Guest Post by Ershad Jamil | Former CGO, ServiceTitan
The business case for AI has largely been told as a cost-reduction story. Automate repetitive work. Reduce overhead. Eliminate administrative tasks. Do more with less. And that story is true. Businesses across nearly every industry are already realizing meaningful efficiency gains from AI as manual work becomes increasingly automated and workflows become increasingly streamlined. But framing AI primarily as a cost-cutting tool undersells what it is becoming.
The more consequential opportunity isn’t on the expense side, it’s on the revenue side.. AI isn’t just helping businesses operate more efficiently; AI is helping businesses capture opportunities, close transactions, recover lost revenue, and improve cash flow. In many cases, the revenue impact can ultimately be larger than the operational savings. For vertical SaaS companies, that shift has important implications because today’s intelligent fintech operating systems help generate outcomes, not just manage workflows like previous SaaS solutions.
From Workflow Automation to Revenue Generation
Consider a simple scenario that plays out thousands of times every day. A homeowner needs a plumber and calls the first business she finds online. Nobody answers. She immediately calls the second business. Someone picks up, schedules an appointment, and wins the job. The first company never knew the opportunity existed. The revenue simply disappeared.
Historically, a business either had staff available to answer the phone or it didn’t. Today, an AI agent can answer instantly, qualify the lead, schedule the appointment, send a confirmation, answer common questions, and continue following up until the customer commits. What was previously a missed opportunity becomes booked revenue. What’s important about this example is that AI isn’t simply helping someone work faster. It’s helping a business earn revenue it otherwise would have lost. That same dynamic is beginning to emerge throughout the payment lifecycle.
For years, payments have largely been viewed as infrastructure that supports a transaction after revenue has already been generated. Businesses make a sale, send an invoice, collect payment, and reconcile the transaction. AI is changing that relationship. Increasingly, it is helping businesses generate more payment volume, collect more of what they’re owed, and convert business activity into cash more effectively.
Collecting Revenue More Intelligently
One of the clearest examples for accelerating revenue is through accounts receivable. Every business deals with unpaid invoices. Customers get busy. Emails get buried. Payment requests are forgotten. Yet most collections workflows remain surprisingly static. A reminder goes out after a set number of days, followed by another reminder and eventually an escalation. The process is largely identical regardless of who the customer is or how they typically behave.
AI creates the opportunity for a far more intelligent approach. By analyzing payment history, communication preferences, invoice amounts, and prior engagement patterns, AI can determine the most effective path to payment for each individual customer. One customer may consistently respond to text messages. Another may only engage through email. Some may require multiple touchpoints while others simply need a reminder at the right moment. Rather than forcing every customer through the same workflow, AI can adapt in real time based on what is most likely to produce a successful outcome.
The result isn’t simply fewer hours spent chasing invoices. More invoices get paid. Cash flow improves. Revenue that might otherwise remain outstanding is collected faster. For software platforms monetizing payments, every additional dollar collected translates directly into additional payment volume flowing through the platform.
Recovering Failed Payments More Intelligently
A similar opportunity exists with failed payments. Across subscription businesses and recurring billing environments, a meaningful amount of revenue is lost every year because transactions fail to process successfully. Cards expire. Payment methods change. Temporary funding issues arise. Yet many systems still rely on basic retry schedules that make little attempt to understand why a payment failed or how it might be recovered.
AI can take a much more sophisticated approach. A payment that failed because of insufficient funds requires a different recovery strategy than one that failed because a card expired. A customer who has paid consistently for years deserves a different experience than a first-time customer. By evaluating the underlying cause of failure and adapting accordingly, AI can significantly improve recovery rates. At scale, even small improvements can have an outsized financial impact. Recovering a few additional percentage points of failed payments across a platform processing hundreds of millions of dollars can translate into millions of dollars in additional payment volume without acquiring a single new customer.
The Future of Payments Is Personalized
Perhaps the most overlooked opportunity is what happens before a payment is ever made. Most payment experiences today remain remarkably generic. Every customer receives the same payment request, the same checkout experience, and the same set of options regardless of context.
Yet consumers increasingly expect personalization throughout every stage of the customer journey. AI makes that possible within payments as well. A customer evaluating a large purchase may benefit from financing options presented at exactly the right moment. Another customer may be more likely to complete a transaction if installment payments are available. Others may prefer ACH, bank transfer, or alternative payment methods based on previous behavior and preferences.
The goal isn’t simply to create a better payment experience. The goal is to increase conversion. Small reductions in friction at the moment of payment can create meaningful improvements in transaction completion rates. More completed transactions mean more revenue for merchants, more payment volume for software platforms, and a better experience for customers.
What an Intelligent Fintech Partner Actually Looks Like
Understanding what AI can do for revenue is one thing. Finding a partner built to deliver it is another. Most payments companies have responded to the AI moment by bolting a chatbot onto existing infrastructure — that’s not the same as intelligence embedded across every layer of the platform. The highest-value use cases in fintech (autonomous AP execution, real-time risk monitoring, dynamic dispute management) require AI that understands payments context at a deep level. A general-purpose LLM doesn’t know your vendors, your merchants, or your chargeback history. An intelligent fintech operating system does.
Payabli is one of the clearest examples of what this looks like in practice. Built for vertical SaaS platforms, it’s not a payments company with AI features — it’s an Intelligent Fintech Operating System, with intelligence woven through Pay In, Pay Out, and Pay Ops from the ground up. Amigo, its AI agent suite, functions as an always-on business analyst, CS agent, and solutions engineer: surfacing residuals insights, flagging anomalies, and handling dispute workflows without a human in the middle. Its AP automation goes further — ingesting invoices, enriching vendor records, identifying the optimal payment path across ACH, virtual card, check, or portal, and executing without human intervention. The long-tail AP problem (the high volume of small vendors that aren’t worth staffing a human to manage) gets solved at scale by an agent that doesn’t require headcount to grow.
The right question for any platform evaluating an embedded fintech partner isn’t “do you have AI?” It’s “where does AI actually show up — and does it take action, or just answer questions?”
The Next Chapter of Vertical SaaS
What will differentiate the next generation of Vertical software platforms is not their ability to help businesses manage workflows more efficiently, but rather their ability to help customers acquire more customers, close more business, collect more revenue, and improve cash flow. Payments sit at the center of that opportunity because they represent the moment where every other business activity is converted into actual revenue.
Vertical SaaS platforms will be measured by how effectively they help customers grow. AI accelerates that shift. When your platform can answer the phone, qualify a lead, schedule an appointment, send an estimate, follow up with a prospect, generate an invoice, recover a failed payment, and optimize the payment experience, it is no longer simply managing a workflow. It is participating directly in the economic activity of the business. That’s a fundamentally different value proposition.
The most successful vertical SaaS platforms of the next decade won’t just be systems of record. They’ll become systems of growth.
If I were leading a payments for a VSaaS platform today, here’s what I would look for in an intelligent payments partner:
- An intelligent fintech operating system, not just a payments company with AI bolted on. Your partner should have AI embedded into every layer.
- A partner with broad AP coverage in embedded finance — managed payables, virtual cards, ACH, checks.
- Offers full-cycle automation: from invoice ingestion to payment confirmation, with AI handling the enrichment, routing, and execution.
AI isn’t just changing how software works. It’s changing what platforms are worth.