Machine Intelligence has made significant advances recently in areas of supervised learning such as image and speech recognition, question & answering and shopping recommendation.
These so called “weak A.I.” apps are expected to transform how consumers search, socialize and shop. Consumers are increasingly likely to ditch the current type & swipe, folder based apps on devices to a much more organic and easier to use voice based personal assistant interface – think Amazon Alexa/Echo in the near term migrating to some semi realized version of Ironman’s Jarvis.
As A.I. powered apps reduce consumer friction and switching cost in commerce, it will trigger a wide range of disruptive implications on business value chains, especially in service based platforms that rely on consumer switching cost for lock-in.
The good illustrative example is Uber. The on-demand transportation and supply chain platform currently enjoys significant first mover advantage due to the relatively high customer switching cost (i.e. the cumbersome effort) of downloading multiple competing apps onto their already crowded phone screens with unclear benefits. The more consumers that behave this way, the harder it is for competing apps like Lyft scale up.
In the not too distance future where consumers regularly use natural speech to interface with an A.I. powered personal assistant app on their phones and devices, they can effortlessly switch between Uber, Lyft, Didi Chuxing, and limitless number of alternative apps. The A.I. personal assistant will simply works in the background to find the optimal option based on travel time, pricing, pick-up dynamics, etc. This reduction in consumer switching cost will significantly erode any first mover advantage enjoyed by Uber.