As is usually the case with fast-advancing technologies, AI has inspired massive FOMO , FUD and feuds. Some of it is deserved, some of it not — but the industry is paying attention. From stealth hardware startups to fintech giants to public institutions, teams are feverishly working on their AI strategy. It all comes down to one crucial, high-stakes question: ‘How do we use AI and machine learning to get better at what we do?’
More often than not, companies are not ready for AI. Maybe they hired their first data scientist to less-than-stellar outcomes, or maybe data literacy is not central to their culture. But the most common scenario is that they have not yet built the infrastructure to implement (and reap the benefits of) the most basic data science algorithms and operations, much less machine learning.
As a data science/AI advisor, I had to deliver this message countless times, especially over the past two years. Others agree. It’s hard to be a wet blanket among all this excitement around your own field, especially if you share that excitement. And how do you tell companies they’re not ready for AI without sounding (or being) elitist — a self-appointed gate keeper?