The Other AI Revolution: Why Predictive AI will Overtake Generative AI in 2027

<p>Predictive AI often gets ignored, yet it may hold the real key to business value in the current tech landscape.</p><p><br></p><p>Machine learning expert Eric Siegel, author of The AI Playbook and Predictive Analytics, joins the Digital Disruption podcast to explain why organizations should invest at least as much in predictive AI as they do in generative AI. He breaks down how predictive AI can improve real-world operations, why so many AI pilots fail to reach deployment, and where the promise of autonomous AI agents encounters reliability challenges. While generative AI captures the headlines, Eric argues that more dependable returns often come from systems that predict outcomes and improve decision-making.</p><p><br></p><p>The conversation explores the differences between predictive and generative AI, why meaningful business value comes from deployment rather than impressive demos, and the limitations of fully autonomous AI agents and artificial general intelligence. Tune in to learn how predictive AI could make generative AI more reliable, why business leaders and data scientists need to work together, and whether AI will really cause a job apocalypse.</p><p><br></p><p>Like and follow for more conversations on AI, technology, innovation, the future of work, and digital transformation. </p><p><br></p><p>00:00 Why businesses may be investing in the wrong AI</p><p>00:47 Predictive AI vs. generative AI</p><p>03:01 How large language models fit into predictive AI</p><p>06:59 What kind of AI is solving real-world problems?</p><p>08:06 The two “wows” of AI: Impressive demos vs. deployment</p><p>09:28 Build your AI strategy with Info-Tech</p><p>10:37 Why predictive AI is still delivering business value</p><p>12:32 Why most AI pilots fail to reach production</p><p>16:25 AI hype and mismanaged expectations</p><p>19:31 AI agents, AGI, and the promise of autonomy</p><p>20:44 Can predictive AI make generative AI more reliable?</p><p>23:57 Why full AI autonomy remains unrealistic</p><p>26:59 Large language models vs. human intelligence</p><p>29:21 The limits and diminishing returns of AI models</p><p>31:35 What businesses are getting wrong about AI</p><p>35:35 Bridging the gap between technology and business leaders</p><p>37:18 Why leaders need a semi-technical understanding of AI</p><p>38:59 Three questions every predictive AI project must answer</p><p>42:22 Will AI replace developers and other technical roles?</p><p>49:29 Who should own AI strategy and accountab