Why Real-World AI Fails (and How We Fix It) | Operational Friction & The Domain Skill Gap

Why do brilliant AI models so often fail when deployed on the factory floor, in clinical workflows, or across complex logistics networks? In this segment from the final installment of our Industrial Knowledge, Governance, and Security Thought Leadership Series, we break down the critical operational friction points that stall enterprise AI adoption—and the new models required to solve them. What You’ll Learn in This Video: 3 Real-World Friction Points: Why messy industrial data, communication gaps between machine learning and domain experts, and compounding error rates in scaled agentic systems cause standard AI models to fail. AI Talent Shift: Why the industry doesn't just need AI PhDs—it needs cross-functional experts who understand both algorithms and the specific domains to which it will be applied. "Train & Employ" Pipeline: How modern academic-industrial partnerships are moving away from traditional, decade-long degree paths to train talent directly on real-world projects. Inside Northeastern's EAI Framework: How the Institute for Experiential AI (EAI) structures basic research, dedicated delivery teams, vertical specializations, and seamless IP transfer to accelerate enterprise AI integration. Featuring: Ayan Paul, Associate Research Professor & Lead at Neural Dynamics Group, Institute for Experiential AI (EAI) at Northeastern University Qiuyan Xu, Founder of Gravitate AI and CEO of Finepoint Inc. 💡 About the Series: The Industrial Knowledge, Governance, and Security Thought Leadership Series brings together leading researchers, enterprise executives, and technologists to explore the practical realities, risks, and strategies for deploying trustworthy AI in high-stakes operational environments. 🔗 Connect & Learn More: Learn more about Northeastern’s Institute for Experiential AI (EAI): https://ai.northeastern.edu/ Learn more about Gravitate AI https://www.gravitate.ai Follow us on LinkedIn:   / gravitate-ai   #IndustrialAI #AppliedAI #MachineLearning #AIGovernance #DataScience #NortheasternEAI #TechLeadership