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Should Accountants Be Worried About Artificial Intelligence?

DePaul University - Artificial Intelligence article

Financial disciplines have long been the first thought-of areas where technology like artificial intelligence will cause dramatic disruption and upend entire ways of thinking. Accountancy is certainly no different from others in the finance umbrella, and we continue to see headlines explaining Why Artificial Intelligence is the Future of Accounting. 

In many ways, the AI revolution is already here. We have AI assistants like Alexa and Siri in our homes (and in our pockets). We have user-friendly and accessible AI-powered analytics tools like IBM Watson Analytics, a platform backed by the same AI that created one of the most culturally defining moments of modern artificial intelligence when it defeated the two best human Jeopardy! players in the world. 

Major firms—including Deloitte, EY, PwC, and many others—are making considerable investments in AI for streamlining auditing and tax processes. Some of the more dramatic headlines have gone as far as to claim that AI will soon be taking accounting jobs.

And yet, demand for accountants is set to grow faster than average through 2026.  

What happened to all the disruption we’ve heard so much about?

>>Future-Proof Your Accounting Career with DePaul’s Online MSA

What is artificial intelligence? 

Before we get into the future of accounting and AI, let’s cover some of the things that are going on in the present. AI is used to describe a number of different technologies that, together, mimic traits of human intelligence, like the ability to understand language and to learn.

There are a handful of technologies that you will certainly see listed if you start looking into AI and will see more of as AI becomes a more steadfast part of the accountancy profession:

  • Natural language processing: Natural language processing (NLP) technology allows computers to understand and classify elements of human language. It’s the backbone of virtual voice assistant software like Siri or Alexa.
  • Machine learning: Machine learning describes a category of algorithms that are not programmed to do a specific set of tasks; instead, they are programmed to be able to parse data and learn from it over time, usually to achieve a goal. The philosophy behind machine learning algorithms is to learn similarly to the way humans do—through experience and observation.
  • Cognitive computing: Cognitive computing is an umbrella term that simply means getting computers to think more like humans. Numerous technologies fall under the cognitive computing umbrella, including NLP and machine learning.

Why should accountants care?

One of the clearest areas of accountancy to see AI in action is with auditing; firms like Deloitte, KPMG, and others are already using AI to streamline audit processes. An important lesson from the way these firms are using AI, however, is that the technology isn’t replacing auditors. Instead, AI helps auditors review far more information in a shorter amount of time, leaving more time to make recommendations and craft insightful reports.

Tasks that involve processing and extracting immense volumes of data are key areas for AI. For example, firms using NLP technology in conjunction with other AI and data extraction tools can parse information from contracts in minutes when it would take humans hours. More advanced NLP algorithms can also extract unstructured data like the emotional sentiment of an email or chat conversation, which may help in pinpointing complex issues like fraud or collusion with competitors. 

However, there are still two major areas where AI struggles, and these two encompass some of the most important parts of accountancy:

  • Responding to novel situations; and
  • Extracting insight from data and determining next steps.

If we go back to IBM Watson and its historic win at Jeopardy!, it’s easy to see these two elements come into play. Although Jeopardy! is thought of as more unstructured than a game like checkers or chess (games that AI has also beaten humans in), once an AI can understand the syntax of clues and the need to answer in the form of a question, the game becomes a data-extraction problem. This is why IBM’s platform beat its human opponents when it came to naming specific Beatles’ songs, literary criminals, and moments in Olympics history while struggling with clues related to texts like Harry Potter. 

This isn’t to say that getting technology to understand language and syntax structures isn’t impressive. However, the instances where technology continues to struggle hold clues to what the future of accounting with AI looks like.

In DePaul’s online Master of Science in Accountancy program, we often emphasize the critical importance of understanding both accounting practice and theory, and AI is actually the perfect example of why this will be so important moving forward—because it is true that in some tasks, AI will outperform humans every single time.

Accountants will always need to be well-versed in the specific methodologies that go into conducting their tasks, but the accountants that excel in an AI-powered world will be those who can take those practices and apply them optimally to new and different situations. Furthermore, they will be able to use accounting practices to inform business decisions.

If accountancy were just a series of tasks that were conducted exactly the same way to the exact same type of data every single time, then technology far less advanced than AI would have already threatened the profession. The reality, however, is that the data we have is often messy and we continue to encounter new situations that demand human insight.

