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Every new engineer who starts at a company receives limited access, supervision, and earns trust over time, but AI agents often receive none of that. In this episode of Stay Sharp in Digital Engineering with Razorleaf, hosts Juliann Grant and Jonathan Scott are joined by Nick Link, co-founder of Bitoda to discuss what happens when AI agents break out of their test environments. They unpack recent high-profile incidents involving OpenAI and Anthropic, define key concepts like safety, security, and deterministic guardrails, and explain why treating AI agents like new hires is the safest way to bring AI into engineering and manufacturing organizations. Key Takeaways: The Hugging Face & Anthropic Incidents: What really happened when AI agents breached testing environments and reached external systems.Security vs. Safety: Security protects systems from external threats, while safety protects organizations from internal missteps.Guardrails vs. Sandboxes: Sandboxes provide virtual test environments, while guardrails enforce boundaries around what agents can do.Deterministic vs. Probabilistic Controls: Why prompt instructions are non-deterministic and weak guardrails, and why deterministic code controls (like custom shells and command whitelists) are necessary.Treat AI Agents Like New Hires: Implement onboarding, grant least-privilege access, and expand permissions gradually as capabilities are proven.Atomic Outcomes: Work backward from simple, atomic goals to keep agents focused on single areas of expertise.Auditing and Vendor Accountability: Maintain logs to trace agent actions and hold software vendors accountable when system guardrails fail.Guest Information: Nick Link: US Marine Corps veteran and co-founder of Bitoda (a managed application services firm supporting defense and aerospace MBSE programs) and Fawdy (an AI-driven incident response platform for IT operations). Resources & Links Mentioned: Become a Member of the Stay Sharp Community: https//staysharpindigitalengineering.com/become-a-memberPodcast Feedback & Contact: Email [email protected] this episode? Please subscribe to Stay Sharp in Digital Engineering with Razorleaf, leave us a rating or review, and share this episode with your engineering and IT colleagues!  Music 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.

In Episode 157 of Stay Sharp in Digital Engineering with Razorleaf, co-hosts Juliann Grant and Jonathan Scott welcome back Prithvi Singh Chauhan, Machine Learning Engineer at Geminus AI, for a closer look at the company, its computational autonomy platform, and the future of AI-driven engineering decisions. Geminus AI takes a different approach to industrial AI. Rather than replacing the physics-based simulators engineering teams already trust, its platform adds a decision layer that helps turn complex simulations into faster, actionable operational recommendations. By connecting existing models, operational data, and multiple physical assets, Geminus aims to help organizations make informed decisions in real time—with physics-based accuracy and an understanding of uncertainty. What You'll Learn in This Episode: Where Geminus AI fits in digital engineering: How its computational autonomy platform bridges the gap between traditional simulation and real-time operational decision-making.Why physics-based AI matters: Why high-consequence systems require more than conventional pattern recognition, particularly when decisions affect safety, production, cost, and sustainability.How faster decisions deliver business value: Hear how Geminus helped an energy operator develop pipeline and production diversion recommendations in just a few days after regional conflict damaged part of its network—a process that traditionally could have taken weeks.How the platform works: Explore its four core capabilities: reasoning, model building, model calibration and drift detection, and system-of-systems optimization.How Geminus builds trust: Learn how operators can validate recommendations against the high-fidelity simulators they already use.What it takes to deploy the technology: Discover how the platform works with existing simulators and data, supports cloud and on-premises environments, and accommodates different engineering and operational roles.Where computational autonomy is headed: Explore the vision for connecting assets across entire enterprises and enabling faster decisions around production, energy resilience, and sustainability.From Simulation to Operational Decisions: Many industrial organizations already have sophisticated simulation tools and physics-based models. The challenge is making those models fast, current, and connected enough to support decisions as operating conditions change. Geminus AI's computational autonomy platform is designed to address that challenge. It builds models using trusted simulators, monitors their accuracy against live operational data, incorporates uncertainty into decisions, and connects individual assets into larger systems. Prithvi shares a real-world example involving a damaged energy pipeline network, where the team developed recommendations to redirect production while maintaining operational constraints and customer commitments. The discussion also explores how model drift can reveal potential physical problems in an asset, making AI useful for maintenance as well as optimization. Meet the Guest Prithvi Singh Chauhan is a Machine Learning Engineer at Geminus AI, where he combines machine learning, high-fidelity simulation, and optimization to improve efficiency and sustainability across complex energy systems. His experience spans international energy projects and other high-consequence industries. He holds a master's degree in petroleum engineering from Texas A&M University, is a named inventor on four U.S. patents, and was named the Society of Petroleum Engineers' Young Engineer of the Year in 2026. Continue the Conversation For more context, listen to Prithvi's previous Stay Sharp in Digital Engineering with Razorleaf, Episode 154 appearance on operational digital twins, where he discusses the technology behind physics-based AI and its role in complex industrial systems. Subscribe to Stay Sharp in Digital Engineering with Razorleaf, for more conversations about digital engineering, product development, manufacturing, AI, and the technologies connecting them. Music 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.

