IMTS 2026: 23 Interviews on Industrial AI from the Show Floor

Four days at McCormick Place, 23 conversations, and one question that came up in nearly all of them, asked twenty different ways: when the machinists retire and the AI shows up, who is actually in control?

This is the index of the full IMTS 2026 interview series, recorded September 14 to 19, 2026 in Chicago and sponsored by Aras. Short highlights from each conversation are on the Best Moments playlist. Below, the interviews grouped by theme, with the guest, the one question the conversation turns on, and the buyer's guide it feeds.

Machinist knowledge and CNC programming

The show floor is machine tools, so it is no surprise that the deepest cluster was about the people who program them and what happens when they leave.

• Airplane Orders Pile Up. Expert Machinists Retire. Nick Khormaei, Neuramill. Aircraft demand is rising while experienced machinists retire. High-precision manufacturing with people kept in control.
• CNC Programming Is Ready for an AI Upgrade David Priev, Limitless Labs. Programming as the bottleneck, and what an AI copilot for CNC actually captures.
• AI Can Crash Your CNC Machine. Simulation Comes First. Shaun Mymudes, Eureka. The guardrail every CNC-adjacent conversation landed on: nothing AI-generated runs without simulation.
• When Machinists Retire, Where Does Their Knowledge Go? Daniel Wälchli, Manukai. Capturing process knowledge before it walks out of the door.
• Your Shop-Floor Know-How Is Your Competitive Advantage Hiren Kumbhojkar, Hexagon. Manufacturing know-how and the data that has to sit underneath industrial AI.

These five map directly onto the AI machining layer in the Best CAM Software 2026 guide, where the SWARF framework treats postprocessor and simulation reality as the dimension buyers underweight most.

Physical AI and robotics

• Physical AI Is Already Here Jean-Pierre Hathout, Teradyne Robotics. Deployments, not demos.
• The Future Is Not a Lights-Out Factory Tyler Bouchard, Flexxbotics. Robot orchestration around people rather than instead of them.
• Automation Has an Engineering Bottleneck Fay Goldstein, Bardin AI. What stands between an automation demo and a working deployment.
• Rearrange Your Factory Without Moving a Machine Rana Hanocka, Thrixel. Simulating layout changes before anyone touches a forklift.

Flexxbotics sits in the automation orchestration row of the Best MES Software 2026 MINT scorecard, and the layout conversation belongs next to the digital factory strand in What is Manufacturing Process Planning?.

Engineering AI and CAD

The CAD conversations split cleanly into three positions, and they disagree with each other in useful ways.

• Text-to-CAD Has a Geometry Problem David Anderson and Michael LaFleche, PTC Onshape. Why prompting a B-rep kernel is harder than prompting a renderer.
• Engineers Shouldn't Just Be Drafters Michael Bogolomny, InfinitForm. Manufacturing-aware generative design that outputs a parametric feature tree.
• AI Can Generate It. Who Will Validate It? Sai Nelaturi, C-Infinity. The validation gap behind every generated geometry.
• Your Design Knowledge Is Stuck in Notebooks Taylor Young and Jeremy Andrews, CoLab. Design review knowledge as an asset nobody has been storing.
• When Engineers Become Editors of AI Agents Rui Aguiar, Cosmon. What the engineering job looks like when the agent drafts and the engineer edits.
• AI Takes Action. Engineers Make the Call. Parth Mehta, Makistry. Agency with a human decision at the end of it.
• Your CAD Changed. Did Your Instructions? Ben Brakenwagen, Dirac. Work instructions that follow the model instead of lagging it.

The geometry argument is the one this site has spent the most time on. Start with What is a Geometric Kernel? for why text-to-CAD is hard, and the Best CAD Software 2026 guide for where InfinitForm and Onshape sit in the landscape.

Data, BOMs and the digital thread

The least glamorous interviews and, if you run a plant, the most important.

• Your Digital Thread Starts With Trustworthy Data Frank Popielas, Capvidia. Model-based definition and data you can actually trust downstream.
• When Your BOMs Fall Out of Sync Soufiane Elaamili, IQBrain. The EBOM-to-MBOM gap, seen from the AI side.
• Your Factory Has Data. Why Are Margins Still Disappearing? Bill Bither, MachineMetrics. Dashboards show what happened. What helps a manufacturer decide what to do next?
• AI Agents Shouldn't Mean Rebuilding Your Factory Software Tanmay Aggarwal, Lambda Function. Agents on top of the stack you already have.

For the BOM conversation, EBOM vs MBOM is the background reading. For the trustworthy-data one, What is Supply Chain Traceability? and What is CAD Interoperability?.

The platform view

• AI Agents Don't Just Look. They Take Action. Rahul Garg, Siemens DISW.
• The Skills Gap Is Already Here. What Can Industrial AI Do? Chris Pollack, Siemens DISW.
• What AI Changes on the Factory Floor Natan Linder, Tulip Interfaces.

Siemens and Tulip framed the moment the same way from opposite ends of the market: agents that act, with an engineer making the call, and the skills gap as the reason the timing is now rather than in five years. Tulip is scored as a composable platform in the MES guide; the Siemens portfolio runs through the Siemens Spotlight.

What I took home

Three things. The knowledge-capture theme was louder than the AI theme; five founders built companies around a retirement wave, not a model. The guardrail question, where the human sign-off sits, separated the serious vendors from the demos within about ninety seconds of each conversation. And the companies solving the boring problems, BOM sync and trustworthy data, are the ones the others quietly depend on.

Every company in this series is profiled on ThreadMoat, with funding, positioning and the ThreadMoat Strategic Disruption Potential score where one has been assigned. The ICC 2026 interviews, recorded the following week in Sacramento, pick up the same questions from the automation side.