With the completion of every merger or acquisition, the electronic ink is barely dry for the Chief of Human Resources and their staff before they are called upon to integrate 500, or 2,500 or maybe 25,000 new employees into the new entity. The M&A transaction has increased the number of people HR is responsible for integrating in a pretty traumatic way. Even though they might have had the to-be-acquired company’s org charts for a while, it’s still a daunting task - it’s part art (aka experience + luck) and part science (aka …if only there was some).
For the Art part, there are several small and large firms that can help out with the process. But on the science aspect, it’s more difficult. How can science help you to figure out which talent to keep in a fair and consistent manner?
Imagine there was AI science that could help. What would it look like? One important characteristic would be that it could look at skills and qualifications of everyone; not just senior executives, but literally everyone - all newly acquired employees and all your existing employees as well. You would want it to be able to basically look at three or four areas to determine the best fit of that individual for your new or combined company. Even if you leave the acquired company as a standalone business (for example Tableau and the Salesforce acquisition), you’d still like to know what the skills are in the acquired talent pool and relate them to your existing organization’s skills and experience so you have a comprehensive view of talent across the merged entity.
You’d also want to make sure that you have a way of understanding the talent beyond just titles. Almost all organizations use their own unique titles with the result being that it is difficult to compare titles from one company to another, and many times even within companies! Your recruiting team could manually tag or label the resumes or profiles of your newly acquired employees and many companies allow their employees to self-tag their skills. But this approach is very labor intensive, time-consuming, and fraught with inconsistencies.
To that end, my own experience may be helpful. I remember in one acquisition, where I was looking at being in a position to recruit from the larger, acquiring, company to help staff our high-growth unit of the acquired business. I had literally thousands of technical staff to select from. As the VP and hiring manager, what I wanted to know most was which people had the skills we needed and hence which ones to interview. Unfortunately, we had to rely on titles which severely restricted our choices. We tried to apply a logic to the type of skills we were looking for. I had several of my team as well as a few other hiring executives attempting to classify the talent. It was an uneven, tedious and inefficient process.
Of the hundreds of potential candidates, my next concern was whether they knew about the work or could they actually do the work. We needed doers, which are people who know and can operate specific systems, rather than people who might have only superficial knowledge because, for example, they may have managed people who had the necessary knowledge and skills. Trying to assess this through resumes or job experience write-ups was very difficult unless you spent an inordinate amount of time manually sorting and correlating or actually interviewing everyone. In the end, it seemed more like we had to make a number of guesses with little basis to support the decisions in order to get the job done.
Thankfully today there is AI science that can help you do all of this and it’s called talent classification. It can provide guidance on what family of jobs a candidate is most suitable to, what skills they have and experience level. This can be done without use of titles. In M&As today, CHROs, HR integration and talent acquisition specialists, as well as hiring managers, can be more confident of being able to sort through the myriad of skills and experiences to be able to recommend the best people to the most appropriate jobs through the use of AI talent classification. In upcoming blogs, I’ll describe how talent classification works and how AI models can be easily built and tested to take the guesswork out talent management.
This is part 2 of an ongoing series where Michael Buhrmann draws upon his extensive experience as an executive to discuss how AI could impact the practice of M&A.