Some companies truly struggle to define and prioritize AI use cases. Good for them! The real problem arises for the many enterprises that grossly underestimate the challenge, feeling they have done the same thing many times before. After all, everyone went through Digital programs. Also, most have identified application areas for Big Data. A common assumption is that the methods for selecting successful AI use cases should not be all that different.

The results are failed prototypes, but more often
great initial demos followed by a quagmire of disappo- intments. For internal processes, AI deployment hits all kinds of practical implementation hurdles. Alternatively, scaling fails or the maintenance effort escalates. When AI is applied in offerings, glitches turn customers off, and the sales process draws out when customer data is involved or there is no willingness to pay. In all cases, the high hopes of value creation rarely materialize.

“In the past, a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their Internet strategy. I think five years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy.”

Andrew Ng

“A century ago, factories electrified without rethinking their production lines and therefore saw no productivity benefits. In much the same way, machine learning technology without management and organizational change will be ineffective.”

Erik Brynjolfsson, Director of the MIT Center for Digital Business