'AI strategies’ fail because AI is a means, not an end. “Do you have an AI strategy?” makes as much sense as asking, “Do we have an Excel strategy?”
Why AI Underperforms and What Companies Can Do About It
Why is the gap between companies’ AI 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.” It is a barrier that promises to stall, delay, or sink algorithmic innovations. And it is growing, not shrinking.
The problem is that most executives are selected for their ability to talk to other people. They have complex and well-honed abilities for listening, empathizing, deliberating, energizing, and de-energizing meetings, emoting and reading others’ emotional landscapes and adapting their ways of being to seemingly intractable social situations.
Those who develop machine learning solutions to business problems are selected for their ability to talk to machines. They write pseudo-code and code, develop large-scale platforms that scale to millions of users, aggregate data in multiple formats from multiple sources. They write interfaces for users that incentivize them to interact with the machines they build via combinations of words, images, colors, haptics, and action prompts.
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AI GONE WRONG?
