Finance’s embrace of AI has reached a tipping point around the world: 88% of leaders surveyed by KPMG are now using the technology. Right now, financial planning is the top use case (78%), but AI-enhanced financial reporting is the next big trend; 68% of CFOs in a PYMNTS report viewed AI as essential to financial reporting. While it may seem as simple as using AI to generate reports and save time and resources, it’s becoming much bigger than that, just as CFOs are taking on more responsibilities than ever—especially as it relates to strategic business planning. Finance teams are betting that AI is the key to fully integrating finance into the wider organization and breaking down the information silos that slow down decision making.
From the Finance Department Out
Financial teams are facing increased demands on their resources and time. According to a report from Heidrick & Struggles, “Two-thirds of CFOs said that new responsibilities have been added to their team’s plate since the beginning of the pandemic,” with more reporting as one of the most common additions across industries.
However, teams are also facing a simultaneous explosion in the sheer amount of data they must work with. It’s estimated that global data volume will reach 181 zettabytes this year, up from just 9 in 2013. For scale, each zettabyte is one billion terabytes (TB); some of the largest external hard drives on the market are “only” 28TB by comparison. With this in mind, it’s no surprise that gathering data and administrative processes take up 75% of a financial planning & analysis (FP&A) team’s load.
With so much data, it can become difficult to determine what’s truly important and what needs to be shared. Furthermore, even if data is well-organized (by no means a guarantee), traditional searching can be ineffective. For example, say you know a data point comes from a September 2024 report; if you don’t remember the file in question, you could be looking in a dozen different locations.
With an AI model equipped with natural-language processing (NLP), finance professionals are accelerating data gathering. NLP allows team members to ask a question the way they would ask a colleague or a way that stakeholders would ask them; with NLP, this “other person” has immediate access to and recalls all your data, simplifying the creation of data-driven reports and insights.
NLP also offers another, less immediately obvious benefit: creating more effective reports. NLP is skilled at distilling complex financial information down to the essentials for non-financial colleagues. With this storytelling boost, financial teams can boost stakeholder engagement—and given that it’s one of PwC’s top skill priorities for future CFOs this year, it’s clearly a valuable tool.
From Other Departments In
Financial data is often siloed off from other parts of the organization, which can lead to other departments working from outdated information or feeling like finance’s decisions are inscrutable. While this is a known problem, it persists nevertheless. CrossCountry and Forrester found that 72% of financial leaders still think their department is siloed off. Consequently, a finance team can be perceived as a blocker to innovation, when the reality is that finance teams operate from a much broader perspective.
Even as these companies embrace AI, siloing can result in less ROI overall. To operate most effectively, a model needs to ingest as much data as it possibly can. PMSquare suggests that 80% data integration is a good goal to have, but not all companies have reached that level of integration.
Connecting financial data to the enterprise’s broader AI model doesn’t just result in better model performance, however. This improved flow naturally enables more accessible financial data—both in terms of being able to see the numbers and, with AI, understanding what the data truly means.
This accessibility is leading to more teams making truly data-driven decisions. Data evaluation and access is a major roadblock to broader adoption of these processes; Gartner has found that only 29% of organizations can sufficiently keep up with data right now. With AI’s assistance, this population will likely increase dramatically.
Elevating Finance to Its Proper Place in the Organization
Finance quite literally sits at the nexus of an organization, but that’s not always clear to other parts of the organization, from the C-suite on down. For example, CFOs often have a totally different set of priorities for their leadership, focusing more on analytics rather than influencing. If a CFO or any financial team spends more time in the back office than leading out in front, then rightly or wrongly, perceptions of their office will change and can build information silos.
But with the help of AI, every financial professional from the CFO down can break down barriers between departments and drive a more efficient and profitable business.
