How AI Is Powering the Next Industrial Revolution

2025-10-15 · Source: Chad Harvey · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Operations & Process Management, Supply Chain & Logistics · Depth: Novice, extended

Summary

Evan Schwartz, Chief Innovation Officer at AMCS Group, discusses how AI is transforming resource-intensive industries such as waste management, recycling, and natural gas. He highlights "compassionate technology" that optimizes logistics, reduces operational costs, and enhances sustainability without replacing human jobs. Specific examples include using AI for route optimization to reduce fleet sizes from 13 to 10 trucks, saving $3 million annually per implementation, and predictive maintenance to maximize vehicle utilization. Schwartz also details the application of vision AI for contamination detection, fraud prevention, and safety inspections in scrap metal and other sectors. The discussion emphasizes the multiplicative effects of integrating various AI applications across an entire vertical supply chain, moving beyond isolated solutions to achieve significant efficiencies and uncover new value streams.

Key takeaway

For AI Architects and VPs of Engineering evaluating AI adoption, prioritize narrow, well-scoped use cases with strong data and clear ROI to build initial success. Focus on training your workforce in prompt engineering and leadership to manage AI agents, shifting from task-based work to strategic oversight and innovation. This approach fosters an "infinite game" of productivity growth rather than a "finite game" of workforce reduction, ensuring long-term business resilience and human capital development.

Key insights

AI can drive significant operational efficiencies and sustainability in resource-intensive industries by augmenting human productivity.

Principles

• AI should multiply human productivity, not replace it.
• Highest carbon footprint areas often indicate greatest inefficiencies.
• Applied knowledge is more valuable than raw knowledge.

Method

Integrate algorithmic AI, vision AI, and agentic AI across the entire vertical supply chain, focusing on connective tissue between processes to achieve multiplicative effects and uncover new use cases.

In practice

• Implement AI for route optimization to reduce fleet size and fuel costs.
• Utilize vision AI for contamination detection and safety inspections.
• Adopt Model Context Protocol (MCP) for seamless system integration.

Topics

• AI in Logistics
• Agentic AI
• Model Context Protocol
• ERP Integration
• Vision AI

Best for: VP of Engineering/Data, AI Architect, AI Product Manager, CTO, Executive, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Chad Harvey.