Two Data Points and Four Key Points to Remember When It Comes to AI

Machine Learning


The upheaval in artificial intelligence (AI) has taken many business leaders by surprise. Impressed by generative AI systems such as ChatGPT, executives are clamoring for the technology to generate and capture business value. By the end of 2026, 80% of companies are expected to be using or developing some kind of GenAI product or service.

While 80% is a high number, it's not surprising when you consider what AI-based systems have accomplished in the past two years alone: ​​they have solved decades-old unsolved math problems, excelled on the bar exam, and diagnosed illnesses with lower error rates than human experts.

But successfully adopting AI in the enterprise is not easy. In fact, it's estimated that 80% of AI projects are doomed to failure. The dilemma facing business leaders today is clear: how do you leverage AI to create and capture value without being abandoned like the vast majority of projects?

My answer to this question has four parts. First, thoroughly understand what the technology is and how it works. Second, clearly identify the specific problem you want to solve with AI, and thereby the value it will bring. Third, pinpoint exactly what data you need to feed the AI ​​so that it can effectively address the identified problem. Fourth, anticipate and manage the reactions of people in your organization. My advice to executives is divided into these four steps:



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