It used to be a relatively simple cost-benefit analysis to determine whether outsourcing various non-essential business processes was wise or not. If you were reducing costs and the new business processes got the job done as well as the older (expensive) processes, then it was pretty much a no-brainer.
The way businesses touch and interact with customers is expanding. Digital and social channels, especially, give a business massive amounts of data about customer interactions. But how to manage all of this information, and how to decide which data is important to driving your business?
Manufacturers across a broad range of sectors are finding that artificial intelligence (AI) systems can benefit their firms in a variety of ways. In the manufacturing section, most of the early AI systems were intended to help design optimal manufacturing processes, and to increase operational efficiencies at large factories, chemical plants, and refineries.
Artificial intelligence with practical, everyday applications is no longer the stuff of science fiction. AI is already being employed by thousands of businesses of all sizes today to help them do business faster and less expensively or to develop an edge over the competition.
Artificial intelligence, machines that think, are going to change the way we live, work and play. In fact, AI is already having a major impact on a number of key sectors in the global economy, and “early adopters” in these industries are already seeing phenomenal return on investment as AI opens up new business opportunities and creates value through identifying new process efficiencies.
You have signed an agreement with a digital partner. You brought them on because you were hoping that they would help hasten the process of digital transformation. However, something does not feel right, and they keep missing important deadlines.
The recent TechCrunch article about Google’s AutoML that lets developers train custom machine learning (ML) models without having to code is just one of many big deals that are changing how ML will be used in Marketing and other fields.