Experimentation to Execution: How AI Can Raise the Bar in HR and Payroll

AI has dominated business technology conversations for the past few years because the promised gains in productivity and efficiency are truly transformative. But a McKinsey report on the state of AI in the enterprise found that while almost all survey respondents are using AI tools in some capacity, most are still in the experimentation phase.

Nearly two thirds of surveyed businesses have yet to scale the technology across their organizations and execute in a way that delivers value. At the same time, technology is evolving rapidly. AI is a moving target, which is another challenge for business leaders who are looking for ways to move from AI hype to measurable results.

HR and payroll are emerging as proving grounds in the journey from experimentation to execution. Here’s an overview of some of the challenges HR leaders are facing at this critical juncture, why human oversight will remain critical and how to move forward in a way that delivers the most positive impact on the business.

The Unique Challenges of Applying AI to HR and Payroll Processes

In theory, the HR and payroll function is an ideal area to apply AI because there are many high-volume, data-intensive processes that require precision and efficiency. However, data doesn’t get any more sensitive than information related to employees’ health, job performance and pay.

Given that backdrop, there are two primary challenges facing leaders who want to deploy AI in the HR and payroll context. The first is data security. It’s simply an unacceptable risk to put personally identifiable information (PII) in a non-secured public AI environment.

HR and payroll AI functions need to run in a secure, HIPAA-compliant environment, not a public ChatGPT instance. That’s the first guardrail, and it’s non-negotiable.

Deciding how to apply the technology is the second challenge. AI tools are capable of labor-intensive tasks like conducting comparative analyses and scanning payroll runs for anomalies, but data accuracy is the key to success. There’s no room for error in the HR and payroll space due to the direct impact on employees. A 99% score earns an F in HR.

For these reasons, HR leaders need platform-specific expertise and a strong focus on governance to effectively apply AI in HR and payroll; general familiarity with AI theory won’t do. It’s also imperative to choose a platform that enables AI tools to learn from the organization’s own data, not just publicly available information from the internet.

Human Oversight Is a Critical Success Factor

As HR and payroll applications prove that AI can deliver measurable results, it’s becoming increasingly clear that effective AI strategies are built not just around governance and data integrity, but also human oversight, which is a critical success factor.

The best approach is to embed AI into real workflows using the organization’s own data, with humans validating AI analysis. This strategy helps organizations avoid the common pitfall of using AI as a standalone tool that pulls public data from online sources. That approach is risky because, even as its most enthusiastic evangelists acknowledge, AI isn’t 100% accurate and requires review as a guardrail to minimize risks.

The process involved in defining pay bands is a good example of an HR task that requires careful human oversight. Companies need competitive pay bands to attract high-quality candidates, and a number of states have salary transparency laws in place. It’s important to make sure the HR team is making decisions based on accurate data.

Several factors go into optimizing pay bands, including considerations about location. So, an HR team that relies on a ChatGPT-style platform that accesses publicly available data would be at a disadvantage if they were inadvertently basing salary levels on data from New York City when determining pay bands for Orlando, Florida.

When HR teams access a HIPAA-compliant platform with strong governance controls that base analyses on their organization’s own data, they can begin to demonstrate real results. But even then, the human element remains critical because accuracy is not optional in HR and payroll. So, the role assigned to AI matters.

Rather than asking AI to set pay bands or identify tax rates, HR leaders should use it for analysis that is then confirmed by humans and to create other tasks humans then carry out. For example, AI can generate reminders to ensure HR pays taxes on time and provide reports to users based on data from the system, not the internet.

Deploying AI To Create Value

One factor that makes AI unique is its incredibly rapid evolution. Because it is constantly learning and expanding capabilities, deciding where and how to deploy AI will always be like trying to hit a moving target.

One strategy that’s worth considering is for HR leaders to identify their top three to five time-consuming processes and determine how AI can help streamline those tasks. Help is already available in several forms, whether it be agentic AI that can be embedded into workflows to complete tasks, or an LLM model that can perform data analysis.

For example, software companies are starting to use artificial intelligence to significantly streamline the administration of employee benefits by acting as an intelligent translator between dense, complex benefit plan documents and the highly structured configuration rules required by Human Resources Information Systems (HRIS). Using advanced Natural Language Processing (NLP), AI can read through intricate legal contracts or benefit summaries to automatically extract critical data points — such as eligibility criteria, coverage tiers, deductibles and contribution limits. It then maps and converts these variables directly into the specific digital formats and logic that the HR software inherently understands. This automation transforms the traditionally tedious and error-prone process of manual data entry, enabling HR departments to implement annual plan changes, update compliance rules or roll out entirely new offerings with unprecedented speed, accuracy and ease.

This demonstrates a fundamental truth about AI capabilities as HR and payroll leaders move from experimentation to execution. Exciting possibilities like discussions between autonomous agents are on the horizon, and that will be a game changer, but ultimately, decisions will require human leaders to make the call.

As HR leaders build systems that center governance, ensure data integrity and integrate human oversight as an essential component, AI can shoulder the load when embedded in workflows, but humans will remain accountable. That’s how it should be as leaders use AI to raise the bar in HR and payroll performance.