From Vertical SaaS to Agentic: What Work Could Your Platform Start Doing?

TL;DR

AI is quickly becoming part of nearly every software roadmap. But for vertical SaaS leaders, the bigger opportunity may not be adding more AI features. It’s changing the role the software plays in the customer’s business.

For years, vertical SaaS platforms have helped customers manage work by replacing spreadsheets, digitizing manual processes, centralizing data, and making workflows faster and easier. Agentic AI introduces a different possibility: software that doesn’t just help someone complete the work, but starts doing more of the work for them.

That shift was at the center of a recent discussion between Luke Sophinos, Founder of Vertical SaaS Group, Mark Passifione, SVP of Integrated Payments at Xplor Pay, and Daniel Burton, VP of Payments for Vertical SaaS at Xplor Pay.

And it raises a much more useful question than, “Where can we add AI?”

How Does AI Change the Role of a Vertical SaaS Platform?

For much of its history, vertical SaaS has helped people manage and complete work more efficiently. AI creates the opportunity for software to play a more active role in that work.

A simple way to think about the progression is:

RECORD → RECOMMEND → ACT → OUTCOME

A traditional platform might show a business that an invoice is overdue. An AI-enabled platform could determine which overdue invoices deserve attention and recommend the appropriate next step. An agentic platform could eventually take an approved action itself, such as sending a follow-up or initiating the next step in a predefined workflow.

The important shift isn’t simply more sophisticated technology. It’s moving from software that helps people do the work toward software that can reliably take on portions of the work itself, with the appropriate permissions, controls, and human oversight.

Why Should Vertical SaaS Leaders Start With the Workflow?

One of the easiest mistakes to make with AI is starting with the technology.

Where could we add a chatbot? Could we add a copilot? Which product features could use generative AI?

Those questions can lead to useful features, but they don’t necessarily lead to meaningful customer value. A better starting point is to look at the workflow your software already supports and map what actually happens from Point A to Point Z when a customer tries to accomplish something important.

Where does information enter the process? Where are decisions made? Where does work move from one person or system to another? Where does someone have to stop what they’re doing and take an action?

Then look for the work between those steps. Where are people repeatedly reviewing information, making predictable decisions, following up, coordinating tasks, or moving information from one place to another?

Those may be some of the places where AI can do more than make an existing feature faster. They may be opportunities for the platform to start taking on portions of the work itself.

This is particularly important for vertical SaaS because these platforms often already have something general-purpose AI tools don’t: deep context about how a specific industry works. They may understand the customer, the workflow, the data, and the sequence of actions required to get from one outcome to another.

That context could become one of the most important advantages vertical SaaS platforms have as software becomes more agentic.

How Does AI Change the Value Your Platform Can Create?

For years, one of the fundamental promises of software has been efficiency.

How can we save the customer time? How can we reduce clicks? How can we automate a manual task or make a process easier?

Those things still matter. But AI may allow vertical SaaS leaders to think about customer value more broadly.

During our discussion, Luke Sophinos framed the shift around a bigger set of questions: Instead of only asking how software can make someone’s work easier, what if the platform can help the customer grow revenue or improve profitability?

That changes how leaders evaluate potential AI opportunities.

Consider two possible AI features. One eliminates a few manual steps from an administrative task. The other identifies missed revenue opportunities and takes an approved action to recover them.

Both may save time. But the second is tied much more directly to an outcome the customer cares about.

The same thinking can apply across verticals. An agent could help fill an empty appointment, reduce inventory waste, follow up on an unpaid balance, identify a customer at risk of leaving, or trigger the next step required to complete a service.

The question becomes less about how much AI is in the product and more about what better outcome the product can create because of it.

Where Should a Vertical SaaS Platform Start With Agentic AI?

If the opportunity is this broad, it can be tempting to rethink the entire platform around AI. But that may be exactly the wrong place to start.

Vertical SaaS leaders already know another playbook: start with a wedge.

Many successful vertical SaaS platforms began by solving one important problem exceptionally well. They earned their way into the customer’s workflow, proved their value, and then expanded into adjacent capabilities.

Luke suggested applying similar thinking to AI. Rather than trying to make an entire platform agentic at once, identify one area where AI can create a meaningful customer outcome.

The test isn’t simply whether AI can perform the task. Ask whether doing it well creates meaningful value for the customer. Could it increase revenue? Improve profitability? Reduce costly administrative work? Accelerate an important process? Prevent a problem before it occurs?

