The Agentic AI Revolution: Why Today's CFOs Can't Wait Until Tomorrow

The modern CFO is a more important strategic voice in the boardroom than ever before. More than half of CFOs expect their firms to prioritize business building in the coming months and are expected to play a significant role extending beyond traditional financial functions in these initiatives. With heightened expectations, it’s no wonder that CFO turnover at S&P 500 companies has reached a 3-year high, with two-thirds of new appointments occupying the role for the first time. I’ve spoken to several finance leaders who are excited by the opportunity for their teams to contribute to core business objectives, pointing to strategic finance as an example of an evolving role dedicated to this updated mission. But before unleashing the talent at their disposal, they must first establish a solid foundation in their core responsibilities around planning, recording, and reporting. And while incoming CFOs bring a range of skill sets and experiences outside of just finance functions, they are also navigating a business climate rife with uncertainty. Indeed, CFOs’ top concerns for their firms in late 2024 span exogenous factors as varied as monetary policy, geopolitics, and labor availability. 

In such an environment, CFOs need the agility to optimize critical processes quickly without embarking on lengthy and complex technology overhauls. Indeed, digital transformation and automation programs have proven to be far from the panacea they were presented as in the early days. Instead of freeing up human capital, they frequently introduce rigidity, locking teams into specific systems and workflows. As a result, finance teams are left struggling to steer the ship in the face of shifting market conditions and evolving business needs.

AI agents are changing this paradigm. Unlike legacy approaches, these intelligent assistants work seamlessly across applications, interpret diverse data sources, and autonomously streamline workflows while preserving the flexibility to pivot with your business. For finance leaders looking to unlock more strategic value from their teams in uncertain times, this represents a unique opportunity to boost ROI utilizing existing rather than all-new systems - with early adopters already seeing 40-60% of time freed up for growth-oriented initiatives.

This article kicks off a series exploring how AI agents can transform finance teams today—not 10 years from now. We'll examine the high-impact areas AI can optimize, from cash flow control to team burnout reduction, and introduce practical use cases that yield tangible results. The question isn't whether to embrace AI agents but how quickly you can deploy them to gain a competitive advantage in an increasingly dynamic financial world.

Finance operations are a drag on modern businesses

The complexity of modern finance operations creates daily challenges that strain even the most sophisticated teams. Take the month-end close process: what was once a straightforward sequence now involves reconciling data across multiple instances of SAP and Oracle, managing intercompany eliminations across dozens of entities, handling ASC 842 lease accounting compliance, and ensuring SOX control documentation. A single post-close adjustment can trigger a cascade of updates across your consolidation systems, financial reporting packages, and internal control documentation - with each change introducing potential audit findings or control deficiencies. At month-end, these inefficiencies compound into major bottlenecks as GL accountants and controllers rush to close books while maintaining accuracy and audit readiness.

Manual processes and legacy systems are increasingly becoming liability points rather than assets, especially in global operations. What starts as a standard workflow - whether it's a three-way match for AP invoices, multi-currency treasury settlements, or accrual calculations - quickly balloons into operational complexity. Technical accounting teams must maintain countless reconciliations and controls, from automated clearing house (ACH) payment matching to IFRS conversion entries to SOX 302 certifications. These processes often rely heavily on Excel workbooks and institutional knowledge, creating a constant trade-off between speed, accuracy, and risk management. The situation becomes particularly acute during annual audit seasons and 10-K filing periods when finance teams face intense pressure to provide PwC or Deloitte with supporting documentation while simultaneously supporting strategic business decisions.

The challenges multiply in areas like cash management and forecasting, where treasury teams must synthesize data from multiple sources in real time. A shift in Days Sales Outstanding (DSO) or an unexpected CAPEX requirement can ripple through working capital projections, requiring rapid updates to cash positions and revisions to credit facility draws. Meanwhile, treasury managers juggle relationships with banks, hedge FX exposures through forward contracts, and optimize returns on short-term investments - all while ensuring proper controls and documentation for FASB ASC 815 hedge accounting requirements. The tracking and reconciliation burden alone consumes hours that could be spent on strategic cash optimization.

These manual processes don't just drain productivity - they create material business risks that impact working capital efficiency, audit readiness, and strategic agility. Payment errors can strain vendor relationships, while control gaps can lead to material weaknesses in SOX testing. As a result, highly skilled CPAs and finance professionals spend their time reconciling sub-ledgers instead of strategic initiatives that could optimize capital allocation and business performance. Most critically, the reliance on manual processes increases operational risk at a time when audit committees and investors are demanding greater financial transparency and control.

AI agents and the modern finance stack 

AI agents represent a fundamental shift from traditional automation approaches in finance. While RPA tools can replicate repetitive clicks and data entry and point solutions automate specific tasks, AI agents work more like digital teammates - they observe, learn, and execute complex processes across your entire finance technology stack. Think of them as intelligent assistants that can navigate between your SAP instances, Excel models, and banking portals, understanding context and making decisions just as your best team members would.

What makes AI agents transformative for finance teams is their ability to handle judgment-based tasks, not just rote processes. They can interpret unstructured data from vendor invoices, understand the nuances of accounting policies, and make decisions about transaction coding or exception handling. Most importantly, they adapt as your processes evolve - no rigid programming required. When your team changes a reconciliation process or implements a new control procedure, AI agents learn and adjust automatically.

