3. Employee retention: training and mobility
Another effective way to retain great employees is to invest in them.
Excluding corporate dinners, which are always welcome if offered by the company (don’t you like to eat for free?), the best investment to motivate an employee is to offer him/her a path of professional growth.
Machine learning already showed its potential in boosting individual skill development, offering platforms that can give personalized guidance and training recommendations even without the necessity of human coaches.
The training programs can be tailored to the specific needs of each employee, taking into account their competencies, careers, the company’s needs, market trends, and many other factors.
What about promotions?
Once employees are properly trained and have successfully demonstrated their skills and performance, AI-based software can evaluate these results and suggest which ones should be promoted.
This means influencing internal mobility without any typical human bias, a problem we have already addressed in the section on recruiting.
So… things like offering coffee to the HR manager will no longer work.
AI to forecast future resignations
However, AI technology is not limited to evaluating promotion possibilities.
It can also predict team members’ discontent (for example with surveys or the previously mentioned behavioral analysis) and consequently their willingness to leave the company.
This allows HR managers to act before it’s too late, setting some retention efforts such as offering additional benefits. Or, once again, a promotion.
4. Algorithms for administrative automation
The last AI application I’d like to talk about was actually one of the first to be implemented in HR: automating administrative and bureaucratic tasks.
Why did I leave it for last? Cause scheduling is boring.
Even HR managers think so, and that’s why they’re so happy to delegate such tedious and time-consuming activities to AI-powered systems.
By automating easily repeatable administrative tasks (such as interview planning, candidate screening, benefits administration, performance review), AI allows HR managers to save time and focus more on the overall strategic planning of the company.
For example, research conducted by Eightfold discovered that HR departments using AI software managed administrative tasks 19% more effectively than departments that don’t use AI.
We still need humans… and good data
Let me conclude today’s article with two brief considerations.
The first one is that ML and DP technologies are extremely data-driven. They can show amazing results, it’s true. But they still require high-quality data to function properly.
If, for example, we want to have a good idea of the employee experience in our company, we need to make sure that we collect truly relevant data about the staff, including employee turnover, satisfaction level, performance, and more.
According to IBM estimates, only 25% of companies that began investing in such information analytics initiatives have actually managed to improve their earnings.
That’s because their analyzes are based on low-quality data. Counting American companies alone, this costs more than $ 3,000 million annually.
Only 25% of companies that began investing in information analytics initiatives have actually managed to improve their earnings. That’s because their analyzes are based on low-quality data.
No need to exaggerate with ML
A second point I’d like to emphasize concerns the undisputed need for human interaction even when ML-based systems could potentially take care of everything by themselves.
Indeed, overly automated experiences risk being more frustrating than helpful, for example, during recruiting. Randstad’s research about this topic showed 82% of respondents were annoyed by such situations.
Moral of the story: machine learning should be used to spice HR up, not to make them ultra spicy.
Need some spice? Take your HR manager to eat Mexican.
And make him pay the bill. He deserves it!