The Cost of AI Resistance in Corporate Finance

Claire Burke, CPA CFO and Treasurer, Dearborn Group
Creating Value for Your Company

In the accounting and finance world, the adoption of new technology is gaining speed, providing both opportunities and challenges for organizations. As we corporate finance professionals strive to drive greater efficiencies and value by implementing the tools available today, I wonder just how far we’ll be able to go with the technologies of tomorrow.

Let’s first imagine a world where certain finance functions are performed automatically, like accounts payable and receivable processing, journal entry preparation, account reconciliations, daily system balancing, and compiling spreadsheets to create financial reports. Next, imagine us accounting and finance professionals being able to focus more exclusively on value-added work, like analyzing data outputs, reviewing trends and flux analyses, and spending more time understanding the drivers impacting financial results to provide meaningful insights to business leaders. Fortunately, this is becoming less of a “what-if scenario” thanks to artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA). As a reminder:

• AI is essentially a collection of technologies that use ML and other techniques to help a business perform tasks that typically require human intelligence. It’s a computer’s ability to learn and mimic human thinking, like judgement-based decisions, reasoning, and cognition.
• ML is the use of algorithms and data to enable a computer to learn from data and make decisions or predictions without being explicitly programmed to do so.
• RPA uses software to automate repetitive production tasks to streamline business processes. It also mimics human behavior to automate consistent, routine workflows in an effort to boost productivity (think bots). It’s a great tool for processes that involve a high-transaction rate of repetitive tasks. Notably, the use of bots in the business world has become so pervasive that they’re now more frequently referred to as “digital workers.”

When you put all three of these together, you get intelligent automation, which is a scalable, cognitive automation technology that can automate processes and increase efficiency and accuracy.

REAL-WORLD CASES

While we’re not yet at a point where we can automate all accounting and finance functions, there are several workflows that are good candidates for what can be automated now. For example, accounts payable can use RPA to streamline the workflow of approving invoices, matching invoices to purchase orders, and issuing payments. Another use case is financial reporting, where RPA can automate the process of data collection, analysis, and report generation. Many of us are still using spreadsheets for more customized financial reporting and analysis, which entails compiling data from multiple files, editing links to these files, or worse, inputting data directly in the spreadsheet from other sources. By teaching software that uses RPA to do this work, the process can be automated and completed with a few mouse clicks, cutting the time needed to perform a task from hours to minutes. It also improves the quality of the financial reports by enhancing accuracy and reducing human error. AI can also assist with account reconciliations, turning a time-consuming, rote task into a more streamlined, real-time process.

POTENTIAL CHALLENGES

While all of this fancy process automation likely sounds great, there are some challenges in making it a reality. One challenge, for example, is the resources needed to implement the technology, both the monetary and human capital kind. Not surprisingly, some technology solutions are expensive, and they take time and thorough planning to execute well. One of my favorite tools when considering an investment is a cost-benefit analysis, which allows you to lay out all the expected benefits to your organization. Ultimately, it helps justify the cost of either moving forward or not moving forward with the technology.

Also, process mapping the workflow to be automated can help you understand what challenges you’re solving, which can go a long way in ensuring you get what you need and remain within your budget and timeline. Of course, also bringing in the right people to provide input into the buildout helps mitigate missing a key element in the process.

Importantly, cybersecurity challenges can also come into play depending on the solution and your organization’s security policies. It’s best to get your IT team involved upfront when evaluating solutions to ensure they meet the security requirements of the organization.

Another challenge in implementing these technologies is the talent needed. With the changing landscape of the accounting and finance field, our talent needs are also changing. Today’s new technology solutions open the door for accounting and finance professionals to spend more time analyzing data and providing those valuable insights.

Of course, not everyone has the ability to be analytical, and this brings in another challenge. Having an analytical mindset is a difficult skill to teach if you want to upskill existing employees. There was a time when being detail-oriented, organized, and possessing strong technical accounting knowledge (or at least having an aptitude to gain this knowledge) was enough. However, today we also need folks to have critical-thinking and problem-solving abilities. The ideal accountant in this world of automation needs to be more of an accounting analyst versus a traditional staff accountant. And did I mention that they also need to be well-versed in multiple visualization tools, such as Tableau or Power BI, and capable of using them to extract accurate insights?

Admittedly, I can be dazzled by new technologies and envisioning all they can do. I’m also currently not in a position to implement everything I want since, as with any organization, there’s a prioritization process in place for corporate resources. But while there are costs to these technologies, there’s also a cost of not implementing them. In a world where organizations are striving for greater efficiencies to drive down costs and be more competitive, falling behind competitors by not addressing inefficiencies could cost your organization more in the long run.

Related Content:

• ‘Discover’ Where to Start With AI: When it comes to implementing artificial intelligence (AI) in your accounting practice, the practical first step should be an AI discovery process. Here’s how CPA firms can get started before the next tax season.
• Should Your Organization Be Utilizing ChatGPT? If used appropriately, ChatGPT can be an efficient tool that can save your organization time and money—here’s how to get started.