In this episode of Stay Sharp in Digital Engineering with Razorleaf, hosts Juliann Grant and Jonathan Scott turn the spotlight on Razorleaf. Joined by CEO and founder Eric Doubell, the discussion provides an overview of Razorleaf, exploring its history, core values, services, products, and role in the digital engineering and manufacturing landscape.   Key Takeaways: Discover how Razorleaf acts as the glue connecting products, processes, and data across the digital enterprise.Learn about Razorleaf's boutique, vendor-agnostic approach to solving complex technical problems.Understand the core services offered across PLM, MES, integration, and data migration.Explore Razorleaf's proprietary products, including the CLOVER integration platform, Aras Manufacturing Suite, and 3DEXPERIENCE Tools.Hear real-world customer success stories across aerospace, industrial equipment, and warehouse automation.Gain insight into Razorleaf's core values and company culture.Get a preview of future trends in digital thread, integration, and AI applications in manufacturing.Main Points Covered: Defining Digital Engineering and Manufacturing: How Razorleaf fits into the technology landscape, managing product data across the entire lifecycle from design to disposal.Boutique and Vendor-Agnostic Approach: Why Razorleaf focuses on client technology needs rather than pushing specific software vendors.Consulting and PLM Services: Platform selection, roadmapping, implementation, customization, support, and de-customization across major platforms including Dassault 3DEXPERIENCE, PTC Windchill, Siemens Teamcenter, and Aras Innovator.MES, Integration, and Data Migration: Services spanning aerospace and discrete manufacturing platforms like iBase-t Solumina and First Resonance, along with integrations between ERP, PLM, MES, and CRM systems.Razorleaf Product Offerings: Overview of CLOVER, a low-code hub-and-spoke integration platform connecting dozens of systems, along with 3DEXPERIENCE Tools and the Aras Manufacturing Suite.Customer Success Stories: Examples of long-term client engagements, including a six-week integration deployment for an AI warehouse automation firm.Company History and Core Values: How Razorleaf grew from a CAD reseller spinoff into a global organization built on principles such as doing the right thing, being exceptional but humble, and taking the long view.The Future of Digital Engineering: How integration, digital thread maturity, and AI are shaping mid-market manufacturing solutions.Guest Information Eric Doubell: CEO and Founder of Razorleaf. Eric founded Razorleaf in 2000 with a background spanning software development, engineering, database administration, and consulting management across multiple industries.If you enjoyed this episode, please subscribe to Stay Sharp in Digital Engineering with Razorleaf on your favorite podcast platform, leave a review, and share this episode with colleagues in digital engineering and manufacturing. Stay tuned for Part 2, where we will dive deep into CLOVER. Music 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.

