Episode #115: AI + PLM — The Data Readiness Reality Check

Episode SummaryAI is everywhere, but in engineering and manufacturing, hype often outpaces reality. In this episode of Stay Sharp in Digital Engineering, we unpack what it actually means to be “AI-ready” and why most organizations are struggling to get there.Hosts Juliann Grant and Jonathan Scott are joined by Graham Law, PLM Solution Architect at Razorleaf, to explore the unglamorous but essential foundation of successful AI initiatives: clean, governed, connected engineering data. With over 16 years in PLM and a deep IT background, Graham offers a grounded perspective on what works, what fails, and where organizations should realistically start.This conversation cuts through buzzwords to focus on practical steps, real risks, and how PLM enables AI to deliver meaningful insight rather than confident misinformation.🔑 Key TakeawaysAI success in engineering is 80% data hygiene and 20% technologyMessy, duplicated, or poorly linked data leads to confidently wrong AI answersPLM provides the structure, context, and governance AI depends onOrganizations should start small, not attempt to “boil the ocean”AI can surface insights humans miss, but only if the data foundation is solidGovernance is just as important as cleanup—stop creating bad data going forward👤 GuestGraham LawPLM Solution Architect, Razorleaf16+ years in Product Lifecycle Management Experience with Windchill, Teamcenter, Aras, SOLIDWORKS PLM                                                                   Former IT Manager with deep systems expertise📣 Call to ActionIf this episode helped sharpen your thinking about AI and engineering data:●      Subscribe to Stay Sharp in Digital Engineering●      Leave a review on Apple Podcasts or Spotify●      Share this episode with a colleague working on AI or PLM initiativesMusic is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2026 Razorleaf Corp. All Rights Reserved.