Manufacturing Happy Hour · 16 Jan 2024 · 53m
Nathan Lasnoski, CTO at Concurrency, discusses how manufacturers can leverage AI to drive ROI, automate intuition, and unlock human creativity by shifting from manual tasks to strategic decision-making. He contrasts the current AI revolution with the Industrial Revolution, arguing that AI should enhance rather than replace human potential, and outlines a framework for executives to identify disruptive and incremental use cases. Lasnoski emphasizes the importance of engaging frontline workers, ensuring data readiness for specific use cases, and maintaining a focus on business mission over technology for its own sake.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Sourcing & supply chainConcurrency AI models$50,000,000 saved
An AI model was used to forecast demand and inventory for a $2B remanufacturing company, resulting in $50M in annual inventory savings.
“So for this one company we worked with, it was about a $2000000000 company and year over year, they saved about $50000000 of inventory efficiencies that they can then redirect to another part of their business.”
Nathan Lasnoski · Hear it at 10:36
Marketing & salestime to quote from hours to minutes
AI automated the creation of quotes from customer emails, reducing the time to generate a quote from hours to minutes for a company with 600 sales reps.
“So what they used AI for was to ingest that, that request from the customer, and then automate the creation of that quote that they can then turn right back around and get in front of the customer, and in this case, only a couple minutes. So they reduce time to quote from like hours to minutes.”
Nathan Lasnoski · Hear it at 20:18
companies scaling up
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Paul Lachance, Chief Industry Strategist at Software Equity Group, draws parallels between the current AI revolution and the steam, electricity, and automation eras to advise manufacturing leaders on navigating disruption. He argues that maintenance teams are strategic levers for increasing organizational capacity through uptime, rather than just fixing broken equipment. Lachance illustrates how agentic AI can automate cross-system data analysis to solve operational issues like OEE slips, while emphasizing that technology must serve defined business outcomes rather than driving adoption through hype.
Al Whatmough, CEO of Toolpath, discusses the role of AI in CNC machining automation, explaining how his company aims to bridge the gap between software and hardware through its acquisition of Chatter. He argues that manufacturers should focus on automating mundane tasks to free up cognitive load rather than chasing complex, high-touch solutions, drawing on his experience at Autodesk. Whatmough also reflects on the generational divide in the industry and the strategic importance of solving simple problems well to build a scalable platform.
Christopher Nguyen, CEO & Co-Founder of Aitomatic, explains why effective AI in manufacturing requires fusing IIoT data with the tacit knowledge of frontline workers. He argues that pure data-driven models fail in physical domains due to the 'data wall' and lack of failure examples, necessitating the capture of domain expertise. Nguyen discusses how his software helps industrial companies translate unstructured human knowledge into structured forms to improve defect detection and process optimization.
Trond Undheim, co-author of Augmented Lean and lead ecosystem evangelist at Tulip, argues that manufacturing leaders must shift from 'mindless automation' to an 'augmented workforce' model that prioritizes human innovation over surface-level efficiency. He explains that digital transformation should not simply be pasted onto existing lean processes but must fundamentally reshape organizational structures to simplify interfaces for frontline workers. Undheim cites companies like Stanley Black & Decker and DMG Mori as examples of organizations successfully implementing this human-centric approach to technology.
Daniel First, founder and CEO of Axion, explains how AI is transforming quality and innovation roles in manufacturing by turning overwhelming streams of customer, device, and service data into actionable insights. He argues that quality teams are shifting from plant-floor oversight to strategic partners across engineering, marketing, and supply chain, using AI to accelerate product evolution and break down silos. First highlights specific examples, such as Shark Ninja’s ice cream machine and power systems in mines, to illustrate how unexpected customer usage drives innovation and how AI agents can detect root causes faster than humans.
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