Man-Machine Collaboration (HR Trends 2017, 4)

Not Man* vs. Machine but Man AND Machine

This morning Xing mailed me the link to an article with the title “Mensch oder Maschine: Wer trifft bessere Entscheidungen?” (Men or Machine: who makes better decisions?”). This is putting men against the machine. I think a better question is: “Wie können Maschinen Menschen helfen bessere Entscheidungen zu treffen?” (How can machines help people to make better decisions).

Three examples in this area.

Uncovering hidden talent

In McKinsey Quarterly of January 2017, Kevin Lane, Alexia Larmaraud and Emily Yueh wrote an interesting article: Finding hidden leaders. They argue that the current talent identification practices are not adequate and biased, and that we should explore alternative ways to detect talent.
Talent identification is clearly an area where technology can help a lot to improve the process. Hopefully we will soon see the end of the traditional succession management and talent identification processes, as they are common in many organisation. Man, in combination with machine, and machines learning from people, can make sure hidden talent is uncovered.

Dirk Jonker of Crunchr told me about a recent experiment in this area. The MD officers of a multinational were asked to take a pile of cv’s of employees and rate them to what extend they were considered to be candidates for certain positions (1=highly unlikely, 5=highly likely). The results were fed into the computer. The next step: the computer was asked to look into the HR information system, and suggest candidates that were not in the initial pile, but were comparable to the profiles of the people with high ratings. MD Officers and machine working together to create a richer succession bench. It is still early days, but the signs are there that artificial intelligence will enable HR to increase their impact in various areas.

Improve quality in call centers

An interesting are is Behavioural Matching. This is the process where people are paired based on the predicted interpersonal behaviour, using clever technology. One of the leading companies in this area is Afiniti. I quote from their website: “Our proprietary “big data” algorithms analyse client and third-party information to identify patterns of successful and unsuccessful behavioural interactions. We then apply these patterns in real time to augment human pairings and their associated commercial outcomes”.

The customer who calls the call center with an issue is paired with the available call center agent of whom it is predicted that he or she will have the most productive interaction with the caller (E.g. the older man who calls from a rural area is matched with a senior call center agent, who comes from the same region). The use of enterprise behavioural matching software works both ways: the customers get better services, and the call center agents have more rewarding jobs, as they get to handle customers they can deal best with. Man and machine improving the experience of customers and employees. Of course,  these types of solutions are also easily applicable in the HR domain (HR services center, in recruitment).

Improve Diversity and Fairness in the workplace

We are all prone to unconscious bias. We give the nice challenging assignments to our favourite team member. We prefer the candidate who comes from our home town. We do not trust people from “strange” countries. The machine can help us to overcome unconscious bias. I came across an interesting company Joonko; they provide a software solution that collects data from the organisation’s SaaS tools, and uses this information to detect unconscious bias. Consequently, they give feedback and suggestions to managers, in a gentle way. I have not seen it in action, but it sounds very promising. Again: man, and machine collaborating to improve diversity and inclusion in the workplace.

*: Where we write Man, we mean Woman and Man

 

Illustration: Studio Fee Overbeeke