The Problem With AI Pilots

Over the past year or so we’ve been engaged in an effort to tell the story of how large organizations are deploying artificial intelligence in their businesses. We were encouraged by the response to the 2018 NewVantage Partners executive survey, in which 93% of respondents said their organizations were investing in AI initiatives. Plenty of companies to write about, we thought. These were very large organizations spending goodly sums on AI and with a history of early adoption of other technologies. But when we approached many of these companies to discuss writing some case studies about their work, most of them demurred.

Most said the reason wasn’t that they wanted to keep their AI activities secret, but that they weren’t actually very far along and hence their projects were not worth discussing yet. They were doing lots of pilots, proofs of concept, and prototypes, but they had few production deployments. When they did have AI systems in production, most were machine learning-based systems that had been in place for many years. This is particularly true in financial services, where large-scale “scoring” has been used to evaluate customers for credit and potential fraud for well over a decade. Some said to us that they didn’t really consider these projects to be examples of AI — consistent with the common view of AI that it describes technology that is never really here yet. Others say that they have robotic process automation (RPA) implementations in place, but most are relatively small, and there is also debate about whether RPA is really AI or not.

Why AI Implementation Is Challenging

But there are good reasons why production implementations of AI technology are relatively scarce. One is the maturity — or lack thereof — of the technology. Chatbots and intelligent agents, for example, are getting better all the time, but many companies still hesitate to turn their customers over to them. Instead, they ask their employees to use them for applications in HR and IT. Some make them available to their call center reps to use in the background to help answer customer questions. Eventually, they hope, they will support customer interactions directly.