The future of accounting and AI

The question to ask about the accounting profession’s future isn’t whether technologies like AI will replace accountants. Instead, it’s important to think about how accountants can use AI to be more effective (this question works with almost any supposed profession-ending technology). Below are the top three trends I’m watching and how they’ll be useful for accountants.

1. Self-service AI: Complex software that targets business users has a unique challenge in that it has to make highly complicated functionality accessible and easily usable. Self-service has been a key priority in countless areas of business technology, from cloud computing to business analytics and intelligence.

Some AI platforms have already followed this path. IBM’s Watson, for example, now has an array of self-service portals that offer functionality ranging from business analytics with Watson Analytics to coming up with new food recipes with Chef Watson.

However, this trend will go significantly further with self-service machine learning. These algorithms normally require a lot of complex math and data science knowledge to build and optimize. However, some of the leaders in AI are working toward making it easier to train machine learning algorithms. For instance, Google has a tool that lets users train an image-recognition AI by dragging and dropping.

For accountants, training their own AI would have a lot of far-reaching implications, but one of the more practical and near-term ones will be in auditing. They will be able to take higher-quality and larger audit samples, for example, because they would be able to train a machine learning algorithm to recognize what types of data are important to them and drastically reduce the time it takes to compile relevant data for an audit.

2. Turnkey AI modules: Although machine learning is impressive, not everyone needs their own machine learning algorithm to do their jobs effectively—and it’s not the best solution for every problem. Smaller businesses, accountants who run their own practices, and organizations that don’t have a plethora of historical data to train AI on will often find it more beneficial to leverage out-of-the-box software with AI built into it. 

While you can already find AI embedded in popular accounting software, including QuickBooks, it is likely that AI will become a more user-facing feature in our accounting tools. We will see purpose-built AI modules that are designed to help with a specific task or series of accounting tasks. 

Predictive functionality will likely be one of the first true innovations to watch out for in this area. This means that AI will be able to evaluate the accuracy of sales and revenue forecasts, flag when a company is likely to miss a payment, and make its own prediction about cash flow using an abundance of data that used to be disparate and time-consuming to bring together. 

3. Accounting, AI, and automation: A discussion about AI would be incomplete without exploring the role of automation. AI will make it possible to automate tasks where it was impossible to do so before, but these tasks will still be heavily data-centric and repetitive in nature.

One of the key advantages of technologies like NLP is the ability to bring together unstructured and structured data. For example, imagine you have a mixture of data types—image files of receipts, PDF contracts, email attachments, etc. As NLP becomes more sophisticated, the technology will make it possible to easily extract relevant information, such as the financial data and input it into a database for more thorough analysis. 

This will drastically cut down on the time accountants spend on tasks like data entry and extraction, ultimately leaving more time for making strategic decisions and providing insights for their businesses.

Future-proofing your accounting career

One of the lessons that often comes from evolutions in technology is the need to stay agile. The AI revolution may not replace accountants, but it will require that they position themselves differently, whether they’re in large organizations or running their own practices. 

The emphasis in terms of high-demand skills will shift toward critical thinking and insightful recommendations that accountants can make in a world where AI can perform myriad time-consuming tasks.  What this means is that accountants will need to show a breadth of knowledge as well as the deep accountancy domain expertise that executing traditional tasks requires— the primary thing that AI changes about this is that accountants equipped with AI will be able to drastically improve the quality and speed of their work, thus shortening time-to-value for their organizations.

About DePaul’s Online Master of Science in Accountancy

DePaul’s online Master of Science in Accountancy program focuses on the skills students need to hit the ground running. Graduates will have numerous opportunities to apply accounting practices to real-world business scenarios, and they will be asked to critically analyze the processes and theories behind how accounting operates within a business.

Because the tools and technologies that businesses use evolve and change, we built our online MSA program to be technology-agnostic. By focusing on the theory and critical reasoning in accountancy and giving our students practical skills to succeed in any IT environment, our goal is to help graduates stay ahead of disruptions like AI as well as the ones that emerge even further into the future. 


About the contributor:

Ray Whittington, CPA, CIA, CMA, is the director of the DePaul University School of Accountancy and Management Information Systems.