The workforce of the future, what I call the AI Augmented Workforce, will be very similar to self-driving cars in that both will be connected to a network that takes in data and pushes out knowledge.
Engineers are relying on Artificial Intelligence (and a host of really advanced support technologies like cameras and lasers) to be the “powered by” technology that undergirds self-driving cars. The technology that allows a self-driving vehicle to drive itself through a specific (and known) town, like for instance Mountain View (where Google is located), already pretty much exists. In fact, you can visit Mountain View today and encounter little Google cars driving around town with an engineer sitting in the passenger seat. What doesn’t exist yet is the technology that will enable thousands of cars, or even a single car, to drive anywhere and at anytime. What many people don’t understand is that once one car can drive anywhere and at anytime then so can millions of cars. The issue of scale is not one of better “mechanics” but one of better “awareness”.
One of the hardest parts of teaching a young person to drive is that of teaching them the “mechanics” of driving. In other words, how to start the car, how to push the pedal to gain and decrease speed, how to use the turn signal, etc. What is much less of a challenge (hopefully) is teaching them that they should put on the brakes and stop the car if a dog runs out in front of them, or that snow is white and causes ice and ice is slick so they should slow down, or that lights reflect off of water and so when it is raining lights will reflect off of the road. These are all things that humans, to a large extent, already “know” because we have learned them over a lifetime of experience. Computers don’t and haven’t.
Today AI systems know the “mechanics” of driving. The AI system can start the car, drive forward, backwards, park, increase and decrease speed, work the turn signals, etc. Mechanically the technology can do anything and everything that we as humans can do and actually do it much better. But it is still learning about the “world” around it.
Cars, robots, software on mobile devices and machines are just nodes in the network of what is in effect a super brain that is constantly collecting real time data, learning from it and then sharing (training/teaching) the lessons learned (knowledge/expertise) with all of the nodes connected to the network. Or to put it even more succinctly, once the central “brain” learns it then all of the nodes connected to it automatically learn it. They are one and the same. So every mile and every “experience” that every car (“node) has is in essence a shared learning experience. And every new node (i.e. car) that connects to the network instantly receives that knowledge. The systems learning curve is logarithmic. So what do self-driving cars have to do with your workforce?
In an AI Augmented Workforce these same underlying dynamics will be at play. Imagine how this will affect, for example, your sales force in the field? The implications are mind boggling. In the near future each of your sales team members could be equipped with an AI enabled mobile device that connects each sales person to the network and works, in real time, to help them close more sales. In addition, each new sales person that you hire is immediately equipped with all of the best practices and product knowledge that every single sales person in the network has “learned” over time.
Sales software already does some of this today. But today the software is passive. It waits to be queried instead of being an active participant in the sale. In addition, it requires that each sales person actively document what happens with each customer. On the other hand, AI Augmentation Software will be a proactive partner in the sale and will be collecting data gathered through voice and facial recognition during the sale and uploading that in real time to the network. It will provide suggestions (prompts) to each sales person in real time as they converse with the sales prospect based on the objections, questions and other verbal and non-verbal cues that the prospect provides – and of course taking into account everything else in the system including customer profile, time of day, time of year, buying history, etc. And information that is gathered during that specific sales call will be fed back into the system, turned into knowledge, and then shared across the network in real time. And similar to self-driving cars the “knowledge and expertise” of the network will improve with each customer interaction.
So imagine that you have 1,000 sales people across the country all calling on customers at the same time. The AI Augmented Software might detect commonalities in objections that customers are raising in a certain geographic region. Trying to find a way to help its sales “partners” close the sale it will begin searching through its information trove, as well as across the unstructured data of the web, to find potential reasons. At the same time it might begin prompting the sales people facing these objections to ask certain questions. Eventually it might discover that certain blogs have been reporting that one of your competitors is on the verge of a new product upgrade and a few local newspapers in that region had recently ran stories about it which raised doubts about your product in your sales targets minds. Worries that they had not specifically raised during the sales call. The AI Software would alert the sales people to this fact, in real time, at which point they could address the issue with the customers. Sale closed!
This type of AI Workforce Augmentation will happen across your enterprise.