Is PLM dying, or has the engineering world simply outgrown it? Juliann Grant and Jonathan Scott welcome back industry veteran Doug Macdonald for round two to unpack his report, The Future of Product Lifecycle Management: After Forty Years, Where Are We and What's Next? Doug makes the case for FPHE (Future Platform of Hardware Engineering), a new category designed to replace legacy PLM systems that no longer fit the complex, multi-disciplinary nature of modern hardware development. Key Takeaways The Complexity Gap: Products have evolved from purely mechanical parts to complex systems with electronics, wireless tech, and embedded software, but legacy PLM hasn't kept pace.Beyond MCAD: FPHE platforms are independent of mechanical CAD and focus on connecting data across the whole product life cycle—from system requirements to field service.The Reality on the Ground: Many engineering organizations rely on broken integrations, spreadsheets, and manual communications because traditional systems fail to manage cross-discipline workflows.An Emerging Market: Early FPHE pioneers like Cognyx, Daluss, EXP Software, Flow Engineering, Intercax, Makersite, SPREAD AI, and Violet Labs are tackling these modern challenges, though broad adoption faces hurdles against established vendors.Guest Info Doug Macdonald: Lifelong innovation enthusiast, former developer, consultant, and marketer across companies like PTC, SAP, Sherpa, and Aras. Resources Mentioned Report: The Future of Product Lifecycle Management: After Forty Years, Where Are We and What's Next? by Doug MacdonaldEmail the show: [email protected] the show? Subscribe on Apple Podcasts or Spotify, leave us a review, and share this episode with an engineer or digital strategist who needs to hear it! Music 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.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott are joined by Prithvi Singh Chauhan, Machine Learning Engineer at Geminus AI. They explore the practical application of AI and machine learning to operational digital twins across oil and gas, manufacturing, and energy systems. The discussion covers why traditional physics models fall short in real-time operations and how combining physics-native engineering with fast machine learning solves speed, staleness, and domain silos. Key Takeaways Traditional physics-based digital twins are built for design and back-office engineering, making them accurate but often too slow, stale, and siloed for live operations.Scientific AI embeds fundamental laws of physics directly into machine learning models, providing millisecond-level predictions while staying reliable under unseen operating conditions.Multimodal AI enables systems to process varied data sources together, including sensor time series, lab reports, simulator outputs, and engineering drawings.Self-calibrating digital twins reduce manual recalibration cycles from months to real time, preventing operational drift and enabling better decision-making.The frontier of operational digital twins includes multi-system connected models, self-maintaining architectures, probabilistic risk outputs, pre-trained foundation physics models, and LLMs acting as conversational interfaces to orchestrate physics engines.Key Terminology: Scientific AI: Machine learning models with built-in physics equations (conservation of mass, energy, equations of state) rather than pure data inference.Multimodal AI: Absorbing disparate formats, frequencies, and data streams across complex engineering workflows.World Models: Pure observational learning versus physics-grounded models in live industrial environments.The AI-Enabled Digital Twin Stack: A layered breakdown covering the physics layer, data abstraction layer, physics-native models, continuous calibration, and decision optimization.The Future of Industrial AI: Connecting multi-unit constraints, models that self-detect drift, foundation models for physics, and using LLMs as orchestration interfaces.Guest Info Prithvi Singh Chauhan is a Machine Learning Engineer at Geminus AI, where he builds physics-native AI systems combining machine learning, high-fidelity simulation, and optimization for large-scale energy systems. His work spans assets across North America, Guyana, the Middle East, India, Malaysia, and Australia. He holds an M.Eng. in Petroleum Engineering from Texas A&M University and a B.Tech. from IIT (ISM) Dhanbad. Named SPE's 2026 Young Engineer of the Year and an inventor on four US patent applications, Prithvi has co-authored around 17 peer-reviewed papers and serves as Program Chair for SPE's Data Science and Engineering Analytics technical section. If you enjoyed this episode, please subscribe, rate, and leave a review on Spotify, Apple Podcasts, or wherever you listen to podcasts. Share this episode with colleagues and friends working in digital engineering, energy, and industrial operations! Music 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.

