From the Lab to Real Life Operationalizing AI and Data Analytics

Enterprises from industries around the globe continue to embrace analytics and artificial intelligence (AI). In a survey of IT decision-makers that my company, CCS Insight, conducted this summer, a remarkable 75% of US and European organizations said they're now using, testing or researching the deployment of machine learning in their businesses, up from 58% in 2018.
But there's a challenge: for the vast majority, AI remains an experimental workbench technology. I speak with many organizations that concentrate their AI projects on proof of concepts or implementing point solutions with a narrow focus. Justifiably so, in my view, because, short of experience and skills, they need to start with small projects to learn and iterate. However, this means that most machine learning models built today fail to make it into production. Fewer than 10% of companies using AI have fully put it into operation within their business processes or have organization-wide strategies. Some estimates suggest that at most, one in five AI solutions become operational.