Now that I have grabbed your attention with sensational headlines, continue reading if you’d like to use AI for hiring transformation and talent management.
Over the decades, HR and Staffing have earned a bad reputation. The workers blame them for always siding with the management, while the management blames them for not adding any value. Not a great situation to be in, I say. So, how can AI supplement or become a strategic differentiator for HR functions?
I’ll use hiring and recruiting as my example. There are more, but then blogs need to be crisp and short 🙂
While traditional hiring tools rely on staffing and HR of the respective companies but any online job board (e.g. LinkedIn, Indeed, Monster and others) worth its salt would already be using some form of algorithms and data mining to sift through the barrage of resumes and highlighting the ones that match what the hiring manager or recruiter is looking for. Still, we hear complaints from the industry about not finding good talent and from workers about not finding jobs that match their skills and experience. It’s similar to the old saying – I have no room in my closet and nothing to wear!
No matter how many laws are erected about equal opportunity or checks-and-balances created within the system, people will find ways to circumvent them. One of my managers easily scooted around these policies and hired his buddies in juiciest roles. To utmost frustration of the team, HR & Staffing did nothing about it because he followed the process. In my career, I have seen such stories repeated over-and-over across companies and countries.
Now imagine, instead of the long-drawn hiring process of hiring, AI sifts through mountain of information and proposes three candidates that best match what you are looking for and the only choice you have is to select one of the three. I’ll not go a step further where AI selects and recruits a candidate who shows up at your door as per the expected start date! Proper use of AI will result in hires that are better fit for the role than any conventional process, no matter how rigorous.
So, here’s the hypothesis: use critical skills as weighted parameters but also match personalities and traits to the corporate and team culture instead of keywords in resume, hard skills and experience to the role. This means the machine learning algorithm will need to put appropriate weight to soft-skills, attitude and adaptability; not just hard skills. Integrating psychometric tests will make it easier for the machine learning algorithms and improve results as they would be more data-driven.
For deeper discussion on the topic, contact me.