Halfway through a live database migration, Honeywell’s team lost their server connection. With no way to tell whether the scripts were running or failing, all they could do was wait. In this episode of Stay Sharp with Razorleaf, hosts Juliann Grant and Jonathan Scott sit down with Ryan Sims (Senior IT Engineer at Honeywell) and Brian Neff (MES Technical Team Lead at Razorleaf) to discuss how Honeywell Aerospace successfully upgraded its Solumina MES with zero shop floor disruptions. Key takeaways from the episode: The Drivers Behind Upgrading: How addressing deferred maintenance prevents today's stable systems from becoming tomorrow's technical debt.Parallel Infrastructure Strategy: Standing up new Kubernetes application clusters in advance while leaving active production running for a seamless cutover.The Power Outage Test: How a sudden network connection drop during database migration proved the value of an immediate, executable rollback plan.Testing Beyond Release Notes: Why unit, integration, and user acceptance testing (UAT) must focus on end-to-end business processes, database triggers, and hidden code dependencies.Post-Go-Live Risk Mitigation: Preserving a legacy QA environment post-launch as an insurance policy to isolate new bugs versus pre-existing system issues.Main Episode Points Why Modernize MES Infrastructure? Honeywell originally implemented Solumina to replace two legacy, high-tech-debt MES solutions. To keep Solumina from becoming tech debt itself, Honeywell needed to move off aging Red Hat 7 operating systems to Red Hat 8, transition to version i110.2, and simplify its underlying landscape by dropping MongoDB dependencies.  Architecting a Zero-Disruption Strategy To protect shop floor productivity, the team provisioned new application servers for the new Kubernetes cluster well before launch day. By maintaining existing ActiveMQ and Apache reverse proxy servers, they eliminated the need for IP or DNS changes across plant users and key integrations like SAP and Teamcenter. Cutting over—or rolling back—was as fast as switching a URL rewrite rule and restarting Apache.  Managing Unexpected Hurdles During the migration weekend, a power outage in a neighboring building disrupted the database connection. Because SQL logs filled up during the blind run, the team relied on a pre-planned full database backup to restore and re-run upgrade scripts without delaying operations. Later, when a minor SAP bill of materials (BOM) integration issue arose during post-launch smoke testing, the technical team authored a pre-processing fix within hours. Best Practices for Enterprise System Upgrades Leverage Non-Production Environments: Work through dev, test, and sandbox stages to refine custom IT security scripts before touching production.Set Go/No-Go Checkpoints: Establish strict time thresholds for rollback decisions using real-world data timing tests.Keep Old Environments Alive Temporarily: Maintain the previous version in a non-production QA state to verify whether post-launch issues are new or legacy.Audit Scheduled Tasks: Ensure legacy background tasks and cron jobs are disabled so old system nodes do not auto-start after server reboots.Guest Information Ryan Sims - Senior IT Engineer at Honeywell in Muncie, Indiana. Ryan supports the Solumina environment for Honeywell Aerospace in Clearwater, Florida, having managed seven non-production and production instances.Brian Neff - MES Technical Team Lead at Razorleaf. Brian brings over 20 years of experience across software development, database architecture, and Agile technical leadership.Resources Mentioned ●      Razorleaf Corporation ●      iBase-t Solumina MES Enjoyed the episode? Subscribe to Stay Sharp with Razorleaf on Apple Podcasts, Spotify, or your favorite podcast app.  Share this episode with fellow engineering and manufacturing IT leaders!  Have a question or topic suggestion? Email us directly at [email protected]. Music 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.

Digital engineering is moving fast, but the fundamental challenge remains: how do you get your mechanical, electrical, and procurement data to "talk" to each other? In this episode, we dive deep into the "Product Memory Flywheel" with Oleg Shilovitsky, co-founder and CEO of OpenBOM. We explore how OpenBOM is using AI agents to solve the industry's most persistent, "boring" problems—like CAD file management and Bill of Materials (BOM) errors—and why fixing these unglamorous issues is the key to true digital transformation. Key Takeaways ●  The Product Memory Flywheel: Learn about the concept of a data context layer that collects information from disparate systems (PLM, ERP, CAD) and activities. It creates a structured foundation for AI to reason across your entire product development lifecycle. ●  Solving "Boring" Problems: Oleg argues that real innovation doesn't always come from flashy tech, but from solving the painful, mundane tasks engineers hate. We discuss how prioritizing these pain points creates the most immediate value. ●  Agentic Workflows: CAD File Agent: A new, chat-based interface that handles file management, versioning, and syncing, shielding engineers from the complexities of traditional PDM systems.BOM Review Agent: A tool that utilizes "Check Cards" to automate validation, highlight errors (like quantity mismatches), and facilitate team collaboration directly within the BOM.●  The Future of Intelligence: How the "Flow" concept integrates data across enterprise systems, allowing companies to transition from manual, error-prone processes to a streamlined, automated digital thread. Featured Guest ●  Oleg Shilovitsky: CEO and Co-founder of OpenBOM, and author of the widely-read industry blog Beyond PLM. With over 25 years in the data management and manufacturing space, Oleg brings a unique perspective on bridging the gap between engineering and the supply chain. Resources & Links ●       OpenBOM: Explore the platform and its latest AI capabilities. ●       Beyond PLM: Oleg’s blog on PLM, digital transformation, and modern manufacturing strategies. Support the Show  If you enjoyed this episode, please subscribe, follow, and leave a review. Your feedback helps us bring you more content on the future of digital engineering.  Share this episode with a colleague who is tired of fixing BOMs in Excel!  Music 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.

