AI versus People Analytics: How AI is destined to change People Analytics

By Konstantin Tskhay, Ph.D. 

The world has changed.  

Over the last 6 months, we have seen an emergence of AI technology taking the average consumer by storm. Its applications do not stop at asking ChatGPT to provide us with a definition for a word we could find on Google anyway. AI can code, AI can create decks, and AI can integrate multiple images to create our LinkedIn headshots.  

The applications of AI are vast 

And new applications are emerging daily—life and work as we know it is changing.  

Indeed, Bain & Company recently released a study estimating that up to 40% of management jobs will be disrupted by AI. This means tasks you hated doing will disappear and become streamlined, allowing you to create more space for strategy, reflection, and thinking about improving businesses.  

This, of course, creates an interesting conundrum for People Analytics.  

On the one hand, People Analytics is a very, very new discipline designed to process data for strategic decision-making about human capital. On the other hand, because People Analytics is all about data; and AI can process more data and do it faster.  

So, what is the role of the People Analytics function, then? 

Think about it:  

As an executive, I don’t need to go to my people analytics leader and ask them for a dashboard to understand my turnover. I can ask my AI engine:  

“Draft a voluntary turnover chart using the data in our HRIS, annualize it, and create three scenarios for turnover for the rest of the fiscal year. Once done, break down the data by gender and level to help me understand whether we are losing people in certain groups more than others. Create graphs and interpretation of results in plain English and make action recommendations.” 

Voila.  

You look at your screen, and after a few seconds (or maybe minutes) of thinking, your chat produces a short, straight-to-the-point overview of turnover. With Graphs! With recommendations! 

Even more incredible is that your board of directors has access to the same technology, asking the same exact questions simultaneously, helping them advise you continuously versus reacting to what is presented in the quarterly meeting.  

What is the role of People Analytics, then? 

• Connect datasets? Data engineering can get this process done.   
• Analysis? AI is running all the predictive models and evaluates them.  
• Data storytelling? Perhaps, but here we are looking at consulting rather than technical skills.  

AI becomes your Technician. It takes the technical work off the hands of the People Analytics team.  

So, what is left then? 

Technicians can’t work in a vacuum; they need two other roles: direction and management.  

Thus, we are left with Entrepreneurs and Managers. Or put together product managers. These are the people who oversee the work of AI, ensure the high quality of the inputs and outputs, and think about new ways to use AI to drive business outcomes via strategic and effective deployment of your people.  

They are less interested in technical specs and more focused on the customer—the business.  

The role becomes less technical and less focused on the grind of delivery. It’s built around research, exploration, and entrepreneurship. It starts to attract different personalities. People who want to disrupt the status quo and create new solutions. It becomes a playground where all ideas are considered, and the best ones survive.  

But as we ponder this beautiful new future, can we still call this work People Analytics?  

Or should we call it something else?  

No, I don’t think that AI will exterminate People Analytics as a function.  

I do think that it will change it into something completely different from what we see today.