AI requires trusted human sources and familiar approaches The new technology exhibited at IMTS – The International Manufacturing Technology Show gives every show a unique character, and artificial intelligence (AI) will heavily influence IMTS 2026. Russ Waddell, a member of the MTConnect board, and Chris Misztur, founder of Mr. IIoT.To bring clarity to today’s hottest tech topic, the IMTS Exhibitor Workshop featured a discussion titled “Applying AI: What Manufacturers Need to Know Today” between Chris Misztur, founder of Mr. IIoT, and Russ Waddell, a member of the MTConnect board. IMTS+ writers connected with Misztur and Waddell to further explore AI and separate fact from hype.What’s really AI? “A lot of solutions have been called AI for 10 years, but we need to distinguish AI from machine learning, where a computer crunches data in the background based on some known algorithm,” says Misztur. “Machine learning presents analysis, but you can’t really engage with that analysis. AI lets you engage and dive deeper.” In a typical manufacturing scenario, machine learning might analyze sensor data or process signals to identify patterns. Historically, someone had to manually query that data, write database queries, or build reports to understand trends such as machine failures. Today’s AI tools can eliminate much of that manual work. “If the machine data is in a database, you can just ask the AI, ‘Show me a Pareto chart of failures over the past 90 days,’” Misztur says. The Frame Remains the Same Despite the excitement surrounding AI, Waddell stresses that the fundamental approach to evaluating AI, or any other manufacturing technology, has not changed. “There’s a framework for shopping for technology that hasn’t changed in decades,” he says. “You start with, ‘What problem does this solve?’ If you walk up to an IMTS booth and someone starts explaining how their AI works instead of asking what keeps you up at night, that’s going to be two very different conversations.” Waddell frames AI adoption as a two-phase progression: Process visibility – gaining access to reliable, connected data. Process improvement – using that data to drive better decisions. Many manufacturers are still working toward full visibility. Machine data, production records, and business systems often remain disconnected, limiting the ability to measure true performance. For example, quoting, estimating, and actual production outcomes are not always linked — making it difficult to determine whether a job met its intended margin. Playing in the Sandbox The first level of AI implementation accelerates existing activities, such as analyzing production or machine data, and linking it to quoting, scheduling, reporting, or maintenance decisions. The next level of AI implementation transforms the workflow itself. Waddell inadvertently explored the possibilities of AI when he conceived of a “sandbox project.” He wanted to simulate shop-floor monitoring software while also learning more about the software development workflow. “I went to Chris as an IIoT expert,” says Waddell. “I did not propose using AI. I did not talk about AI. I did not say, ‘AI.’ I said, ‘Can you build me a sandbox web application for messing around with a monitoring dashboard and some machine simulators behind it?’ He told me I couldn’t afford him. We talked it out a little bit more. Eventually, he caved, but he said he would have to build the sandbox using Claude Code,” an AI-powered agentic coding tool from Anthropic (“agent” is a term used to describe AI tools). “From beginning to end, it took three weeks versus your typical six-month cycle to build out an entire data platform, from ingesting machine data to analyzing it and reporting it on dashboards,” says Misztur. Waddell also began the process using the free version of Gemini, Google’s AI tool and chatbot. He was only dabbling, but he asked Gemini to produce a software requirements specification for simulators and a realistic-looking monitoring tool. “I was pleasantly surprised,” says Waddell. “Not only did Gemini give me the spec doc, but it also asked me if it could build a prototype.” As a result of his experience, Waddell suggests people experiment with various AI tools and compare results for their applications. Culture Matters Technology alone does not drive transformation; organizational culture plays a critical role. Many manufacturing environments prioritize immediate production demands, leaving limited time for systematic analysis or long-term improvement. “As a result, initiatives like data integration or predictive maintenance are often delayed,” Misztur says. “However, at some point, to use an analogy, you have to stop driving the car and get an oil change.” He says shop managers need a systematic approach to digitize their operations. Fortunately, the younger generation is familiar and comfortable with software. That said, Misztur cautions that while some people can achieve higher-quality results with AI, he witnessed firsthand the problems of a junior developer. “AI just did not work for him. It kept changing code that was not supposed to be changed, and he didn’t have the experience to recognize the mistakes. For him, a traditional development approach worked better,” he says. Thus, one caution for AI users is that when you don’t know what you don’t know, find a subject matter expert to validate the output. How to Get Started with AI in Manufacturing Manufacturers looking to apply AI today can start with a few practical steps: Focus on a specific problem: Identify a clear use case such as downtime reduction, quality analysis, or scheduling optimization. Assess data readiness: Ensure machine, process, and business data are accessible and reasonably structured. Start small and iterate: Pilot AI in a limited scope before scaling across operations. Leverage existing partners: Machine builders, software providers, and automation vendors increasingly embed AI into their solutions. Validate results with experts: Combine AI outputs with domain knowledge to ensure accuracy and relevance. Compare tools: Different AI platforms excel at different tasks; testing multiple options helps identify the best fit. Engaging at IMTS 2026 To help manufacturers explore AI at IMTS 2026, the show introduces two major additions: the Industrial AI Arena and the Industrial AI Conference. The Industrial AI Arena brings together more than 25 established leaders and emerging innovators in a dedicated environment focused on applied AI (and the list is growing). Manufacturers can evaluate solutions that address critical challenges across quality and inspection, process optimization, downtime reduction, cybersecurity, safety, and demand forecasting. The Industrial AI Conference (Wednesday, Sept. 16, 10 a.m.-4 p.m. CT) is a one-day, practitioner-focused program designed for implementation. The agenda outlines a framework for understanding AI, including predictive maintenance, quality applications, edge versus cloud deployment, and data readiness. Developed in collaboration with Dr. Jay Lee and the University of Maryland’s Center for Industrial AI, the program also features sector-specific case studies and a roundtable on implementation trade-offs, with a focus on measurable impact across uptime, quality, cost, and throughput. While walking the show floor, Waddell recommends looking for examples of how AI providers engage with exhibitors across machines, tooling, software, and automation. “These relationships often reveal which technologies and applications are gaining traction,” he says. “The way a job shop could end up implementing AI is that they will connect with their machine, tooling, software, and automation partners at IMTS. They will discuss application engineering problems, production, and business goals like they normally do, and then these experts will use AI as part of the solution.” In practice, implementing AI in manufacturing is less about adopting a standalone technology and more about building trusted relationships and proven workflows. IMTS 2026 offers a unique opportunity to see how AI is being applied across the production lifecycle — and to connect with the experts helping manufacturers turn potential into measurable results. Register now.
Manufacturers can apply AI by focusing on real problems, improving data visibility, and working with trusted partners. IMTS 2026 highlights practical tools delivering measurable impact.
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