How did we get from CAD and PDM to the PLM systems we use today? And have we really moved beyond PDM? In Stay Sharp Episode 151, hosts Juliann Grant and Jonathan Scott sit down with Doug Macdonald, a longtime PLM industry veteran and self-described “lifelong innovation enthusiast and recovering advocate for PLM,” to explore the history of product lifecycle management—and how the technology evolved alongside the products and businesses it was designed to support. Doug has been part of the industry since the early days of CAD/CAM and PDM, working across development, consulting, sales, advocacy, and marketing roles at companies including PTC, SAP, Sherpa, Aras, and CIMdata. His career provides a unique perspective on how the technology evolved and why some of the fundamental assumptions behind today's PLM platforms still remain. From CAD Files to PDM The conversation begins with the emergence of PDM as CAD moved into the mainstream. What started as a practical need to manage files, versions, and engineering data gradually became something much more significant. As companies accumulated more product data in centralized repositories, managing versions and protecting that information became increasingly important. The repository itself also created an unexpected consequence: once a company's intellectual property and product history were deeply embedded in a system, moving to another CAD platform became considerably more difficult. How PDM Evolved into PLM From there, the discussion follows the evolution toward PLM. As products became more complex, organizations needed to manage more than CAD files. Parts and assemblies led naturally to bills of material (BOMs), while increasing amounts of product data created a need for change management and configuration management. The terminology evolved as well. The industry moved through concepts such as PDM, collaborative product commerce (CPC), and ultimately PLM—even though many of the underlying capabilities were continuing to evolve rather than being completely reinvented. Has PLM Really Moved Beyond PDM? One of the central questions of the episode is whether PLM ever truly became something separate from PDM—or whether much of PLM remains an extension of PDM. Today's PLM platforms may include capabilities for quality, materials management, software, electronics, and other aspects of the product lifecycle. Yet, as Jonathan and Doug discuss, many organizations still primarily use these systems for managing CAD data, versions, structures, and related engineering information. That creates an interesting gap between the vision of PLM and how organizations actually use it. The MCAD Connection The conversation also examines the continued influence of mechanical CAD on the PLM market. Many of the industry's largest PLM platforms remain closely connected to CAD, and the hosts explore how that relationship may influence both product strategy and the architecture of today's systems. If MCAD remains a major source of revenue, there is an obvious incentive to continue building around that foundation. But products and manufacturing organizations have changed. Modern companies increasingly operate through complex global supply chains and collaborative networks rather than the highly vertically integrated models that characterized earlier generations of manufacturing. The question becomes whether today's PLM architectures have evolved enough to support that new way of working. Why Aren't Companies Using More of Their PLM Capabilities? If PLM platforms contain so many lifecycle capabilities, why aren't organizations using all of them? The discussion considers whether some modules were developed around the requirements of individual customers or particular historical markets rather than broader industry needs. A capability can exist within a platform without necessarily being the right fit for the way organizations operate today. This raises a larger question about the future of PLM: Are today's platforms designed for the manufacturing world we have—or the manufacturing world we used to have? What You'll Take Away In this episode, you'll hear: How CAD/CAM helped drive the emergence of PDMWhy managing versions and product data became such a critical problemHow PDM evolved toward BOM management, change management, and ultimately PLMThe role of collaborative product commerce in the industry's evolutionWhy PLM may still be heavily rooted in PDMHow the continued connection between MCAD and PLM influences the marketWhy some organizations aren't using the full range of capabilities available in PLM platformsHow the shift from vertically integrated companies to complex supply-chain networks challenges traditional PLM architecturesWhy understanding PLM's history may be essential to understanding where it needs to go nextWhat's Next? This conversation is only the beginning. Doug will return for a future episode to discuss where PLM and product platforms are headed next—and what the next generation of these systems may look like. Listen to Episode 151 and then stay tuned for the next conversation. Have thoughts about the evolution of PLM? Leave us a comment—we'd love to hear what you think. Until next time, stay sharp! Music 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.

