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It’s increasingly difficult to have a conversation about HR technology without hearing the phrase “AI-powered.”
Despite rising AI mental fatigue, every vendor seems to be racing to introduce the latest copilot, agent, assistant, or automation. Yet amid all the buzzwords, organizations are asking a simple question: How do we know when AI is actually making things better?
At PrismHR, that question drives every conversation about artificial intelligence. Whether it’s automating workflows, whether it’s improving efficiency, whatever that task is that we’re trying to solve for, intentionality is fundamental.
It’s so rooted in our philosophy that we use the term “Intentional Intelligence.”
While much of the industry focuses on AI as a headline, PrismHR focuses on AI as a tool. Our tools should solve real business problems, improve user experiences, and strengthen operational efficiency without sacrificing trust, accuracy, or accountability.
PEO and CHRO leaders have heard enough promises about what AI may do someday. What matters now is how a technology provider decides where AI belongs, where it does not, and what safeguards sit between promising prototype and trusted workflow.
“When it comes to artificial intelligence, it’s tempting to let the technology become the focus,” said Dr. Sami Mian, Chief AI Officer of PrismHR. “But nothing about AI changes what makes a product trustworthy — it still comes down to whether the people building it started from the problem, tested what they shipped, and left a way for a person to catch a bad result. AI raises the stakes on all three. It doesn’t replace them. Applied honestly, that discipline sometimes tells you to use less AI, not more, in order to deliver a superior product.”
What Does AI Governance Actually Mean?
Ask 10 technology companies to define AI governance, and you’ll likely get 10 different answers.
For PrismHR, governance goes beyond policies. It’s about making responsible decisions before, during, and after AI enters a workflow.
Adam Van Beek, Chief Innovation Officer at PrismHR, believes one of the most important AI governance decisions happens before any code is written.
“We want to make sure we’re just not building them to build them to say we have AI,” Van Beek explained in a recent interview. “We’re making sure it makes sense.”
That means evaluating every project through a practical lens, asking questions like:
- Does AI solve a meaningful customer problem?
- Does it improve efficiency or accuracy?
- Does it create a better experience for payroll and HR professionals?
- Can it be delivered securely and responsibly?
- Is it economically sustainable?
The answer isn’t always yes… and that’s exactly the point.
Why Responsible AI Sometimes Means Using Less AI
One of the most interesting lessons PrismHR has learned is that AI maturity isn’t measured by how much AI you use. Sometimes it’s measured by how much you remove.
Van Beek unpacked the company’s payroll audit initiative as a perfect example.
The original goal was straightforward: help payroll professionals quickly understand what was happening within a payroll run by bringing critical information into a single view and generating intelligent summaries. Initially, the team built the solution using generative AI.
“We were like, ‘This is awesome. This is great. We love it,’” Van Beek recalled.
But instead of stopping there, the team challenged itself.
Could the same outcome be achieved more efficiently?
Could the solution scale better?
Could customers receive the same value with lower operational costs?
The answer was yes.
“We ended up rewriting it, I don’t know, five or six times,” Van Beek said. “And then we came up with this new report which was now 99% driven by code.”
The team ultimately used AI to help generate and improve the code itself while significantly reducing the need for AI during runtime.
“You just kind of rebuild, build, rebuild, reorganize, and rethink,” Van Beek said, noting that client feedback gets incorporated into the iteration loop.
The lesson was simple: Sometimes AI is the product, but sometimes AI is the accelerator that helps build the product. And sometimes AI is the framework that gets iterated to give clients what they’re looking for.
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The Best AI Is the AI You Barely Notice
That philosophy extends beyond product development.
Mike DeLessio, President at PrismHR, believes customers shouldn’t need to become AI experts to benefit from AI.
“AI is in our DNA,” DeLessio said. “It’s a common thread that runs through our applications.”
But that doesn’t mean customers want AI to become the focal point of every interaction.
“If you hear that terminology too much, it can do a couple different things,” DeLessio explained. “If you’re not technologically literate, AI can be scary.”
Many business leaders are already navigating concerns about:
According to DeLessio, customers don’t need more AI buzzwords.
They need better outcomes.
“Our model is more ‘show it,’” he said. “Show it how it works in the application rather than talk about the buzzwords and AI in a broader sense.”
In other words, the value should speak louder than the technology. When it comes to governance, this value-add can help ease adoption for hesitant clients and their employees.
That’s why PrismHR increasingly focuses on embedding AI directly into workflows where it can reduce friction and simplify work without requiring users to learn a new language or change how they operate.
Human-In-the-Loop Thinking: Why Human Oversight Still Matters
A common AI narrative suggests that the future is fully autonomous. However, as leaders in the human resources space, PrismHR sees a different future. A future where AI and human expertise work together rather than in competition.
“The whole key is AI leads, but it’s always a human in the loop,” said Robert Byers, PrismHR’s VP of Strategic Initiatives, while discussing future AI-enabled HR workflows. “That’s what all of our customers want.”
That philosophy becomes especially important in payroll. It’s one of the most sensitive business processes an organization manages. DeLessio emphasized that reality when discussing the future of AI in payroll operations.
“Our customers value the human-in-the-loop interaction because payroll is a tough thing,” he said. “Having AI go through and generate something there is viewed as risky.”
Instead, PrismHR believes AI should handle repetitive analysis, surface anomalies, identify variances, and provide recommendations, while PEO professionals remain responsible for reviewing, validating, and approving outcomes.
“Those critical components, the variances, fraud detection, things like that, the human in the loop is critical,” DeLessio said.
AI Governance Starts with Trust
As AI capabilities expand, security and governance become even more important.
According to Van Beek, one of the biggest challenges facing AI adoption is ensuring that advanced capabilities respect the same access controls and security standards that customers already trust.
“As a user, I have access to specific data and the agents will conform to that as well,” he said.
More importantly, PrismHR distinguishes between informational assistance and actions that affect employee records, payroll, or sensitive workforce data.
“Security needs to be rock solid,” Van Beek explained. “If it’s making a change, that accuracy needs to be 100%. We can’t guess on those things.”
That emphasis on governance extends into testing, validation, quality controls, and ongoing monitoring.
Because in payroll and HR, trust isn’t a feature.
It’s a requirement.
The Future Belongs to Intentional Intelligence
The conversation around AI often focuses on speed.
Who can launch the most capabilities fastest?
Who can automate the most work?
Who can create the most headlines?
We’re asking a different question: How do you create the most value?
| AI-first thinking | Intentional intelligence |
|---|---|
| Begins with the technology | Begins with the user problem |
| Adds AI wherever possible | Selects AI where it improves the result |
| Measures novelty | Measures value, accuracy, and usability |
| Treats automation as the destination | Uses automation to support people |
| Assumes the largest model is best | Matches the model or method to the task |
| Focuses on the launch | Continues testing and refining |
| Leads with buzzwords | Lets the workflow demonstrate the value |
The answer isn’t always another AI feature.
Sometimes it’s better automation.
Sometimes it’s a smarter workflow.
Sometimes it’s human expertise supported by technology.
And sometimes it’s using AI to discover a better solution before intentionally reducing AI’s role in the finished product.
“We’re using AI to understand our costs,” Van Beek said, “and when we’re building applications, we want to make sure we’re not building them just to say we have AI.”
That mindset captures PrismHR’s vision for the future.
PEOs, ASOs, and HR leaders deserve more than experimentation without guardrails. Responsible innovation means moving quickly enough to discover what is possible, while remaining disciplined enough to choose what should become part of the product.
It’s about delivering results people can trust. Not AI-everywhere, but AI where it matters.
