Andy Watts is the Chief Financial Officer for Brown & Brown, Inc.—ensuring financial discipline, value creation and operational excellence.
For much of its history, the finance function has been structured around recording and reporting what has already happened. Artificial intelligence is beginning to turn that structure on its head.
5 Ways AI Is Reshaping Finance Operations
As AI becomes embedded in financial systems, finance teams are empowered to analyze operations in near real time, identify risks earlier and generate insights that inform faster strategic and operational decisions. The more significant change I see, however, is not technological. It is how finance work is evolving and the skills finance teams will need to succeed in it.
1. Automation Of Core Financial Processes
The first major shift is the automation of routine financial tasks.
AI tools are increasingly capable of handling activities such as reviewing journal entries, processing cash applications, reconciling accounts, processing invoices and managing accounts payable workflows. These capabilities reduce manual effort and improve efficiency across finance organizations.
For many teams, this means fewer hours spent on transactional work and more time available for analysis and decision support.
Automation alone does not transform finance, but it creates the capacity for finance professionals to focus on higher-value activities.
2. Data-Driven Forecasting And Budgeting
AI also improves the ability to analyze large volumes of data when developing budgets and forecasts. Traditional forecasting processes often rely on historical trends and periodic updates. AI systems can continuously analyze large volumes of operational data and identify patterns more quickly to help organizations anticipate changes sooner.
As a result, forecasting becomes less about producing a static annual budget and more about continuously updating expectations and analysis based on new information.
Finance teams increasingly need technical and analytical skills to interpret these insights and translate them into strategic recommendations for the future.
3. Strategic Decision Support
As automation reduces manual work, finance professionals are spending more time advising leadership teams and their business partners.
AI systems can process massive datasets and perform analyses that would be almost impractical for humans to do with traditional tools. The role of finance professionals is increasingly shifting toward asking the right questions, understanding the business deeply, interpreting the results of analysis and communicating implications to the business.
In this environment, finance becomes a strategic partner rather than simply a reporting function.
4. Real-Time Risk Monitoring And Compliance
Risk management is another area where AI is reshaping finance operations. Traditional audit and compliance processes often identify issues only after transactions have occurred—often many months after. AI systems can monitor activity continuously or in real time, flagging unusual transactions or patterns that may indicate risk.
Instead of reviewing hundreds of transactions after the fact, finance or audit teams can focus on the critical transactions that appear unusual or high risk. This real-time monitoring capability allows organizations to detect issues earlier and respond more quickly.
5. Enhanced Collaboration And Communication
AI also improves the ability to synthesize information across large volumes of documents and data sources.
For example, finance teams can use AI tools to review internal or external reports, summarize key insights and present them in structured formats that support faster decision-making. The time saved allows finance professionals to focus more on communicating findings and collaborating with business leaders about potential actions.
In many organizations, this shift is strengthening the relationship between finance and the rest of the business.
The Continuing Importance Of Human Oversight
Despite these capabilities, AI should be viewed as an assistant rather than a replacement for professional judgment. Human oversight remains essential.
For example, when using AI to analyze financial data, small differences in how a question is framed can produce very different answers. Asking for a stock’s “average price over the last 30 days” may produce a different result than asking for its price over “the last 30 trading days.” Without human review and oversight, these distinctions could lead to incorrect conclusions. Think of your AI agents like interns: They need clear communication and coaching to perform at their highest level.
This is why leaders emphasize a “human-in-the-loop” approach to AI adoption. Technology accelerates analysis, but finance and accounting professionals must still validate results and interpret their implications.
Building A Strategic Road Map
As AI capabilities expand, CFOs increasingly need to rethink how their organizations are structured, how work and data will flow through their org chart and which skills their teams need to truly be future-forward.
That work typically starts with developing a clear vision for business analytics and an architectural road map for financial systems. Without a defined vision, it becomes difficult to determine which data and technologies to implement, how systems should integrate and, most importantly, what capabilities your teams must have to execute the vision.
In practice, this road map almost always centers on three critical elements: people, process and technology.
Organizations must first design the processes they want to operate. Technology can then be implemented to support those processes, and finance leaders can determine which roles and skills are needed to leverage them to their fullest potential.
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