Then determine whether your platform has the context, data, permissions, and controls required to take on that work reliably.

Start with one outcome. Prove it. Then expand.

How Should Agent Autonomy Expand Over Time?

Becoming agentic doesn’t mean jumping immediately to full autonomy. The amount of responsibility an agent takes on can increase as the platform demonstrates that it can perform the work reliably.

Think about that progression as crawl, walk, run.

In the crawl stage, AI may primarily interpret information, understand context, and assist the user. In the walk stage, it begins taking clearly defined actions with human oversight. Eventually, an agent may be able to coordinate multiple actions across a workflow within established permissions and guardrails.

The progression matters because the relationship between the customer and the software changes when the software can act. A customer is no longer only trusting the platform to store information or provide a recommendation. They’re trusting it to do something on their behalf.

That makes trust a product requirement, not just a brand concept. Platforms will need to think carefully about what an agent is authorized to do, when human approval is required, what happens when something goes wrong, and how customers can understand or control the actions being taken.

The goal shouldn’t necessarily be maximum autonomy. It should be earned autonomy.

What Happens When an AI Agent Can Transact?

As agents take on more of the workflow, another capability becomes increasingly important: commerce.

Many business outcomes eventually require money to move. A workflow might end with collecting a payment, issuing a refund, purchasing inventory, paying a vendor, renewing a service, or completing another transaction.

Embedded payments already allow many of those transactions to happen directly inside a software platform. Agentic commerce takes the idea further by allowing a transaction to become one of the actions an agent can take in pursuit of an approved outcome.

With embedded payments, the platform has the capability to facilitate a transaction. With agentic commerce, an agent may eventually determine that a transaction is the appropriate next step and execute it within the authority and conditions it has been given.

Consider a platform managing appointments. An agent could identify an opening created by a cancellation, contact an appropriate customer, fill the appointment, and facilitate the transaction connected to the booking.

In that scenario, payments aren’t a separate destination in the workflow. They’re one of the capabilities the agent can use to complete the work.

For vertical SaaS platforms already embedded deeply in their customers’ operations, that creates an interesting strategic question: If your platform understands the workflow and can take action within it, where should commerce become part of that action?

What Work Could Your Platform Start Doing for Your Customers?

The shift from vertical SaaS to agentic won’t happen all at once, and it doesn’t need to.

The more useful place to begin is with the work your customers are already doing. Map the workflow, identify the repetitive decisions and actions, and look for one outcome where your platform has enough context to take on more responsibility. Then prove it.

If the platform can reliably create that outcome, it earns greater trust. That trust creates an opportunity to expand into the next action, the next workflow, or the next outcome.

Over time, the value of vertical SaaS could increasingly come not only from the workflows a platform manages, but from the work it can actually perform within them.

Which brings us back to the question vertical SaaS leaders should be asking:

What work are your customers paying people to do today that your platform could start doing for them?

Find one valuable outcome. Prove it. Then do more.

FAQs About Putting Agentic AI Into Practice

Q. What types of work can AI agents take on in a Vertical SaaS platform?

A. AI agents may be well suited to repetitive work that requires interpreting information, making predictable decisions, coordinating steps, following up, or taking defined actions. The best opportunities will vary by industry and depend on the context, data, permissions, and controls available to the platform.

Q. How do you identify a good workflow for agentic AI?

A. Start by mapping how a customer accomplishes an important task from beginning to end. Look for repetitive decisions, handoffs, manual actions, and places where work stalls. Then identify where having the platform take on part of that work could create a meaningful customer outcome.

Q. Should a Vertical SaaS platform automate an entire workflow at once?

A. Not necessarily. A more practical approach may be to start with one clearly defined outcome, prove that the platform can deliver it reliably, and expand from there. This allows the platform to demonstrate value and build trust before taking on more responsibility.

Q. How should AI agent autonomy increase over time?

A. Autonomy can expand as an agent demonstrates that it can perform specific tasks reliably. Platforms can begin with assistance, progress to clearly defined actions with human oversight, and eventually allow agents to coordinate multiple actions within established permissions and guardrails.

Q. Where can payments fit into an agentic workflow?

A. Payments can become one of the capabilities an agent uses to complete an approved task or outcome. Depending on the workflow and the authority it has been given, an agent could eventually initiate or facilitate a transaction rather than requiring a person to complete that step manually.