Why is the gap between companies’ artificial-intelligence ambition and their actual adoption so large? The answer is not primarily technical. It is organizational and cultural. A massive skills-and-language gap has emerged between key organizational decision-makers and their AI teams.
The problem is that most executives are selected for their ability to talk to other people. Those who develop machine-learning solutions to business problems are selected for their ability to talk to machines. These two groups cannot, do not and will not speak to each other in productive ways.
We need to bridge this gap. Organizations need people who can talk to both people and machines, and they need people in their upper echelons who specialize in talking to machines.
The current lingua franca of business is part of the problem. The proliferation of economists in business school faculties since the 1960s has contributed to the production of a common language system that executives use to plan their actions and justify their decisions. In an age where competition depends on algorithms and massive, distributed data sets, this language is inadequate.
To catch up, companies need to change how they communicate and how they frame problems. They need to offer their nontechnical executives training in computational and algorithmic thinking.
Equally important, organizations must develop the relational and communicative skill base of their technical team members. Functioning competently in a top management team or board meeting is about much more than accurate reporting, valid reasoning, critical thinking or decision-making. It is about finding successful modes and means of expression. These so-called soft skills are among the hardest to develop and wield. But they are as important for technical employees as for anyone else.
AI strategies fail because AI is a means, not an end. But for companies to get past the hype and focus on the real potential that AI offers, they’ll have to start with how they communicate.
