Artificial intelligence could play a particularly significant role in addressing manufacturing’s persistent skills gap. Woods envisions specialized large language models for areas such as CNC machining, robotics, controls and PLCs that can capture decades of accumulated manufacturing knowledge and make it accessible to less-experienced workers. AI systems could combine information from manuals, maintenance records, process data and even video recordings of experienced employees performing complex tasks. As these capabilities evolve, Woods expects conversational interfaces and AI-driven tools to simplify machine programming and setup, potentially eliminating much of the need for operators to manually work through G codes and M codes and allowing workers to concentrate more on problem-solving and creativity.

He also examines the convergence of machine hardware and software and the growing importance of open architectures that allow machines, controls and software platforms to exchange useful data. Woods discusses AMT’s work with MTConnect and newer efforts involving universal scene description (USD), while emphasizing the value of industry participants collaborating around common standards rather than creating competing ones. He explains how smaller manufacturers can participate in digital transformation without replacing every piece of legacy equipment. Through integration technologies, distributors, automation expertise and cooperation among organizations such as AMT and the National Tooling & Machining Association (NTMA), existing machines can be connected to newer data and information systems, giving manufacturers a practical path toward more integrated, digitally enabled operations.