Members of AFP’s North America FP&A Advisory Council discussed their AI goals for 2024.

The council members’ goals mirrored AFP conversations with members in other settings: Their goals spanned the spectrum, from those who are just starting to look into AI to those who have a multi-pronged plan for implementation. Read on to see which one of our council members best aligns with you and get some ideas for where you may go next.

Getting Started with AI

One group of council members expressed that they are making their way to the starting line.

“Before I can even consider using AI in our finance function, I need to bring my department into this century,” said one council member. “The shop I inherited is one of those Excel hells, and even worse, half the team is using different software — Google Sheets and Slides — leading to even more manual manipulation. I need to get the basic foundation in place before I’ll be in a position to really contemplate AI.”

“This year is going to be about discovery — what AI in finance really means for me,” said a second council member. “There are a lot of ideas being shared by experts, but I've yet to map it all back to what it means for me in my day-to-day job.”

Another council member works for a company that went through a big reorg last year, so everything “extra” was pushed to the side: “As a finance group, we haven’t even thought about how to best start using artificial intelligence in finance. My goal for this year is to initiate a restart; I’ll start small, applying it to error-checking models and mathematical problems.”

For one council member of this group, momentum has started to build: “My goal is to normalize the conversation, get everybody thinking about artificial intelligence and machine learning in finance,” said the council member. “It’s important so that as they develop new processes and  start acting like a big company, they are prepared. Thankfully, they are starting to come to this realization.”

Tentative Use Cases for AI

Starting small is the strategy of several council members. “I thought I’d start very small and kind of slide into the AI space,” said one council member. Several council members are looking for individual gains.

“I’m looking for opportunities to use AI to make me more productive and efficient,” said a council member. “I’ve been using ChatGPT to help me create things like outlines for blogs, and I’m really looking forward to being able to use Microsoft Copilot. I have a lot of administrative tasks on my plate that are just part of running a business, and it would be helpful if I could automate more of them and put AI to use for my clients as well.”

Another council member is using ChatGPT, too, because, as they put it, “it’s a much better writer than I could ever be.” They’re also using it to forecast short-term cash needs and predict revenues based on assets under management.

A growing range of generative AI assistants will enhance the efficiency of specific tasks by 10% to 20%, according to Boston Consulting Group. One council member shared how they are using it to spot anomalies that might signal fraud or noncompliance: “We have AI assessing every order that comes in, establishing machine learning and using different variables to determine whether it could be fraudulent. Our finance team is using AI to assess companies that are more likely to file for bankruptcy by looking at different payment scores and trends. AI would be new in the FP&A area for us, and I'm just looking to learn and see how we can apply it.”

One council member is laying the groundwork for a test project for spend projections: “We go through our planning and load the budget into the system. At the end of the quarter, we find out what did and didn’t happen. The variance is due to timing, so I’m thinking that if we have our history and the data of how we spend our money, then we could use AI to help inform the timing.”

AI can help reinvent business partnering by offering insights into financial forecasts, aiding scenario planning during budget cycles, and providing faster and more thorough business intelligence. It can modernize tedious finance tasks, making it easier to gain quick and clear insights.

“Working with different departments such as accounting and operations and business development, everybody wants their own specialized file,” said a council member. “And that requires a lot of manual adjustments, so I'm hoping that in 2024 we can use AI to kind of force the automation of a lot of the standardized reporting because I'm trying not to live in queries all day. I just don't have time for it.”

Another council member shared their ambition and frustration at the current state of tools:

“We are a $200 million company, and I'm thinking about what I can utilize that's cost-effective but tailored for smaller businesses to help make decisions on pricing based on volatile consumer behavior.  There are so many tools out there; I’m looking forward to the AFP conference this year so I can meet the providers who are thinking in terms of AI.”

More Advanced Use of AI in Finance

The CFO of one of the council members’ companies held an AI hackathon as a structured way to crowdsource use cases: “One thousand finance professionals across the different functions and teams were tasked with coming up with three to five ideas for their function. We shortlisted about 20 ideas, which are now being tested on a very small scale to see how they can be incorporated into the existing processes.”

The strategy should outline a roadmap for AI implementation, detailing milestones, timelines and methods to measure the impact of AI initiatives: “We are focusing on how to expand the areas where we already have AI in place, and we’re doing it in two ways. At an enterprise level, with machine learning and forecasting, we've prioritized very specific metrics, which, up to this point, have been really difficult to forecast, so we are trying to improve the efficiency of the system and the accuracy of the output. And for the individual, every finance person has a challenge to leverage those co-pilots in everything we do and each of our finance teams. We all share a log of what we are doing and how many hours we save.”

To integrate AI into financial operations effectively, a sturdy data infrastructure is essential; it must be able to manage large datasets and deliver real-time insights. While you are investing in data infrastructure that supports AI technologies like machine learning and deep learning, keep in mind the importance of ensuring security and compliance with data privacy regulations.

“We're solidifying what our data foundation plan is going to be in order to enable the capabilities of artificial intelligence and machine learning in finance over the next two to three years. There are a lot of needs when it comes to data and large data sets, and we want to make sure we're storing it in the most effective and efficient way possible, especially as cloud computing and data storage can be costly depending on how you store it,” said another member.

One council member works for a company that has instituted a multi-pronged approach involving training, the creation of data lakes, employment of data scientists and partnering with a large AI company.

The council member said, “At the company-wide level, we’ve launched a Digital Ambassador training where we hold training cohorts of about 50 people, teaching them the language capabilities and providing use cases for machine learning that we have done in-house.

“Within finance, we’re creating data lakes as a basis for all our models, though we are being cautious on the AI front due to the confidential nature. So right now, we're leveraging more data scientists to model out what we would want AI to do in the future.

“And finally, we have a strategic partnership with one of the large AI companies; we're partnering with them on a project that pulls in the SEC filings of our competitors and then uses natural language learning to parse the content for questions and answers. That takes some of the burden off our FP&A people.”

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