The Strategic Shift
CFOs and finance leaders today are juggling a lot. They are increasingly taking on a stronger role in the overall strategic direction of the company. They are navigating external economic scenarios that are out of their control but have to be addressed. They are also dealing with one of the biggest topics in business today, one that can’t be ignored: AI.
According to a recent survey from Deloitte:
Eighty-seven percent of CFOs expect AI to be extremely or very important to their finance department’s operations in 2026.”
Despite their full plates, many finance leaders are making time to embrace AI – at least from the standpoint of understanding what’s possible. For many CFOs, the biggest shift with AI so far is not replacing finance professionals on their teams. It’s empowering them to spend less time gathering information and more time acting on it.
Below are three practical AI applications for finance and accounting today.
3 High-Impact Ways Finance Leaders Are Using AI
1. Narrative Data and Conversational Reporting
Traditionally, getting answers from ERP systems required finance teams to rely on technical reporting specialists, complex BI tools, or manually exporting data into spreadsheets for analysis.
AI is changing that dynamic quickly.
Today, finance leaders can interact with data conversationally by asking questions in plain English and receiving immediate summaries, explanations, trends, and recommendations. Instead of building custom reports or navigating layers of menus, CFOs can simply ask:
- “Which customers haven’t purchased recently?”
- “What products have declining margins?”
- “Why did operating expenses increase last quarter?”
- “Which locations are underperforming against forecast?”
This type of conversational reporting dramatically lowers the barrier to accessing insights.
Modern AI tools can summarize large data sets, explain trends, and even generate narrative commentary that would have previously taken analysts hours to prepare. Rather than focusing on pulling reports, finance teams can focus on interpreting results and making strategic decisions.
For organizations running older ERP systems or heavily customized reporting environments, this is especially impactful. Many businesses historically invested in expensive reporting add-ons or depended on a handful of ERP experts to retrieve information. AI now makes advanced data interaction more accessible across the organization.
The result is faster decision-making, more self-service analytics, and improved visibility into the business.
2. Exception Management and Data Enhancement
One of the most practical applications of AI in finance is identifying anomalies, inconsistencies, and incomplete data before they create downstream problems.
Finance teams can spend significant time managing exceptions manually:
- Duplicate vendor or customer records
- Missing fields and incomplete data
- Outliers in transaction activity
- Coding inconsistencies
- Reconciliation issues
AI can accelerate these processes by scanning large volumes of financial and operational data to identify patterns.
For example, AI can:
- Detect duplicate entries and recommend consolidations to parent accounts
- Identify unusual transactions or spending patterns
- Flag anomalies in financial data
- Enhance data by filling in missing information (for example: if accounts are missing city information, AI can fill this in)
The result is improved customer or vendor data quality. Even seemingly small data issues can create issues with month-end close or financial reporting. By proactively identifying and correcting issues earlier, finance teams can improve reporting accuracy and potentially speed up the closing process.
3. Saving Time on AR and AP
Accounts receivable and accounts payable processes are among the most transaction-heavy areas within finance, making them ideal candidates for AI automation.
Many organizations still spend hours on manual tasks such as:
- Matching customer payments to invoices
- Following up on overdue receivables
- Processing invoices
- Routing approvals
- Paying vendors
- Handling exceptions manually
AI can help automate many of these workflows.
In accounts receivable, AI can match incoming customer payments to outstanding invoices, even when remittance information is incomplete or inconsistent. This reduces manual cash application work and accelerates reconciliation.
On the accounts payable side, AI can automate invoice capture, coding recommendations, approval routing, and payment scheduling. AI can also help identify duplicate invoices, unusual payment requests, or potential fraud risks before payments are issued.
The impact goes beyond efficiency. Faster AR processes improve cash flow visibility, while streamlined AP workflows help finance teams better manage working capital and vendor relationships.
Finance departments that embrace AI can significantly reduce time spent on repetitive transactional activities, freeing teams to focus on higher-value analysis and strategic initiatives.
Modern ERP + AI: Why the Technology Foundation Matters
While AI capabilities are advancing quickly, the effectiveness of AI ultimately depends on the quality and accessibility of your underlying business data.
Organizations running disconnected systems, outdated ERP platforms, or heavily manual processes may struggle to fully capitalize on AI initiatives.
Modern ERP platforms provide the foundation needed to support AI-driven finance operations by delivering:
- Real-time, centralized data
- Integrated financial and operational processes
- Scalable automation capabilities
- Built-in AI functionality
- Easier integration with leading AI platforms and tools
Solutions like Oracle NetSuite are supporting easy integrations with external AI platforms like Claude and ChatGPT while increasingly embedding AI directly into NetSuite.
This will give finance leaders the ability to leverage prebuilt AI agents, increase automation capabilities, and even build custom AI workflows tailored to their business needs.
As AI adoption grows, finance organizations with modern ERP environments will be positioned to move faster and unlock greater value from their data.
Real-World Example: Flying Toward the Future
Companies upgrading their ERP environments are doing so not only to improve current operations, but also to take advantage of the latest technology available – including AI.
Radio Flyer’s recent move to NetSuite highlights how companies are investing in modern cloud platforms to leverage AI and improve operational efficiency.
After implementing NetSuite, Radio Flyer was able to leverage the NetSuite AI Connector to connect their NetSuite software with AI. For Radio Flyer’s global operations, the new AI capabilities have been significant and have also increased the ROI on their ERP investment.
There’s No Hiding: AI is Here
AI is no longer a future concept; it’s quickly becoming a practical tool for finance leaders now.
From conversational reporting and data enhancement to AR and AP automation, CFOs are already using AI to improve visibility, reduce manual work, and make faster, more informed decisions.
But AI is only as powerful as the systems and data supporting it.
Organizations that invest in modern ERP platforms, integrated processes, and clean, accessible data will be best positioned to capitalize on the next generation of AI-driven finance capabilities.
