AI in the enterprise: The future of corporate finance

While there might be more obvious applications for AI in the enterprise, there is perhaps no single use case for AI that has more potential to impact your bottom line than finance—literally. There is a significant disparity between the high skill level of accounting professionals and the basic data entry that they are so often required to do.?

Being data-heavy in a manual world makes this an inevitable reality, so you end up with specialized, expensive employees who are simultaneously overworked and underutilized. Manual work like this is also error-prone, and mistakes in the financial world almost always have a negative impact on your bottom line.?

Machines simply perform this kind of repetitive, tiresome work better than we do, because, well, they don’t get tired. Leveraging artificial intelligence in this sector would not only free up valuable resources to do more strategic and impactful work, but improve the accuracy of the work they would leave behind. Fewer mistakes leads to lower tax liability, more predictable financial reporting to your board and shareholders, reduced fees associated with audits, and fewer fines for being out of compliance. It’s a trickle-down effect that sits right above your balance sheet.

Let’s look at a few of the specific use cases within finance where the right deployment of AI will have a transformative effect.?

Accounts payable. Automation is not a new concept in this realm. The presence of so much structured data when matching purchase orders to invoices makes for a clear use case. We’ve seen systems that can match documents, but if there are errors, manual intervention is still required. The opportunity here with AI is to add the critical intelligence component—a system that can not only identify mistakes or holdups, but flag them to the appropriate party with a roadmap for how to best resolve the issue. On the numbers side, this would also allow you to stay more up-to-date in assessing vendor performance.?

Audit and compliance. The closing of financial books is everyone's least favorite headache. Teams of highly skilled accountants are tied up analyzing large volumes of data trying to catch errors and assess transaction histories. The process is time-consuming, and prone to error. An AI algorithm applied to this function would not only speed things up, but could greatly increase accuracy. Beyond that, it could provide continuous, real-time monitoring against the compliance process, showing you how to improve. When regulatory changeovers occur, such as ASC606, the new standard requires several months of work to be able to support from an infrastructural standpoint. Those changes require months of painful, manual work and expensive outside consultants. Much of this can and should be automated to free up highly trained resources for more strategic work. The opportunity for AI across improving internal controls when doing testing and changes in relation to 404(b) is a related opportunity.?

Tax optimization. This use case is closely related to audit and compliance. Here the core objective is to enhance the tax process while reducing liability. It’s about improving accuracy and efficiency. Utilized to its full potential, AI should allow you to automate everything from a draft version of your tax filing and planning for the next year, to fact checks and error reduction. Currently the traditional solutions such as Avalara have helped but still require a tremendous amount of manual effort to not just deploy but also manage. AI can make an impact here and drive greater efficiency.?

Foreign exchange rates. There has always been a considerable amount of volatility in exchange rates as it relates to currency risk. Global economic events can be triggered by any number of financial or political upheavals. They are difficult to predict, and massively impactful within an increasingly global economy. For companies working on a large scale, changes to a currency’s value in the time it takes for an invoice to arrive could have costly implications. Planning and forecasting for currency evaluation is an area ripe for the application of AI. With such clear and available data to work with, training a model to identify stable currencies would be relatively easy. That model could then track global currency in real-time, predicting changes and helping plan for the impact.?

Budgeting and planning. Budgeting, specifically in large companies, is a significant lift and an arduous process. Frankly, we all hate having to do it. The manual aspects of gathering budgets from individual departments, analyzing who needs less or more to create a balanced plan for the coming year, is time that could be better spent working on the initiatives themselves. I envision a Chat-GPT type system for budgeting and planning. Instead of grinding away manually, a generative AI program could ingest the past few years of budget information and collaborate with you to account for what you plan to add in terms of headcount, renewals, and current initiatives to automatically generate a first draft of your budget for the coming year.