150 Episodes of Digital Engineering Conversations—and Counting It’s a milestone worth celebrating: Stay Sharp in Digital Engineering has reached 150 episodes! In this special episode, co-hosts Juliann Grant and Jonathan Scott take a break from the usual guest format to look back at nearly three years of Stay Sharp. Together, they revisit the topics, trends, guests, and conversations that have shaped the podcast—and consider what’s still left to explore. From the beginning, the podcast has focused on the rapidly changing world of digital engineering, manufacturing, and product development. But as the hosts reflect on 150 episodes, one thing becomes clear: while the technology continues to change, many of the fundamental challenges remain remarkably consistent. From PLM to AI—and Everything in Between PLM has been a recurring theme since the earliest episodes. The hosts revisit the early What Is PLM? series, which remains one of the podcast's strongest-performing topics, and discuss why questions about PLM's role within the enterprise are still relevant today. They also look at how the conversation has expanded into other areas, including MES, quality, integration, migration, and data management. These may not always be the most glamorous subjects, but they are often where digital transformation succeeds—or fails. AI has become another major theme. While the hype around AI can sometimes feel repetitive, Juliann and Jonathan discuss why the conversation continues to evolve as companies move from experimentation toward practical implementation. A recurring lesson from many of their AI conversations is the importance of the underlying data foundation—and the fact that there is still no single answer for how organizations should approach it. Digital Twins, Digital Threads and MBSE The podcast's Digital Twin series gave the hosts an opportunity to explore a term that is often overused and misunderstood. Conversations with practitioners helped put digital twins into context and demonstrate how organizations are applying the concept in the real world. Digital thread has been another important—and still evolving—theme. Rather than viewing digital thread as a single project, the hosts describe it as an incremental process of connecting data and information across systems and business processes. Model-Based Systems Engineering (MBSE) is another topic they expect to remain relevant for years to come. Discussions with experts including Chris Finley and David Long helped demonstrate both the practical applications of MBSE and why it extends well beyond the traditional examples many people associate with it. The Less Glamorous Side of Digital Transformation Some of the most valuable conversations have focused on topics that don't always generate headlines: integration, migration, data quality, and unstructured data. The hosts reflect on conversations about everything from the massive amounts of information associated with aircraft to CAD migration and the challenges of moving data between systems and formats. These discussions reinforce an important reality: digital transformation isn't just about adopting new technology. It also means dealing with the existing data, systems, processes, and technical debt that organizations have accumulated over time. Digital Engineering and the Defense Industrial Base The podcast also spent considerable time exploring digital engineering and the defense industry, including cybersecurity, CMMC, and the Defense Industrial Base (DIB). Juliann and Jonathan discuss the potential opportunities for small and midsize manufacturers to participate in the DIB, as well as the challenges organizations face as cybersecurity and compliance requirements evolve. The People Behind the Technology Technology may be at the center of digital transformation, but many Stay Sharp conversations have highlighted another critical factor: people. The hosts discuss the importance of organizational culture, adoption, change management, partnerships, and the relationships between technology providers, customers, service providers, and systems integrators. Their Digital Product series has also provided an opportunity to go beyond product features and specifications to understand the people and cultures behind different software companies. As Jonathan points out, conversations reveal things about a company that aren't always apparent from a website or product sheet. A Few Favorite Guests With 150 episodes and an impressive roster of guests, there are plenty of conversations worth revisiting. Juliann and Jonathan highlight several memorable guests, including Brion Carroll, Patrick Hilberg, Jos Voskuil, Michael “Fino” Finocchiaro, Martin Eigner, Chris Finlay, David Long, Dan Miklovic, and others. They also give a special shout-out to Jen Ferello, who helped get the podcast off the ground and remains an important part of the Stay Sharp story. And, of course, the hosts recognize the many guests who have shared their expertise throughout the first 150 episodes. What's Next for Stay Sharp? Reaching 150 episodes isn't the end of the conversation. If anything, the hosts discover that there are still more questions than ever. They want to hear from listeners about the topics they should cover, companies and industries they should explore, and guests they should invite onto the show. They also plan to use LinkedIn surveys to make it easier for listeners to weigh in. Most importantly, Juliann and Jonathan thank everyone who has listened, shared feedback, approached them at conferences, appeared as a guest, or helped make the podcast possible. After 150 episodes, Stay Sharp in Digital Engineering is still doing what it set out to do: having thoughtful conversations about the technology, people, and ideas that help professionals stay sharp in digital engineering. Here's to the next 150 episodes. Music 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.