This flexibility means you can extend the life and value of your existing systems rather than replacing them. Instead of undertaking costly ERP migrations or building complex integrations, AI agents work as an intelligent layer across your current tech stack - whether that's Oracle Financials, legacy mainframe systems, or homegrown solutions. They enhance what you already have, bridging gaps between systems and automating workflows that previously required manual intervention.

The impact on finance teams goes beyond just efficiency gains. By taking on repetitive tasks like transaction matching, report generation, and compliance documentation, AI agents free your skilled professionals to focus on strategic work that drives business value. Controllers can spend more time on financial planning and analysis instead of shepherding the close process. Treasury analysts can focus on optimizing cash positions rather than reconciling bank statements. Most importantly, reducing the burden of manual, error-prone work improves team morale and retention - critical advantages in today's competitive talent market.

For finance departments, this means achieving unprecedented efficiency without sacrificing agility or requiring massive change management. AI agents serve as force multipliers for your existing team and systems, eliminating the manual bottlenecks that slow down critical processes while maintaining the flexibility to adapt as your business evolves. The result is a more engaged finance function that can focus on strategic initiatives while ensuring core operations run smoothly and accurately.

Opportunities lie across key processes

Let's examine four critical areas where AI agents can deliver immediate value for finance organizations:

Financial Close Acceleration 

The monthly close process often consumes weeks of a finance team's time, with countless hours spent on manual reconciliations and adjusting entries. AI agents transform this process by automatically validating GL entries, flagging anomalies, and preparing reconciliation documentation. They can pull data from multiple ERPs, match transactions across systems, and even suggest correcting entries based on historical patterns. One Fortune 500 company reduced its close cycle from 12 days to 5 days by deploying AI agents to automate account reconciliations and intercompany matching while simultaneously strengthening its control environment.

Real-time Cash and Revenue Management

Traditional cash forecasting relies heavily on static Excel models and delayed information, leading to suboptimal working capital decisions. AI agents can continuously monitor the entire cash cycle - from revenue recognition to collections - providing real-time visibility and control. They automatically process bank statements, track customer payment patterns, validate revenue recognition calculations, and flag collection risks before they impact DSO. For billing and collections teams, AI agents can reconcile complex revenue recognition schedules, automate deferred revenue calculations, and ensure ASC 606 compliance without manual intervention. This enables treasury and accounting teams to make more informed decisions about cash deployment while maintaining iron-clad revenue controls. For example, a global manufacturer improved forecast accuracy by 40%, reduced their safety cash balance by $50M, and decreased DSO by 12 days using AI-powered cash cycle management.

Proactive Audit Readiness 

The annual audit cycle typically triggers a scramble to compile documentation and support for key controls. AI agents transform this reactive process into a continuous, proactive approach. They automatically maintain audit trails, monitor control effectiveness, and compile supporting documentation throughout the year. When auditors request samples, AI agents can instantly retrieve relevant documentation and explanations. One company reduced audit preparation time by 60% and audit fees by 25% by maintaining continuous audit readiness through AI agents.

Accounts Payable Optimization 

Manual AP processes are rife with inefficiencies - from invoice processing delays to missed early payment discounts. AI agents can automatically extract data from invoices (regardless of format), match them against POs and receipts, and route them for approval based on your delegation of authority matrix. They can identify opportunities for early payment discounts, prevent duplicate payments, and maintain vendor master data accuracy. A global retailer deployed AI agents across their AP function and reduced processing costs by 70% while capturing an additional $2M in early payment discounts annually.

In each of these areas, AI agents don't just automate tasks - they enhance the quality of financial operations while freeing up skilled professionals for more strategic work. The impact extends beyond efficiency gains to stronger controls, better decision-making, and improved team engagement. Most importantly, these improvements can be achieved without replacing existing systems or disrupting established processes.

Hitting the ground running

The path to AI-enabled finance transformation doesn't require a multi-year roadmap or complete system overhaul. Finance leaders can start with focused implementations in areas with immediate impact and clear ROI. For example, automating bank reconciliations or streamlining intercompany eliminations can deliver measurable gains within weeks while building confidence for broader deployment. The key is to identify processes that are both time-consuming and rules-based - think invoice processing, journal entry validation, or report generation - where AI agents can take over the heavy lifting while your team maintains oversight.

Finance leaders stand at a critical juncture. Market volatility and economic uncertainty demand greater agility than ever, yet finance teams remain bogged down by manual processes and rigid systems. AI agents offer a pragmatic path forward - one that delivers immediate efficiency gains without requiring massive investment or disruption. By starting with targeted implementations in core finance processes, CFOs can free up valuable team capacity while strengthening controls and improving accuracy.

The question is no longer whether to embrace AI but how quickly you can deploy it to gain a competitive advantage. Leading finance organizations are already using AI agents to accelerate their close process, optimize working capital, and strengthen their control environment. Those who wait risk falling behind as their peers build more agile, efficient finance operations.

In our upcoming series, we'll take deep dives into specific finance processes, examining exactly how AI agents are transforming these areas through real-world examples and implementation approaches. We'll explore specific use cases, ROI metrics, and practical guidance for getting started with AI-enabled finance transformation.

Tanay is a Product Lead at Orby AI , driving innovation with Agentic AI to transform enterprise processes. Previously, he worked at Google on projects such as COVID-19 Exposure Notifications, Payments, and the Next Billion Users initiative. Tanay holds an MBA and an MS in Engineering from Harvard University, and a BS in Applied Mathematics-Computer Science and Economics from Brown University.