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New report: 6 steps to implementing a machine learning strategy

Sure, machine learning is becoming a business imperative, but how does it work in practice? That’s the subject of a new step-by-step guide to solving business problems with artificial intelligence and ML, based on insights gathered by IDG Research Services. Its publication comes at a time when technology leaders face growing pressure to embrace these emerging technologies, yet many have questions about how to get started.

Based on a survey of senior IT leaders as well as in-depth interviews with technology executives, the IDG guide outlines six basic steps to implementing a ML strategy. These include:


  • Identifying use cases
  • Choosing a well-understood problem
  • Determining success metrics
  • Coping with talent shortages
  • Assessing infrastructure options
  • Showcasing the business value of ML projects



Real-life examples help illustrate many of these, such as the health services company that used ML to reduce support ticket-resolution time from 48 minutes to six. In another section, a financial services VP explains that cloud-based ML services enable his company to avoid spending money on computing resources that sit idle.



The guide also includes concrete tips for new ML adopters, provided by the CIOs and other IT leaders who participated in IDG’s research. For example, a real-estate CIO recommends the use of third-party tools that rely on AI and ML technologies, while a financial services VP highlights the challenge and potential of incorporating unstructured data into ML initiatives.



For more actionable advice aimed at businesses in the initial stages of building their ML strategy, access the complete guide below.