Abstract
This chapter introduces the business (or non-technical) adoption challenges specific to next generation smart technologies. It begins with a review of the likely impacts that machine learning (ML) will have on oil and gas. Observed reasons why ML projects are rejected by users, the causes of the trust gap between humans and smart machines, and a framework for building that trust are presented. Specific tactics for gaining trust, progressing beyond pilot projects, and scaling up deployments are revealed. The chapter explores the challenges and solutions to overcoming resistance to change in the front line, and concludes with the seven actions that are proven to help drive adoption.
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