7 last-mile delivery problems in AI and how to solve them

The term last-mile problem comes from the telecom industry, which observed that it costs inordinately more to build and manage the last-mile of infrastructure to the home than to bring infrastructure to the hub city or residential perimeter. Businesses are starting to discover a similar last-mile delivery problem in AI: It is much harder to weave AI technologies into business processes that actually run companies than it is to build or buy the AI and machine learning (ML) models that promise to improve those processes. "The path to deploying ML is still expensive," said Ian Xiao, manager at Deloitte Omnia, Deloitte Canada's AI consulting practice. He estimates that most companies deploy only between 10% and 40% of their machine learning projects depending on their size and technology readiness. In fact, the last-mile problem is a bit of a misnomer when applied to AI deployment in the enterprise.