F112: Building the Future Workforce: Skills, Pipeline & Knowledge Transfer

Manufacturing and fabrication industries are confronting a structural workforce crisis driven by skilled labor shortages, accelerating retirements, increasing operational complexity, and institutional knowledge loss. This session presents a practical, AI-driven workforce acceleration strategy that integrates talent acquisition and immersive training into a scalable talent pipeline model.

Attendees will explore how Artificial Intelligence functions as a career co-pilot for job seekers, a practical skills assessment engine, and an intelligent screening tool that improves hiring accuracy. The session will also examine the deployment of immersive Virtual Reality, AI-enabled coaching agents, and Digital Twin technology to deliver hyper-realistic, simulation-based safety and technical training. Independent studies confirm retention rates of up to 75%, compared to 5–20% for traditional training, along with accelerated learning and increased worker confidence and awareness.

Participants will leave with a clear implementation roadmap covering knowledge capture from retiring experts, AI-enabled talent solution, and scalable rollout strategies to build productivity-driven workforces.

Every manufacturer knows the problem. Your best people are walking out the door with decades of knowledge locked in their heads, and your filing cabinets, SharePoint sites, and shared drives aren't saving any of it. This session breaks down what manufacturers between 200 and 2,000 employees are actually doing about it, based on patterns from dozens of conversations with plant managers, IT directors, and operations leaders across metal fabrication, aerospace, and industrial manufacturing. Attendees will see a live walkthrough of how document intelligence tools built on Microsoft Azure can turn years of scattered tribal knowledge into a searchable, AI-powered system that a new hire can query on day one. You will leave with a practical evaluation framework for determining whether your organization is ready for AI-driven knowledge capture, what it realistically costs, where most companies get stuck, and how to avoid buying technology that never gets used.