In what feels like a matter of months, artificial intelligence has gone from buzzword to must-have technology.

Similar to tech advancements of the past, what was considered “AI” just a year ago is now archaic by today’s standards, and the business world is taking notice. As a result, industries are looking for ways to leverage AI and automate workflows to make more accurate decisions faster. So much so, that Gartner and McKinsey have both identified AI-enabled automation and predictive machine learning as key technologies that will transform the way businesses operate.

What This Means For The Financial Industry

Although the finance and accounting industries were some of the first to lean into the digital transformation of the 1970s and ’80s, they’ve remained stagnant as other sectors have continued to evolve. This has left banking systems to rely on outdated, arcane computer systems woefully unequipped to handle the demands of modern business. The downstream outcome of this is that finance and accounting professionals have been forced to bear the brunt of this antiquated technology, leaving them to fill in the gaps.Why do credit card statements make it so hard to identify what was purchased? Why do bank feeds need to be manually reconciled? What was that check for?

All of this comes down to a lack of context. The money moved, but the knowledge about why it moved got lost. Accountants are then put through the tedium of reconstructing that knowledge from disparate systems, every month, to close the books. With a lack of any real time view into what, why or how transactions occur, extreme mental gymnastics are often required to understand the financials and overall health of a business.

Reimagining The Financial System With AI

With AI’s help, business finance is ripe for disruption.

AI-enabled accounting takes a proactive approach to processing financial information. This means reducing the likelihood of errors, ensuring greater consistency across the ledger and allowing continuous data monitoring.

Where once bad or missing transaction information led to messy books and uninformed business decisions, advances in AI can now use context clues to categorize transactions accurately from the outset—getting us to a world where transactions can actually be “self-documenting.” This will make accountants’ and business owners’ lives easier while improving overall operating efficiencies.

Understanding AI's Limits And Challenges

Like any new tool a company is looking to implement, there are limitations and challenges in doing so—and AI is no different. When it comes to using AI in finance, the first and often largest challenge is bias.

If the data used to train an AI model is biased, then the downstream decisions will be unfair and discriminatory. Bias can manifest in myriad ways as well. For an example focused on the bookkeeping process, if your model is trained on data only from a certain industry (e.g., restaurants), that will bias the categorizations that a model might employ to that specific industry, even if your company is of a different industry.

It’s important to have diverse data in order to build a non-biased model.

In an untapped space that’s yet to be regulated, ethics and compliance are hotly debated topics that haven’t been fully flushed out. While companies in certain industries will need to comply with data and consumer protection, as well as anti-discrimination laws, there remain regulatory unknowns around AI that are left to be made at the discretion of the companies implementing the technology.

How To Get Started With AI

Implementing AI is ultimately a company-by-company decision—and even more granular than that, a job-role-by-job-role decision. An easy way to start thinking about this is to ask the question, “Is this implementation for a technical team or not?”

For Business Owners And Individuals

If you’re a business owner or individual looking at AI to become more efficient, start by understanding what your and your team’s strengths are and what parts of the current workflow could benefit from deeper automation.

What do you do every day that feels repetitive and monotonous? These answers will help determine which AI tools can help give you superpowers. Whether it’s ChatGPT to speed-boost your content marketing efforts or Midjourney to inspire design ideas or concepts, there’s a tool for your needs.

For Technical Implementation Inside Companies And Teams

When companies seek to implement more technical AI solutions (e.g., building and deploying their own models), careful planning and consideration are essential for success. First, companies need to understand the problem they’re looking to solve and why they want to implement AI.

Then, businesses will need to collect and prepare high-quality data and choose appropriate algorithms and tools for the project. Teams will need to develop and test the AI model, ensuring that it meets the defined goals and outcomes and is free of errors or biases.

Finally, in order to understand the impact of AI and whether it works for your role, you’ll want to test the model against historical data and compare that as a proxy for future performance.

Once the model has been developed and tested, companies can begin to deploy it. However, unlike traditional software, deployment isn’t the end for AI. Deployed models are then exposed to new information, data drift and skew. It’s important to monitor and evaluate the performance of the model and make adjustments as necessary.

A New Era

For an industry that’s seen limited technological intervention over the past three decades, the integration of AI into everyday finance and accounting workflows represents a radical overhaul of an outdated, “manual” way of doing business.

By reducing the likelihood of human error and constantly monitoring data for integrity, businesses can achieve healthier operations and greater efficiency.

Small-business finance is broken, but it won’t be for long.

LeackStat 2023