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Executive Summary | Driving Digital Transformation Using AI and Machine Learning

There is considerable excitement about AI technologies, including machine learning and natural language processing. Organizations are embracing these technologies to gain better insights, make better decisions, and improve competitive advantage. In fact, AI is at the heart of the digital revolution around analytics occurring today. AI promises to help organizations improve their operations and processes and to drive new revenue opportunities.

Organizations are making use of AI technologies in numerous ways. Some of these may sound familiar, such as using AI to build churn models or predict fraud. Others seem more revolutionary, such as using AI to diagnose cancer or improve crop yield. AI is being used across the organization and across industries. Those organizations that are already using AI technologies are gaining value from it.

Machine learning dominates the technologies in use; over 90% of respondents who are active users of AI make use of machine learning. Many are building machine learning models and putting them into production. Others are building applications. Organizations are also making use of natural language processing for mining text as well as for servicing customers. For instance, chatbots are popular for customer service. Organizations are using deep learning for image recognition. The use cases are wide and varied.

Organizations utilizing AI technologies are doing so primarily by employing skilled data scientists and other team members, such as DevOps. In fact, 67% of organizations deploying AI technologies today state that AI projects are built by data scientists and are deployed into production by DevOps teams. There is also movement to use augmented intelligence applications, e.g., those where AI is infused into the software to automate functionality such as data cleansing, deriving insights, or building predictive models. Where less than one-third of respondents use these tools today, an additional 50% are planning to use these tools in the next 1-2 years.

However, employing data scientists or using augmented intelligence isn't enough to create a successful AI deployment. AI requires a modern data infrastructure to support new data types and often massive amounts of data. Many organizations are moving to the cloud for data management. They are making use of data engineers and newer pipeline tools to help integrate data and make sure it is trustworthy. They are hiring DevOps teams to deploy models and monitor them in production. They are evangelizing to build excitement and trust.

This TDWI Best Practices Report examines how organizations using AI are making it work. It looks at how those exploring the technology are planning to implement it. Finally, it offers recommendations and best practices for successfully implementing AI in organizations.

Alation, AnswerRocket, Hitachi Vantara, Infoworks, Melissa Data, and TIBCO sponsored the research and writing of this report.

About the Author

Fern Halper, Ph.D., is vice president and senior director of TDWI Research for advanced analytics. She is well known in the analytics community, having been published hundreds of times on data mining and information technology over the past 20 years. Halper is also co-author of several Dummies books on cloud computing and big data. She focuses on advanced analytics, including predictive analytics, text and social media analysis, machine-learning, AI, cognitive computing, and big data analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead data analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her by email ([email protected]), on Twitter (, and on LinkedIn (

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