The Role of AI in the PEO Industry Circa 2019

by Frank Huang | October 15, 2019

Clients and prospects often ask about the status of analytics in the PEO industry. It seems like PEOs are bombarded from every direction with talk of “predictive”, “scalable”, and “robust” analytics. The hype has progressed so quickly that even “analytics” is a byword, now replaced with talk of artificial intelligence (AI) and machine learning (ML) being a panacea for all risk-related ills.

What is the reality?  

Are there huge swaths of the industry that are using “advanced” analytics, while the rest of us folks talking about it are left in the dust?

How much variance is there within the same segment of analytics? 

These questions and more will be the subject of this two-part article. In the first part, we’ll cover the spectrum of analytics out there (an attempt to cut through the buzzword fog) and then survey where the PEO industry is on that spectrum. In the second part, we’ll discuss how even within the same category of analytics there can be significant variance on model performance. We’ll also discuss the high potential of analytics in the PEO industry and how regulation may evolve over time.

Brief Primer on Types of Analytics

Generally, analytics can be divided into four types, from least to most complex:

  1. Descriptive – “Something happened?”
  2. Diagnostic – “Oh, right. Something happened… what happened?”
  3. Predictive – “This is going to happen again. I’m guessing it’ll happen… here.”
  4. Prescriptive – “If it’s going to happen here, let’s do something about it!”

All of the above types of analytics are useful, but the less complex types are more foundational and investigatory while the more complex types provide proactive information to effect decision-making. 

Where is the PEO Industry?

When I survey the PEO landscape, I see most PEOs in the descriptive and diagnostic end of the analytics spectrum. They utilize dashboards that can provide loads of information, and run univariate and bivariate analysis reports to monitor their business and stay abreast of changes. Most of the features in SaaS offerings are also generally on this end of the spectrum.

Of the various hurdles to adopting analytics, a material one is that management often does not fully understand the insurance mechanism, and thus does not appreciate the potential value from analytics. A more tangible hurdle is that PEOs may have more pressing technology concerns, like transitioning out dated platforms and/or technologies. In fact, three of the largest PEOs cited business transformation efforts and dated technology in their top ten most significant risk factors on their recent 2019 10-K.

While most PEOs are in the early stages of utilizing analytics, there are a good number of PEOs getting into or are already users of predictive and prescriptive analytics. Underwriting is the common entry point, and often driven by an upcoming event such as moving to a large deductible policy or as part of an exit strategy. There are plenty of instances, in and outside of the PEO industry, where a change to leadership more comfortable with analytics effects a cultural change for the organization, encouraging greater use of analytics.

It is also worth noting that as more and more investment from venture capital and private equity enters the market, I would expect greater investment into data and analytics. While the industry will continue to be led by first- and second-generation operators, we are starting to see influence from outside investment and their different perspectives. Regarding perspectives, the insuretech movement is a great example, with firms like JustWorks as a result.  

In Conclusion…

So, is AI taking over the PEO industry? Not to my knowledge. PEOs are still mostly utilizing descriptive and diagnostic analytics, with some progressing to predictive and prescriptive analytics. Will advanced analytics and AI be widespread in a few years? Possibly. But my hunch is that it will take time. If it does take time, does that provide opportunity for you to separate yourself from the pack? Absolutely.


  1. What is your view of analytics in the PEO space?
  2. How knowledgeable is your firm about the world of analytics?
  3. What are the hurdles in your firm for adopting greater use of analytics?
Frank Huang has more than 15 years of actuarial consulting experience serving a wide range of clients, including serving as ADP’s Chief Actuary.  Learn more about our PEO consulting practice here.