For every business that’s using AI to do something groundbreaking, there are more that, well, aren’t. Adobe’s 2018 “Digital Trends” report found that while only 15 percent of companies are currently using AI, 31 percent have it on the agenda for the next year — but that rising demand doesn’t necessarily correlate with a rise in high-quality AI products. Much of it is fluff.
I learned this the hard way in 2016. We were looking to license a model for a very specific task, and it led to horror. We’re an AI company ourselves, but we focus exclusively on language understanding and NLP. One of our clients was looking to add image recognition to one of our big AI models, so we started looking for partners that were good at image recognition. And it was really hard. We ended up settling on a vendor that our client recommended.On paper, the company seemed good. The sales team showed us case studies, and I’d heard the names of the data scientists mentioned in Slack channels about machine learning. But when we actually plugged into the AI, the results were wholly unsatisfying. We should have spent more time doing due diligence and speaking with the machine learning team (or at least an informed executive). When we tried to address the problem with the sales team it became clear they had no idea what the tech did. This separation between the sales team and the engineering team is a huge problem with AI — the people selling the solutions have to understand how the AI actually works.
Because AI technology is getting so much hype, many companies are pivoting to AI without any experience in the field. And when you combine the complexity of the technology with the number of people getting in on the game, it’s a recipe for disaster. It’s hard to validate what good performance looks like; there aren’t that many widely established benchmarks for AI confidence or accuracy across different verticals, and some companies are taking advantage of the confusion. And AI is complicated enough without having to sort out bandwagon hustlers.