David Zwick, CFO of Billtrust, is a global-minded technology executive with success driving operational and financial growth strategies.
Two years ago, a Deloitte study found that just 15% of CFOs were building generative AI (GenAI) into their strategy. And 34%, in a survey conducted by my company, Billtrust, admitted they believed it’d take the next generation of finance leaders to fully implement it. If you’re a CFO reading this, there’s a good chance you were part of that initial skeptical cohort—and there’s an even better chance you wish you had moved faster.
The Stakes Are High
Now, as agentic AI promises to automate entire processes without human intervention, a Capgemini report states that trust in fully autonomous AI agents has dropped from 43% to just 27% in one year, and fewer than 1 in 5 organizations report high maturity in the infrastructure needed to support them. Why? They’re stuck in evaluation mode, citing concerns about control and trust, while dismissing autonomous AI as “not battle-ready.” But here’s the thing: We’ve heard this before.
The uncomfortable truth is that finance leaders are repeating the exact same pattern of overcautious adoption that cost them months of competitive advantage with generative AI. And while some companies are still debating trust and control, others are already deploying agents in production to streamline workflows, accelerate decisions and reshape operations. Forbes reports that 74% of early adopters are already seeing measurable ROI, including revenue growth, improved productivity and stronger cybersecurity.
The difference between these leaders’ adoption of GenAI and agentic AI is that with agentic AI, the stakes are higher, the potential returns are greater and the window for first-mover advantage is narrowing faster than before.
While most CFOs remain paralyzed by the prospect of “letting go of the wheel,” a small group of forward-thinking finance leaders are quietly building the autonomous processes that will define the next decade of financial operations. The question isn’t whether agentic AI will transform finance; it’s whether you’ll be leading that transformation or scrambling to catch up.
The Pattern Is Clear—And Costly
The parallels are striking. A few years ago, CFOs cited the same concerns about generative AI that they’re voicing about agentic AI today: lack of control, unclear ROI and questions about reliability. Many waited for “more mature” solutions while competitors gained ground with early implementations.
But those that moved first with GenAI saw better results and built organizational capabilities that compounded over time. They trained their teams, refined their processes and developed the AI literacy that’s now essential for finance leadership. The CFOs who waited are still playing catch-up, not just with technology but with the cultural and operational changes that AI adoption requires.
The irony is that success with generative AI should make agentic AI adoption easier, not harder. Organizations that have already proven AI’s value have the foundation, the team buy-in and the technical infrastructure to take the next step—yet many are choosing to start the evaluation cycle all over again.
Meanwhile, the business case for autonomous financial processes grows stronger daily. Manual accounts receivable workflows, collections prioritization and cash application processes aren’t just inefficient; they’re actively constraining growth. Every day spent in analysis paralysis is another day of operational friction that autonomous AI could eliminate.
Where To Start: The Low-Risk, High-Impact Entry Point
The good news is that agentic AI adoption doesn’t require a leap of faith, just some strategic thinking about the places human error already costs more than AI mistakes could.
Start with the processes that are already broken—those high-volume, repetitive tasks where your team spends hours on work that adds little strategic value. In accounts receivable, that means payment matching, dunning sequences and basic dispute resolution. These aren’t strategic decisions that require human judgment; they’re operational tasks that follow predictable patterns and clear business rules.
The beauty of these entry points is that they’re measurable and contained. You can pilot autonomous payment processing for a subset of customers, track the results and scale gradually. Unlike broad AI initiatives that touch multiple departments, you can prove value quickly and build confidence before expanding to more complex processes.
Your finance team won’t disappear overnight. Instead, they’ll spend less time on operational drudgery and more time on analysis, strategy and the high-value work that actually drives business outcomes. The operational tasks that consume their days today will run themselves, freeing your people to focus on what humans do best.
The Cost Of Playing It Safe
I firmly believe that two years from now, you’ll be reading about CFOs who automated their cash application processes in 2025 while you were still evaluating vendors. They’ll be processing payments in minutes instead of days, their DSO will have dropped significantly, and their finance teams will be focused on strategic initiatives rather than manual reconciliation.
The finance leaders who moved early on generative AI now have a playbook for AI adoption, trained teams and proven ROI to show their boards. They understand that waiting for “perfect” solutions means missing the window in which competitive advantages are built. The same opportunity exists with agentic AI, but the window is narrower this time.
The most successful CFOs aren’t the ones who make perfect decisions. They’re the ones who recognize inflection points early and move decisively when the risk of inaction exceeds the risk of action. For agentic AI in finance, that inflection point is now.
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