In this episode, we sit down with Oleg Shilovitsky, industry expert, author of Beyond PLM, and CEO of OpenBOM, to explore a transformative concept he calls "product memory." If you’ve ever looked back at a product design decision two years later and wondered, "Why did we make that choice?", you aren't alone. Oleg explains why product memory is the missing context layer that connects decisions to data, filling the gaps that traditional PLM, PDM, and ERP systems often miss. We discuss the "three parents" of product memory—including the critical role AI plays as a forcing function—and dive into the complex questions surrounding data ownership, security, and the future of business models in a post-SaaS world. Key Takeaways: What is Product Memory?  It’s a context layer that captures the "why" behind decisions, relationships, and conversations across a product's lifecycle—not just the static records found in PLM.The Three Parents of Product Memory: The concept is born from the need to capture decision-making context, the technological capability of context graphs, and the acceleration provided by AI agents.AI as a Forcing Function: Unlike previous tech trends (like cloud adoption), AI requires structured context to be effective. Without proper "memory," AI agents hallucinate, making the capture of this context an urgent priority rather than a "nice-to-have."Security & Ownership: While product memory raises questions about data privacy and intellectual property, Oleg argues it should be treated like any other process—with clear protocols and security standards.Future of Monetization: As we move beyond seat-based SaaS models, the industry is exploring new ways to monetize data and outcomes, though the exact business models for product memory are still emerging.Resources Mentioned: Beyond PLM Blog: https://beyondplm.comUpcoming Book: From CAD Files to Product Memory by Oleg Shilovitsky (available soon via beyondplm.com).If you found deep dive into the future of manufacturing valuable, please like, subscribe, and share it with a colleague or network peer who is tackling these same digital engineering challenges.  Music 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.

What if you could answer a complex engineering change request in minutes instead of days? In this episode of Stay Sharp: In Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome back Chris Finlay, former Vice President of Engineering Innovation at SAIC and current Digital Systems Engineering Director at Raytheon, to explore the tangible, practical benefits of the digital thread. We move past the buzzwords to discuss how to break down engineering silos, improve traceability, and implement a digital strategy that actually delivers value. Key Takeaways The Power of Traceability: Discover how digital threads allow you to query designs in real-time, moving from manual, document-based matrices to instantaneous verification.Breaking the Silos: Learn how a digital thread integrates disparate engineering domains—from systems architecture to mechanical CAD and software—allowing teams to collaborate without needing to master every single tool.The Role of Configuration Management: Understand why CM is the backbone of a successful digital thread, especially when moving from document-based artifacts to an "Authoritative Source of Truth."Start with the End in Mind: Get actionable advice on how to begin your implementation journey by focusing on your specific business goals rather than trying to trace every single data point at once.Episode Highlights Traceability on Steroids: Chris shares a real-world example from a Navy program where a complex engineering trace was completed in minutes, not days, saving hours of manual labor.The "Digital Cable" Concept: We discuss how the digital thread functions like a multi-strand cable, connecting various engineering disciplines and providing a single, consistent version of the truth.Impact Analysis: How a well-crafted digital thread enables you to perform impact analysis in minutes, revealing exactly which components are affected by a change—revealing complexity that was always there, but previously invisible.Overcoming Implementation Barriers: Advice for organizations of all sizes on how to start, when to automate, and why you should focus on "ad-libbing" and refining your processes rather than simply digitizing old workflows.Connect with Us Guest: Chris Finlay, Digital Systems Engineering Director at RaytheonPodcast: Stay Sharp in Digital EngineeringJoin the Conversation: Have questions for Chris or thoughts on this episode? Leave us a comment or contact us at [email protected]  If you enjoyed this deep dive into digital engineering, please rate, review, and subscribe on Spotify or Apple Podcasts. Share this episode with a colleague who is looking to streamline their engineering workflows!  Music 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.

