Eric Siegel, Ph.D.

The excitement over machine learning and AI has reached a fever pitch. But what is the value, the function, the purpose? The most actionable win to be gained from data is prediction. This is achieved by analytically learning how to render predictions for each individual from data. Such predictions drive more effectively the millions of operational decisions that
organizations make every day. In this keynote, Machine Learning Week founder and bestselling author Eric Siegel reveals how machine learning – aka predictive analytics – works and how it delivers value to organizations across industry sectors.

The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s
notoriously difficult to launch. Outside 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. In this keynote, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. He illustrates the practice with stories of success and failure, including revealing case studies from UPS, FICO, and prominent dot-coms.

Industry leader Eric Siegel's latest research shows most models generated with machine learning to improve business
operations in a new way never deploy. It turns out that machine learning operationalization – which changes existing processes to improve them – takes more planning, socialization, and change-management efforts than most ever begin to realize. The problem is more in leadership than in technology. In this talk, Eric will outline the required practice needed to run ML projects so that they successfully deploy and deliver a business impact.

The gold standard method for leveraging data to reduce risk – in credit, insurance, and other lines of business – is machine
learning. The predictive models this technology generates reduce risk, cut costs, and boost profit. In this keynote address,
bestselling author and former Columbia University professor Eric Siegel will demonstrate what is learned from data and how enterprises apply what's learned to improve the business metrics that matter most in the financial services sector.

Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. In this keynote address, author Eric Siegel shows that, for most ML projects, the term “AI” goes entirely too far — it alludes to human-level capabilities. By unpacking the meaning of “AI,” he'll reveal how overblown a buzzword is.

Question: How does machine learning actively deliver increased returns? Answer: By driving operational decisions with
predictive scores, one score is assigned to each individual. In this way, an enterprise optimizes on what customers WILL do.
But, in tough times, our attention turns away from increasing returns and towards decreasing costs. On top of boosting us
up the hill, can machine learning pull us out of a hole? Heck, yes. Marketing more optimally means you can market less. Filtering high-risk prospects means you will spend less. And, by retaining customers more efficiently, well, a customer saved is a customer earned - and one you need not acquire. In this keynote, Eric Siegel will demonstrate five ways machine learning can lower costs without decreasing business, thus transforming your enterprise into a Lean, Mean Analytical Machine. You’ll want to return home and break the news: We can’t afford not to do this.