HR Isn’t Hiring Right
More bad thinking from folks claiming to understand human capital.
Background
I’ve written several articles on the sub-optimal and misinformed practices within the HR community that can be improved upon with HR Analytics. In a recent conversation with a good friend last week, I came across another one of these misinformed practices. My friend shared his recent conversation with an HR professional who was assisting his organisation to build out Generative AI capabilities. They got to talking about the profiles of the leadership team the organisation was looking to hire; this is where HR’s inputs should matter. The HR person shared that they were putting out a casting call for folks with deep experience in developing Gen AI solutions, and the way they’ve translated that (because none really exists) is to look for folks with PhD in Gen AI. “It’s a very technical subject, and we need someone with loads of technical expertise to really build out the capabilities roadmap.” (I’m paraphrasing here.)
This got me thinking. If organisations are building out new Analytics capabilities (including Gen AI and whatever that might come next), should they have the function head and seniors sourced from academia or start-ups, or perhaps as internal transfers from the business side?
And so I dedicate my 82nd article to unpacking the hiring strategy for emerging analytics capabilities.
(I write a weekly series of articles where I call out bad thinking and bad practices in data analytics / data science which you can find here.)
Create Utility
I’m reminded of the early days when organisations were building out their data analytics / data science capabilities. While the analytics discipline was rooted in Statistics and Computer Science, there was a lot of anecdotal evidence suggesting that those companies that were successful had analytics leaders sourced from the business side. These were typically senior business folks with some level of technical understanding and appreciation for the discipline. The reason for that success is because these “business folks” were able to define and shape the utility of data analytics, which was about positively augmenting the P&L, and less about the cleverness of the algorithms. In retail banking, the early analytics leadership typically comes from Cards & Loans, as these business rely on information for optimal performance. But dial forward to today, it would be unthinkable for an organisation to appoint a business person as a chief analytics officer. The discipline has matured and those with technical abilities understand the utility equation.
But Gen AI and Agentic AI don’t have the same utility equations as “traditional” analytics / AI. Gen AI and Agentic AI are pre-built solutions; they are tools. They are solutions looking for problems. Those in academia and start-ups simply don’t have enough exposure to the nuances of real organisation problems for which the AI solution can be applied to. We should therefore draw the leaders for this emerging analytics capabilities from domains that would benefit most. For example, I would argue that someone from Sales & Marketing is probably the right profile for Gen AI as they get into lots a content creation and summarisation; their work cannot be simply “templated” like Operations. And someone from a digital transformation background is probably the right profile for Agentic AI as there is a significant transformation agenda while dealing with huge uncertainties.
Re-Bottle When It Matures
I reckon it might take about 3–5 years from when an organisation starts down the path of Gen AI and Agentic AI application for the capabilities to mature sufficiently. At such point, they need to change out the leadership profile. This is what HR needs to be advising the business: hire someone who can define and drive the utility of the new emerging technology, and then transition to someone who can exponentially accelerate that utility once it’s defined and properly understood. It’s a 2-hander approach.
Conclusion
HR continues to make serious lapse in their hiring logic when it comes to building out capabilities based on emerging technology. I’ve experienced it in my early years of data science when the practice was still nascent. And I continue to see it with the new AI technologies of today.
HR needs to understand that the first generation of leaders of a new function is to define and shape its utility. And so, figuring out the profile that can best achieve that is paramount. And then understanding the change in profile that is needed, once the capabilities has matured, that will accelerate it to the next level.
