Executive Summary | Driving Digital Transformation Using AI and Machine Learning
- By Fern Halper, Ph.D.
- September 25, 2019
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 VP of TDWI Research. Her work focuses on AI, generative AI, agentic AI, AI governance, cloud computing, and other modern analytics approaches. She has more than 25 years of experience in data and business analysis and AI and has published numerous articles on data mining and information technology. Halper is co-author of “Dummies” books on cloud computing, hybrid cloud, and service management, as well as Big Data for Dummies. Halper is also the author of the 2026 book, Data Makes the World Go ’Round: The data, tech, and trust behind AI success. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. She has taught at both Colgate University and Bentley University. Her Ph.D. is from Texas A&M University. You can reach her by email ([email protected]) or LinkedIn (linkedin.com/in/fbhalper).