The year 1995 is often heralded as the beginning of the 'new economy' triggered by digital communications that was set to upend markets. Today, over two decades later, the net impact of the digital era, in economic terms, can be said to be a reduction in the cost of search, communications and a variety of transactions. The cost reduction in turn led to a boost in the volume of searches, communications and certain transactions.
The buzz being heard presently about cognitive technologies (aka Artificial Intelligence ) is in many ways similar to the earlier buzz about digital communications technology. All technological revolutions involve, in economic terms, the cost of some activity becoming cheap. Artificial Intelligence is, in essence, centred around prediction technology, so the significant economic impact will be a drop in the cost of prediction.
When the cost of any input falls significantly, two things usually happen. Prediction currently is an input to a host of activities which include transportation, healthcare, agriculture and retail, among others. With the drop in costs, predictive technologies will be increasingly applied to domains where they weren't being used earlier. Together with this increased application, the value of other things that complement prediction will rise.
As an example of these new domains, consider navigation. Till recently, autonomous navigation was restricted to highly controlled environments like warehouses and factories where programmers could anticipate scenarios and build 'if-then-else' decision algorithms accordingly (e.g. 'If object approaching sensed, then slow down, else continue at set speed'). Once the cost of predictions fell, innovators simply reframed driving as a prediction problem. Rather than program endless 'if-then-else' decision algorithms, AI was simply asked to predict what a human driver would do in a huge number of different scenarios. Vehicles were outfitted with a variety of sensors like cameras, lidar, radar and others and went around collecting millions of miles of human driving data. By linking the environmental data collected from outside via the installed sensors with the driving decisions made by the human inside the car, AI learned to predict how humans would react ( braking, accelerating, steering, stopping ) to a large variety of environmental conditions prevailing at any instant outside. Thus, prediction technology has now become a major component of the solution to a problem which was earlier not considered a prediction problem at all.
As AI becomes mainstream, the value of human prediction skills will decrease simply because machine prediction will provide a cheaper and more accurate substitute for human predictions. However, such a scenario may not spell doom for human jobs, as several expert projections seem to indicate. That is because human judgement is a complement to machine prediction and hence, when the cost of machine prediction goes down, the value of human judgement would be expected to rise. Management would thus seek out more human judgement in such a scenario.
A survey of nearly 1800 managers from 14 countries together with inputs from 37 executives who are in charge of digital transformations in their organizations, helped identify five practices that managers need to master as disruptive technologies make major inroads in their workplaces. Briefly put, these are:
1. Leave Administration to AI
Managers have been seen to be spending over 50% of their time on administrative control and coordination activities. AI will automate most of these tasks and relieve managers of the responsibility of having to carry them out.
2. Focus on Judgement Work
Many decisions require insights beyond what AI can arrive at, based on data and prediction algorithms alone. The application of experience and expertise to critical business decisions and practices is the essence of human judgement. As stated earlier, the value of human judgement will rise as the cost of machine driven predictions go down.
3. Develop Social Skills and Networks
In a world where AI carries out many of the administrative and analytical tasks that managers perform currently, the social skills critical to networking, collaborating and coaching will help managers to add value and stand out.
4. Work Like A Designer
As AI takes over more & more of the administrative and analytical tasks, manager-designers need to bring together their own creative ideas and harness the creativity of others to come up with integrated, workable and appealing solutions to problems.
5. Treat Intelligent Machines as 'Colleagues'
There is no need to race against 'intelligent machines' or treat them as competitors. Managers must recognize the fact that AI machines can greatly help in decision support systems and data-driven simulations as well as help in search and discovery activities. They should learn to value the advice of AI machines while making decisions based on their own 'judgements'.