reconciliation, might already be present in your firm’s accounting and client management software—and you might not even be aware.
All you might have noticed is that things just got a little bit easier when the software appeared to get that little bit smarter.
This is the shape the revolution will take—many small increments, rather than an overnight change.
However, there’s always a “but” in stories such as this and here it boils down to this: artificial intelligence is part of a wider revolution in technology that’s enabled by the cloud.
Cloud computing is the only way to collate and make massive amounts of data freely available that machine learning needs.
We’ve highlighted how machine learning might become the best auditor in the world and spot errors humans struggle to see. Well, it can only do this if all the data is accessible.
If the data it requires is spread over 100 spreadsheets or, even worse, printed documents, then it simply isn’t possible if the AI can’t access the files.
In other words, artificial intelligence builds on technologies such as cloud technology and the message is simple: if you want AI then you need to ensure you’ve fully embraced the cloud revolution.
But don’t worry, it doesn’t mean you need to be an expert on cloud computing and AI.
It simply means being aware of what’s common-sense good practice as far as technology goes and ensuring solutions such as cloud computing are adopted within your practice.
This gives you all the benefits available today while ensuring you’re prepared for the future.
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