How Paylocity Is Building for the AI Era Without Losing the Human Element

Paylocity has been steadily expanding beyond its payroll roots, adding new products, services, and AI capabilities around a broader vision of one connected workforce platform.

For Melissa King, who leads Paylocity’s roughly 1,400-person product and technology organization, the opportunity is about removing low-value work so people can apply more judgment where it counts.

That philosophy shows up in Paylocity’s product strategy, its growing services business, and even the discipline that comes with operating as a public company.

When Melissa King joined Paylocity nearly two years ago, she brought a career spent building software across product, engineering, operations, revenue strategy, and marketing.

That range has shaped how she thinks about technology, especially as Paylocity rolls out Ignite, its AI strategy, alongside Elevate, its expanding managed services offering.

At first glance, those might seem like two different bets. One uses technology to do more work. The other introduces more human expertise.

Melissa sees them as complementary.

“Clients shouldn’t have to choose between better technology and better support,” she said. “They need both.”

That idea became the through-line of our conversation.

Better technology still needs better support

Much of the AI conversation in HR has centered on how much administrative work can disappear.

Melissa agrees that plenty of it should.

Searching for information, reviewing large volumes of data, chasing routine follow-ups, and moving work between steps are all areas where AI can take on more responsibility.

Paylocity’s Ignite AI capabilities are designed to handle work that is repeatable, data-rich, and pattern-based. The system can identify an issue, explain what is happening, and increasingly take action inside the appropriate workflow.

But there is a limit to what technology should own.

That is where Elevate comes in.

“Elevate is what brings the expertise, the context, and the judgment that the technology cannot fully own,” Melissa said.

The combination points toward a different operating model for HR teams.

Software handles more of the predictable work in the background. Experts step in when the situation requires experience or context. The customer remains in control.

Melissa used recruiting as an example. AI can summarize qualifications, identify potential candidates, assess alignment with a role, and surface strong candidates already sitting in the database.

That gives recruiters more time to actually recruit.

“The goal isn’t in that scenario to automate the hiring decision,” she said. It is to create more room for “meaningful conversations, thoughtful evaluation, and really building those relationships.”

For HR teams that are already stretched thin, the best outcome from AI may be changing where their time goes.

Why Paylocity keeps expanding the platform

That same philosophy helps explain Paylocity’s expansion over the last several years.

The company has moved well beyond its historical identity as a payroll provider, adding capabilities across areas such as retirement, leave management, workforce planning, recruiting, and global payroll.

Melissa sees a simple reason for that expansion: the work itself is interconnected.

Workforce planning affects recruiting. Recruiting feeds onboarding. Labor decisions have financial implications. AI increasingly spans all of it.

“Clients shouldn’t have to stitch together data and systems and processes to get work done,” she said.

For Paylocity, that creates a reason to expand the definition of the platform.

The company does not rely on one method for getting there. Melissa described a constant evaluation between building, buying, and partnering.

The decision starts with the customer problem.

When Paylocity sees a company already solving an important problem particularly well, acquisition can make sense. Partnerships can work when another provider has deep expertise and the experience can be tightly integrated. And, as Melissa joked, product teams will always love building things themselves.

Regardless of the route, the standard is the same.

An acquired product cannot simply share a logo with Paylocity. The experience eventually needs to feel native.

That becomes increasingly valuable as AI begins operating across workflows instead of sitting inside individual modules. An agent is much more useful when the data, permissions, and actions it needs are connected underneath it.

The human is more than “in the loop”

The conversation got especially interesting when we discussed how AI is changing the work of software developers themselves.

AI coding tools can now prototype interfaces, generate code, accelerate requirements work, and compress parts of the development cycle that once took far longer.

That speed comes with a new challenge.

If an AI agent can grapple with more of the problem, what exactly is left for the human?

Melissa pushed back on one of the most common phrases used to answer that question: “human-in-the-loop.”

“It makes me bristle a little bit,” she said.

The phrase is generally intended to reassure people that a human still has oversight. To Melissa, it can also make the person sound passive.

“The human-in-the-loop isn’t sitting on the sideline. It’s not an FYI. It is an active role, not a passive role.”

That active role depends on expertise.

Someone can feed prompts into an AI system, but without critical thinking and experience, they are giving up the part of the job where humans provide the most leverage.

“You can do so much more, but don’t delegate and don’t diminish your critical capacity,” Melissa said.

As HR software becomes more autonomous, the practitioner’s job increasingly moves toward judgment, oversight, and deciding what should happen when the answer is not obvious.

AI can increase an employee’s capacity. The human still has to know what to do with that capacity.

Accountability is not the enemy of innovation

Paylocity is also operating through this transition from a somewhat unusual position in today’s HCM market.

A number of large HR technology companies remain private, while others have moved out of the public markets. Paylocity continues reporting its performance every quarter.

The common argument for staying private is freedom. Companies can make longer-term bets without as much scrutiny around quarterly performance.

Melissa has worked in both environments.

Her perspective is that the difference is not as simple as it can appear from the outside.

“The honest answer is to transform in both settings requires strong financial performance every quarter,” she said. “While maybe not publicly reported, you still have that pressure.”

What changes in a public company is the visibility.

Paylocity cannot stop delivering while it modernizes its platform, integrates an acquisition, or builds a new generation of AI products. That forces the organization to be deliberate about sequencing investments and demonstrating customer value along the way.

Melissa sees that pressure as useful.

“I don’t see that level of accountability as the enemy of innovation,” she said.

A product can look impressive in a demo. In payroll and HR, it also has to be reliable enough to become part of a business-critical workflow.

That bar rises as AI takes on more responsibility.

Customers need to know how their data is protected, who has access to what, and what happens when an AI capability moves from experimentation into everyday operations.

For Melissa, the controls and transparency expected of a public company can become an advantage in that environment.

“Being impressive is not enough,” she said. “It’s got to be reliable, it’s got to be responsible.”

Building for an HR team that already has too much to do

One of Melissa’s biggest surprises after entering HCM was seeing just how resource-constrained many HR teams are.

She recalled meeting a large organization where an HRIS team of four people was supporting nearly 20,000 employees.

“I thought I really understood that challenge,” she said. “I did not.”

That experience provides useful context for everything Paylocity is building.

HR teams do not need AI because AI is exciting. They need help absorbing an enormous amount of operational work without adding equivalent resources.

The opportunity is to give those teams more capacity while preserving the judgment required for payroll, compliance, recruiting, workforce planning, and other consequential decisions.

That is why Paylocity’s AI strategy and services strategy increasingly converge.

AI handles more work. Human expertise becomes more focused. A connected platform gives both sides the context they need.

And the person is still doing far more than sitting “in the loop.”

They are responsible for deciding what good looks like.