Eric Siegel shows enterprise leaders why accurate models are not enough and how to turn predictive AI into real business value by valuating models, setting decision thresholds, and aligning deployment with ROI.
Powerful breakdown of the valuation gap in predictive AI. The point about decision boundaries carrying more weight than marginal accuracy gains is crucial for operations teams, yet gets burried under technical metrics. Making KPI tradeoffs interactive rather than static fixes the "silent failure" problem where projects stall becuase stakeholders can't quantify upside. The profit curve approach should be standard in every deployment workflow.
It's time to lead with quantifying upside and time to value. If you would like to dive deeper into Eric Siegel's work, he has two excellent books "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" and "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment".
Powerful breakdown of the valuation gap in predictive AI. The point about decision boundaries carrying more weight than marginal accuracy gains is crucial for operations teams, yet gets burried under technical metrics. Making KPI tradeoffs interactive rather than static fixes the "silent failure" problem where projects stall becuase stakeholders can't quantify upside. The profit curve approach should be standard in every deployment workflow.
It's time to lead with quantifying upside and time to value. If you would like to dive deeper into Eric Siegel's work, he has two excellent books "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" and "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment".