Tag: agentic AI

OM in the News: AI Makes a Fundamental Shift in Manufacturing

For two decades, manufacturing has been defined by a relentless pursuit of optimization. We automated assembly lines (Ch. 9), digitized records and built predictive maintenance models (Ch. 17), all in the service of marginal gains in efficiency.

While this approach yielded significant returns, we have reached the ceiling of what traditional, rule-based automation can achieve. “In 2026, the industry is undergoing a fundamental shift toward a model of agentic enablement,” says the head of Google Cloud, Praveen Rao in Industry Week (Jan.22, 2026).

Rao says this isn’t just a technical upgrade; it is a new industrial model where AI moves from a tool that recommends to an agent that achieves. It also isn’t about reducing headcount, but about transforming operators into “super-users”: who are empowered by AI to solve more complex problems and drive higher value. He makes 3 predictions:

The Agentic Supply Chain: Beyond Prediction to Execution Historically, manufacturers have been forced to lock up vast amounts of capital in finished goods, essentially “betting” on demand forecasts. Agentic AI changes this math by allowing production to align dynamically with real-time customer intent. Traditional predictive models could warn of a supplier disruption, but it still required a human to spend days rerouting logistics. In 2026, AI agents will close this loop. Systems will be empowered to detect a tier-2 supplier failure in the middle of the night.

The Rise of the Technocrat Entering the era of the Technocrat, the factory worker of the future will no longer be measured by analog tools of the past—the hammers and the screwdrivers, but by mastery of generative AI for rapid troubleshooting and agentic AI for process orchestration.

Hyper-Personalized Intelligence on the Shop Floor  The AI agents of the future will possess “long-term memory,” understanding the specific context and historical preferences of every shop-floor operator. This is agentic AI acting as a personalized performance coach, delivering the right insight to the right person at the exact moment of need.

Rao concludes: “The transition from fragmented automation to integrated, agentic systems is the new industrial paradigm. The companies that fail to adopt an agent-first mindset will not just fall behind; they will find themselves competing against living factories that can think, adapt, and execute 24/7 without friction.”

Classroom discussion questions:

  1. Explain what an AI agent does.
  2. Give an example of personalized intelligence on the shop floor.

OM in the News: 3 Core Skills for the AI Manufacturing Workforce

 Companies invest heavily in workforce development—global corporate training represents over a $350 billion market—but few can answer the fundamental question: Does our workforce actually possess the capabilities required for AI-era manufacturing? The problem, writes IndustryWeek (Dec. 16, 2025), is that firms are training for yesterday’s skills while tomorrow’s requirements remain undefined.

Manufacturing faces a dual disruption. AI, robotics and automation are reshaping production at unprecedented speed, while skilled labor shortages intensify when experienced workers retire, taking decades of knowledge with them. Most training programs rarely assess whether workers developed the fundamental capabilities needed to work effectively in AI-augmented environments.

There are the 3 Core Skills needed:

1. Human+ capability This isn’t about workers learning to code or becoming data scientists. Human+ is the ability to work effectively alongside AI and automation—knowing when to trust algorithmic recommendations, when to override them based on judgment and how to optimize human-machine collaboration for maximum productivity. Manufacturers invest millions in AI-powered quality control systems, predictive maintenance platforms, and autonomous production scheduling—then struggle to achieve projected ROI because their workforce lacks the core skills to extract value from these technologies.

2. Agentic AI orchestration As AI-era manufacturing evolves from simple automation to autonomous agents that manage complex workflows, workers need the capability to orchestrate multiple AI systems effectively. Agentic AI orchestration is the ability to coordinate these systems so they don’t work at cross-purposes. It means understanding how to deploy AI agents for quality control, predictive maintenance, supply chain optimization and production scheduling—and managing the interactions between these systems when they conflict or produce unexpected results.

3. Interoperability catalysis Modern manufacturing runs on complex networks: older machines next to new robots, ERP systems talking to manufacturing systems, logistics platforms feeding production plans, and partner data coming in from suppliers. Interoperability catalysis is the ability to make all of that actually work together:

  • Legacy and modern systems (the 40-year-old CNC and the AI-powered vision system)
  • Digital and physical environments (ERP and planning data vs. shop-floor reality)

The Path Forward: Manufacturing’s competitive advantage in the AI era won’t come from having the most advanced technology. It will come from having a workforce capable of extracting maximum value from that technology. These 3 core skills represent the foundation. Manufacturers who systematically assess and develop these capabilities will thrive as AI reshapes production.

Classroom discussion questions:

  1. Is the current workforce capable of managing AI-manufacturing demands?
  2. Are business students interested and willing to take these jobs?