The first observation in Adam Smith’s Wealth of Nations was on the division of labor. More specifically, how an economy, as it progresses, becomes increasingly compartmentalized into specialized divisions that produce a particular good or service. For example, a single shoe maker will not be as fast, or efficient as 10 shoemakers doing a particular part of the same shoe. Today’s division of labor is being supplied by advanced in artificial intelligence. The creation of a digital mind that can perform complex, human-like tasks is being implemented on a wide scale in enterprises. This has brought great gains in productivity, but also challenges in it’s usage.
Recently, a machine-learning program at JPMorgan just saved it 360,000 hours of interpreting mundane loan agreement. Google uses an AI program to save 15% on it’s energy expenditures at it’s data centers. These and many other examples are playing out in the world right now. Increasing productivity in organizations and thereby making them wealthier.
However there is also the ever-looming challenge of implementing the AI. Unlike a software program, the AI must be trained with huge amounts of data. If the data were corrupt, if the training algorithms were off, or the skills required by the AI don’t match the application for which it was designed, it could easily become a multimillion dollar mistake.
AI doesn’t solve everything, nor is it a technology to be dismissed. Like anything, it is a tool whereby we can use to our advantage, if we think about it carefully enough. If we see AI as a means, rather than an end in itself, business and organizations can bring about the new, 4th revolution that the internet for now has failed to deliver —apparently people feel more distracted than wise when watching compilations of cat videos or seeing the zillionth newborn baby on their Facebook feeds.