Operationalizing AI

Preface

Increasingly, data scientists are consulted on a daily basis about making sustained and fundamental changes to the ways their organizations do business. However, their organizations are typically not designed to realize the benefits of those changes. Company leaders rely on these highly skilled and expensive data scientists to help them change their respective markets by building predictive capabilities into their products and workflows, but they often think the change can be led by the data science team alone. Across industry sectors, management and leaders see a gap between the promised and actual impact of data science projects, and wonder why there is such a noticeable difference.

At the heart of this gap is delay. The longer the time to market for data products, the higher their cost and the greater their risk. The risk increases as data drifts, scope creeps, and requirements grow. To shorten the time to market, lower overhead, and reduce the risk, organizations need a comprehensive understanding of how to build artificial intelligence in a repeatable fashion. In other words, organizations need to understand how to operationalize AI.

Operationalizing AI, which we will define more fully in the next sections, allows disparate technology groups with shared deliverables to adopt a unified language around how they talk about data science within the business. To demonstrate the value of operationalizing AI, let’s consider how software development has evolved. Delivering ...