Ahsan Shah, SVP AI & Analytics, Billtrust.
For decades, finance teams have been tasked with streamlining operations, cutting costs and reducing friction. In many ways, automation delivered. Reports arrived faster, payments flowed more smoothly and compliance became less painful.
But somewhere along the way, a new challenge emerged. Finance got faster, but not necessarily smarter. The systems became more efficient, but they didn’t always make better decisions. Speed alone isn’t enough.
For many, agentic AI has become a key part of mitigating this challenge. These systems don’t just automate tasks—they analyze context, make decisions and initiate action. They can navigate ambiguity, learn from patterns and respond in ways that feel more human than mechanical.
We’re already seeing this evolution in how people interact with AI, such as using ChatGPT or Grok, in voice mode for natural conversations. This human-like responsiveness is rapidly becoming the new standard for how we expect technology to engage with us.
Finance As A Front-Line Experience Function
Credit and collections are a perfect example. What was once a back-office function focused on reminders and risk thresholds is now one of the clearest expressions of a company’s values. When a customer misses a payment, how the business responds can either build trust or erode it.
Traditional automation sends standardized follow-ups on a set schedule, but agentic AI takes this further. It can assess payment history, communication tone and broader account context to decide how best to engage. A long-time customer might receive a more empathetic outreach, while a first-time issue could trigger a proactive phone call. The goal isn’t just faster resolution—it’s smarter, more human interaction.
Tools like intelligent email assistants can help teams to automatically triage high volumes of inbound messages, draft context-aware responses and close the loop on payment confirmations or disputes faster than ever. This can result in faster collections and stronger, more professional customer communication at scale.
And those nuances matter. In tight economic conditions, preserving customer relationships can directly impact revenue retention and brand equity. Agentic AI helps make it possible to scale empathy without sacrificing efficiency—something that would have been impossible with manual processes alone.
How To Build The Foundation For Intelligent Finance Operations
The transition to agentic AI requires finance leaders to fundamentally rethink their operational frameworks. Success depends on establishing clear decision-making protocols before systems go live.
Define exactly when AI should hand off to human judgment.
Focus on identifying scenarios where context, empathy or complex reasoning are essential beyond simple dollar thresholds. For collections, this might mean escalating cases involving customers who’ve experienced recent hardships or disputes that involve multiple stakeholders.
Map your data ecosystem—and your domain context.
Agentic AI is only as smart as the data it can access and the business context it’s given. Finance leaders need to audit their current systems, identify integration gaps and ensure that platforms like collections, CRM and risk assessment tools are interconnected.
But it’s not just about data flow—it’s about decomposing business processes and embedding domain-specific knowledge so AI can interpret and act on that data meaningfully. While AI may seem like a technical advancement, its true power lies in empowering functional business users who can articulate concepts and workflows unique to their domain. That context is what transforms AI from a tool into a strategic partner.
Establish governance frameworks early.
Create clear accountability structures for AI-driven decisions. Who reviews automated credit extensions? How do you audit AI communication with customers? Building these oversight mechanisms from day one prevents larger governance issues later.
The most successful implementations I’ve observed start small—often with a single use case like payment reminders—then gradually expand as teams build confidence in the technology and refine their processes.
Not Replacing Human Judgment—Enhancing It
All of this points to a larger shift in mindset. Finance has long been seen as a function that protects value. Increasingly, it’s becoming a function that creates it. A successful agentic AI strategy should help to enable this transition not by replacing human judgment, but by enhancing it.
These systems are designed to escalate complex situations and loop in human expertise precisely when it’s needed most, ensuring that human time is focused on the most relevant and high-value interactions. When machines handle more of the repetitive, rule-based work, finance leaders can focus on higher-level strategy. They can spot opportunities, guide decisions and shape experiences that align with both business performance and customer expectations. This complementary approach amplifies human capabilities rather than replacing them.
When day-to-day work becomes less transactional and more analytical, roles evolve—and so do career paths. That shift can play a critical role in talent retention and development.
The Opportunity And The Responsibility
As with any powerful tool, success depends not just on adoption but on intention. The real opportunity with agentic AI is empathy at scale. Finance teams now can engage with customers and partners in ways that feel tailored, thoughtful and responsive without sacrificing control or consistency.
But this also requires new ways of thinking. CFOs must consider how decisions are made, what inputs are valued and how to build trust in systems that act on their behalf. They’ll need to collaborate more closely with teams in IT, operations and customer service to ensure that intelligent systems reflect real-world values, not just business logic.
The companies that get this right won’t just operate more effectively. They’ll build finance organizations that are more connected to the business, more aligned with the customer and more capable of navigating whatever comes next.
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