"People ask me to predict the future, when all I want to do is prevent it. Better yet, build it. Predicting the future is much too easy, anyway. You look at the people around you, the street you stand on, the visible air you breathe, and predict more of the same. To hell with more. I want better." ~ Ray Bradbury, from Beyond 1984: The People Machines
Earlier this year we launched a conversational AI solution to better the overall candidate experience during the recruitment process and specificially to facilitate better and quicker interview scheduling. Post launch we were reaching out to candidates to see how the experince went. In one such conversation the candidate said the process was quick, but there is something that was fascinating about the overall interaction with the chatbot. "The chatbot communicated with me in french, and the interesting aspect is that I know french". This sent us seeking for answers, the default language we had set was english so how did the chatbot decide to communicate to this specific candidate in french?. It was not the error but the accuracy of the error that sent us seeking answers.
The answer was simple, the candidate had mentioned french as a primary language in his application form and even though all of his communications with us was in english, this was a detail the chatbot was able to pick up. The answer ended up being much simpler than what we anticipated, but it gave us a perspective on what this technology can do for the future, the level of details that AI can get at scale, and the possibilities and opportunities with that. Another discovery for us was just the patterns of data AI can generate, we found most interviews scheduled on mondays were recheduled, a simple data point which informed us to think how we can change the candidate experience by fixing this. Again these are simple insights but impossible to get without technology.
The Target story captured in the book The Power of Habits by Charles Duhigg captures how AI uses data really well. This story that garnered national attention began with an irritated father entering a Target in Minneapolis talking to the store manager, complaining about the store sending his daughter a sale booklet for baby clothes, cribs, and diapers even though she is still in high school. Shocked and surprised the store manager apologized to the angry father. Yet, the same store manager received a call from the same father weeks later to find out the father had a talk with his daughter and discovered she was indeed pregnant. Target knew this (based on their pregnancy prediction score - simple algorithm that matches purchases of pregnancy test kits with follow up purchases of folic acid) before she even told her mother and father.
In the above examples, the solution to complex problems are found by algorithms finding simple solutions by combing through huge data sets, and finding patterns. Investing in technology and nurturing it to find more solutions and supplementing this with human intelligence can help us to solve so many issues that seem to be unsolvable today. There is a world in recruitment where I clearly see that positions will apply to individuals, rather than individuals applying to positions and AI will be a key partner to acheive this. Bob Dylan says this in the best way possible... For the times they are a-changin
