In manufacturing, time matters on the shop floor. New hires must become productive safely in days, not months. Veterans need their expertise codified so it survives shift changes and retirements. Michael Burgess, Vice President of Operations at Crenlo Engineered Cabs, puts it plainly: “AI helps turn those walk-ups and line-side tips into verified standard work, helping with safety and productivity.” The key is not only collecting video clips, voice notes, and annotated photos, but also turning them into structured steps that the next person can use, with clear checks for quality and safety.

This need for structured knowledge is reinforced by federal priorities reshaping domestic workforces, particularly in manufacturing. Initiatives that emphasize American competitiveness require faster technology pathways that help workers learn and apply organizational knowledge.

That structure starts with the data foundation. Elias Brown, Data Manager at Vallourec, highlights the role of large language models: “Complex manuals, work orders, and quality notes into a single vocabulary. AI can reason across sources to fill gaps, flag conflicts, and keep procedures current at the speed of change.” With a shared vocabulary across SOPs, CMMS, and quality systems, AI assistants can answer questions in the moment, point to the right tool, and suggest the best action as conditions shift.

Scaling this across industries requires collaboration. The AI Applied Consortium, an alliance of executives, academics, and Fortune 50 organizations, is building frameworks and best practices to empower manufacturers. With the support of organizations like the United States Center for Advanced Manufacturing (USC4AM), a US-based nonprofit that partners with the World Economic Forum’s global manufacturing initiatives, and through platforms like the IQPC Connected Worker Summit, leaders are sharing thought leadership and practical successes. Shree Parikh from USC4AM captures the shift well: “We are moving away from training as a one-time event toward continuous, competency-based development. The real measure is not what course was completed, but what capability can now be applied on the floor.”

AI is also reshaping jobs themselves. Tasks that once took years of experience to master are becoming machine-assisted routines. Human work is moving toward orchestration, judgment, exception handling, and improvement design.

This trend is echoed in the World Economic Forum’s report on the skillsets of 2030. At the top of the list are not narrow technical skills, but broader capabilities like creative thinking, agility, and technological literacy, skills that complement AI rather than compete with it.

Dr. Satyam Priyadarshy, former Chief Data Scientist at Halliburton, emphasizes the mindset shift required: “Automation is not the end of human work. It is the end of unexamined human work. The premium shifts to decision intelligence, knowing which signals matter, how to challenge model output, and when to pause a process. That mindset must be trained intentionally, not assumed.”

Career paths must also evolve. Konrad Konarski, Chairperson of the AI Applied Consortium, explains: “The winners will be companies that redraw roles constantly and give people agentic tools to navigate and augment decision making.” This lattice approach allows a welder to transition into digital inspection or a maintenance technician to grow into reliability engineering, because task families are modular and credentials are continuous.

Corporate culture is the force that turns pilots into practice. Tools cannot replace norms that reward and empower. A culture where agility and constant learning are expected and supported makes that stretch safe and valuable. Recognition should go to those who retire brittle practices and mentor others through AI-assisted content creation. Learning plans must be dynamic and personal, always pointing one step ahead of the automation line.

Konarski frames it directly: “Make agility a policy. Tie incentives to skill currency and use AI to keep each person’s learning plan one step ahead of the automation line.” The practical path forward is to UNBOX;

• • Underpin progress by unifying the knowledge layer, enabling evolving AI tools to reason seamlessly across it
• • Now is the time to capture tacit knowledge using simple devices and an effortless approval flow
• • Break down roles into task modules so that skill matrices can be refreshed without friction
• • Organize a center that sustains the work without slowing momentum
• • X-factor success by measuring outcomes that genuinely matter to safety, quality, and time to proficiency
  

When these elements come together, the connected worker is not just supported by a tool; they are empowered by it. They are part of a living system that learns every shift. AI does not replace craftsmanship. It preserves, scales, and directs it toward the next set of challenges that only people can solve.

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