Growing pains aren’t just for kids; they impact small businesses too. Paying suppliers, collecting receivables, reconciling accounts and managing cash flow are critical tasks, yet they rarely represent the reason an entrepreneur starts a company. They are necessities that are essential, often painful, and more difficult to manage as businesses scale.
“When a business crosses a certain size, the finance work compounds faster than the team does. The volume of transactions, the vendor relationships, the complexity — it all scales up, but the headcount does not,” Archana Prasad, senior vice president, payments and financial services at BILL, told PYMNTS during a conversation for the June edition of the “What’s Next in Payments” series, “Aspirin or Vitamin? How AI Is Rewriting How Clients Buy.”
“Financial operations have always been aspirin,” Prasad added. “It’s never optional. Growing businesses have to pay their bills. They have to collect receivables. Full stop.”
But artificial intelligence is beginning to change that equation by altering what business owners believe should be possible.
“The underlying pain has not changed,” Prasad said. “Business owners are still dealing with the same operational headaches they have always had. What has changed is that they believe that there is a better way.”
She pointed to a growing body of evidence suggesting that small- to medium-sized businesses are adopting AI faster than their larger counterparts, driven by necessity and the promise of leverage.
“The smaller, small and medium-sized businesses are the first ones and the fastest ones to adopt it,” Prasad said. “And that has fundamentally changed what they expect from the tools and providers that serve them.”
How AI Is Shifting SMB Pain Points From ‘Do It Yourself’ to ‘Do It for You’
The day-to-day concerns of most small businesses remain remarkably practical. Business owners spend evenings matching invoices, monitoring cash flow and chasing payments instead of focusing on growth.
“The pain is very specific,” Prasad said. “It’s not abstract inefficiency. It’s cash flow visibility, it’s vendor relationships, it’s getting paid on time. These are the things that keep business owners up at night.”
For years, software vendors focused on helping customers manage those workflows more efficiently. AI is pushing the market toward a more ambitious goal: eliminating much of the work altogether. A year ago, the idea that software could independently execute financial tasks seemed aspirational. Today, it feels like a baseline requirement.
“Saying your software could handle something automatically sounded ambitious even just a year ago. But today it sounds like a completely reasonable expectation,” Prasad said.
That shift is forcing software providers to rethink product strategy. The evolution, she argued, mirrors a broader transition underway across enterprise software: from “do it yourself,” to “do it with you,” and ultimately to “do it for you.”
“The long-term direction is really about using AI to build more intelligent, touchless financial experiences — ones that remove work from customers’ plates, not just organize it better,” Prasad said.
The Main Street Moat Isn’t the Model; It’s the SMB Itself
AI adoption in finance is accelerating because businesses aren’t purchasing entirely new categories of software. They’re instead demanding better outcomes from systems they already depend on. Still, as generative AI lowers the barriers to building software, questions about competitive differentiation are becoming more urgent. If startups can assemble impressive demonstrations using off-the-shelf models, what protects incumbents?
“What you can’t replicate in a weekend is nearly two decades of financial workflow data, the integrations that sit at the center of how half a million businesses actually move money, and the trust those businesses have placed in the platform over the years,” Prasad said.
“The model is the easy part,” she added. “Anyone can spin up a demo in a weekend with off-the-shelf models.”
In financial services, the ultimate differentiator is not whether software works under ideal conditions but whether it performs reliably across edge cases, exceptions, audits, disputes and compliance requirements.
“Our customers aren’t just running workflows. They’re making financial decisions,” Prasad said. “At some point, someone has to look a CFO or an auditor in the eye and explain what happened and why it happened. That’s not a feature you bolt on after the fact. It has to be built into the foundation.”
The Emergence of a Third Category
The most successful software companies may no longer fit neatly into the vitamin-versus-aspirin distinction. Instead, a third category is emerging: software that becomes so deeply embedded in operations that replacing it becomes organizationally disruptive.
“We started with tools that helped people do the work. Now we’re building systems that do the work alongside them, and increasingly for them,” Prasad said.
AI, it turns out, doesn’t transform financial software from a vitamin into an aspirin. It makes the aspirin much more effective. In that future, software evolves from being a tool into infrastructure.
“What AI has done is make that aspirin work dramatically better,” Prasad said. “The question stops being, ‘Do you need this?’ because you always did. It becomes, ‘How much are you leaving on the table by not doing this well?’“
“Maybe the better question isn’t vitamin or aspirin,” she added. “It’s: Does your software get smarter the longer a business uses it?”
Watch the full interview with Archana Prasad, senior vice president, payments and financial services at BILL, to hear more about:
- Why AI is changing expectations faster than it is changing business problems. Prasad explains that SMBs are adopting AI ahead of larger enterprises, creating a new expectation that financial software should not just streamline workflows but increasingly execute them automatically.
- How financial software is evolving from workflow management to autonomous execution. The conversation explores BILL’s vision of moving from “do it yourself” to “do it with you” and ultimately “do it for you,” using AI to remove operational burdens around payables, receivables and cash flow management.
- Why trust, explainability and financial data create the real moat in the AI era. Prasad argues that while competitors can quickly build AI-powered demos, they cannot easily replicate decades of financial workflow data, embedded integrations, auditability and customer trust that underpin mission-critical financial infrastructure.
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