Originally published in Forbes, June 29, 2024. Some problems are better solved with predictive AI than with generative AI. To run more effectively, many business operations need prediction more than they need the generation of new content. That’s why predictive AI is the kind of AI companies turn to for improving the effectiveness of large-scale processes. Predictive models decide whom ..read more

 “Nearly half of S&P 500 companies have talked about AI during earnings calls since May [2023], an NBC News analysis of S&P 500 earnings calls found. In fact, AI was mentioned about as often as the Federal Reserve and interest rates. But the hype isn’t translating into real-world use: A November[2023] survey from the Census ..read more

 Originally published in Built In, May 22, 2024. Your biggest operations are made up of many small decisions. Predictive modeling can help you make them. Predictive AI is the technology businesses turn to for boosting the performance of almost any kind of existing, large-scale operation across functions. It learns from data to predict outcomes and ..read more

 Originally published in Forbes, June 11, 2024. If you’ve ever had a data scientist make a machine learning model for you, you probably first experienced excitement, followed by bewilderment. The potential power is awesome. In its enterprise application, predictive AI is the antidote to information overload. Too many prospective customers? Use a predictive model to prioritize them ..read more

 Originally published in Forbes, March 4, 2024. Which kind of AI should companies focus on—generative AI, which produces writing, computer code, images, video and other content, or predictive AI, which targets ads, marketing, fraud detection, risk management, healthcare treatment and other established operations? Those posing this question may be missing the point—similar to when someone ..read more

 Originally published in Forbes, March 25, 2024. To do its job, AI needs your help. It has the potential to drive millions of operational decisions—such as whom to contact, approve, investigate, incarcerate, set up on a date or medicate. But there’s a critical project step that’s often overlooked: Choosing the decision threshold, a single number ..read more

 Originally published in Forbes, April 10, 2024 When OpenAI’s board momentarily ousted Sam Altman from his post as CEO last November, the media obsession was… intense. Why so much fuss about a corporate drama? The public mania—and perhaps the ousting itself—came in no small part from a widespread belief that the company is well on ..read more

 Originally published in Harvard Business Review Machine learning might be the world’s most important general-purpose technology, but it’s notoriously difficult to launch. Outside of Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. This article ..read more

  Listen to Eric Siegel, former Columbia University Professor, discuss his book The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Radio show: Bloomberg Businessweek Hosts: Carol Massar and Tim Stenovec. Producer: Paul Brennan. Click here to listen to the full 10-minute segment Transcript: 00:00 Speaker 1 This is Bloomberg Business Week with ..read more

    This article from Morgan Vawter, Global Vice President of Data & Analytics at Unilever, serves as the foreword to The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, by Eric Siegel. Morgan will present a keynote at Machine Learning Week, June 4-7, 2024 in Phoenix, AZ – where registrants will receive ..read more