Artificial intelligence is everywhere right now. It’s in the headlines, it’s being built into software tools, and it’s showing up in more and more conversations about engineering, manufacturing, quality, and process improvement. But here’s the problem. A company can buy the software. A team can attend the training. Everyone can walk away impressed by what AI might be able to do. And then, a few weeks later, very little actually changes. The tool is still there. The potential is still there. But the habits never formed. And that matters directly to reliability. In electronics manufacturing, reliability doesn’t come from good intentions. It comes from repeatable processes, disciplined execution, clear documentation, good decision-making, and the ability to recognize problems before they become failures in the field. That’s where AI can become more than just another interesting tool. Used properly, AI may be able to enhance workflows such as: • Reviewing ECOs and identifying downstream process, documentation, material, or inspection impacts • Analyzing AOI, SPI, test, and process data to detect recurring defect patterns • Supporting root cause analysis by organizing failure data, inspection results, and corrective action history • Monitoring process drift in areas such as reflow, cleaning, stencil printing, placement, and environmental conditions • Assisting with corrective action reports, customer responses, and internal documentation • Capturing tribal knowledge from meetings, shift reports, troubleshooting notes, and past problem-solving activity Those are all reliability-related activities. They touch the way products are designed, built, inspected, documented, corrected, and improved. But AI only helps if it becomes part of the way work actually gets done. Mike Konrad's guest is Sean Patterson, author of the article, “Why Your AI Training Isn’t Sticking.” Patterson argues that AI adoption is not really a training problem. It’s a habit problem. In other words, the goal isn’t simply to teach people what AI can do. The goal is to connect AI to the work people are already doing, such as preparing for meetings, reviewing engineering change orders, working through technical questions, or organizing complex problems. For those of us focused on reliability, that distinction is important. AI doesn’t replace engineering judgment, process knowledge, or verification. But when used properly, it may help strengthen the behaviors that support reliability: asking better questions, capturing knowledge, reducing overlooked details, and improving the consistency of technical decision-making. Here, Sean talks about why AI training often fails to stick, how small triggers can turn occasional use into practical habits, where AI can fit into engineering and manufacturing workflows, and how companies can approach AI in a way that is useful, responsible, and directly connected to reliability.