Beyond the Hype: Christine Longwell on AI, PLM, and the Future of Digital Engineering The digital engineering industry is experiencing one of its most significant periods of transformation in decades—but separating meaningful innovation from marketing hype has never been more challenging. In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome industry analyst Christine Longwell, founder of PLM Insights. Drawing on a career that spans mechanical engineering, software implementation, competitive analysis, market research, and consulting, Christine shares a unique perspective on where product lifecycle management (PLM), AI, and digital engineering are really headed. The conversation explores why understanding real customer challenges starts on the factory floor, why software vendors often struggle to understand small and medium-sized manufacturers, and how service providers are becoming trusted advisors in technology selection. Christine also discusses the rapid rise of AI, why organizations should be cautious of bold predictions, and why people—not technology—remain the biggest obstacle to digital transformation. If you're trying to make sense of today's AI landscape while planning for tomorrow's digital engineering environment, this episode offers practical insights grounded in decades of industry experience.  In this episode you'll learn: Why successful digital transformation starts with understanding business processes—not just technology The importance of seeing customer challenges firsthand through factory visits and support teams Why SMB manufacturers remain underserved by traditional market research How AI is changing the PLM landscape—and where the hype ends Why organizational silos may be the biggest barrier to AI success The growing importance of trusted implementation partners and systems integrators Why independent customer research and win/loss analysis are becoming increasingly valuable How AI may accelerate convergence between PLM, ERP, and supply chain systems As AI reshapes the digital engineering landscape, one thing remains constant: successful transformation starts with understanding people, processes, and real business challenges—not just adopting the latest technology. Christine Longwell's unique perspective, spanning engineering, software vendors, market research, and consulting, reminds us that meaningful innovation comes from listening to customers, challenging assumptions, and making technology decisions based on business outcomes rather than hype. Whether you're evaluating PLM strategies, navigating AI adoption, or planning your next digital transformation initiative, this conversation offers practical insights to help you make smarter, more informed decisions. If you enjoyed this episode, subscribe to Stay Sharp in Digital Engineering on your favorite podcast platform so you never miss a conversation with industry experts shaping the future of product development and manufacturing. Have a question for Christine Longwell or a suggestion for a future episode? We'd love to hear from you. Leave a comment, reach out to the Razorleaf team, or email us at [email protected]. Be sure to share this episode with colleagues who are navigating AI, PLM, and digital transformation. Until next time, stay curious, stay connected—and Stay Sharp. Music 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.

Did the Department of Defense just put CMMC on hold? Not exactly. The Department of Defense recently announced a pause on one of the most significant upcoming requirements of the Cybersecurity Maturity Model Certification (CMMC) program, creating confusion across the defense industrial base. Does this mean contractors can slow down their cybersecurity efforts—or does it simply change how compliance will be evaluated? In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome back Steve Nichols, Razorleaf's government solutions expert, to explain exactly what changed, what didn't, and what defense contractors should be doing during this temporary pause. Steve breaks down the July announcement, explains why companies are not off the hook for CMMC compliance, and discusses why maintaining strong cybersecurity practices remains essential regardless of how future certification requirements evolve. In this episode, you'll learn: Why the government paused third-party CMMC Level 2 assessmentsWhat requirements still remain in effectThe difference between self-certification and third-party certificationThe real costs of preparing for a CMMC assessmentWhy good cybersecurity hygiene is still criticalHow organizations should prioritize the 110 security controlsWhat the government may change after the public comment periodHow small businesses could be affectedWhy waiting for final guidance may be a risky strategyHow AI could eventually play a role in cybersecurity assessmentsKey Takeaways The current pause affects only the third-party assessment requirement for CMMC Level 2 certification. Organizations are still responsible for meeting applicable cybersecurity controls and certifying compliance where required. Companies should continue improving their security posture rather than assuming requirements will disappear. Steve also explains that cybersecurity compliance should be viewed as an ongoing business process—not a one-time audit. Organizations that continue improving their IT environment today will be in a much stronger position regardless of how the government ultimately adjusts the CMMC program. Featured Guest Steve Nichols leads Razorleaf Government Solutions practice, helping defense contractors navigate digital transformation, cybersecurity requirements, PLM strategy, and government compliance initiatives. His experience spans startups, commercial software companies, and federal programs, making him a trusted advisor for organizations operating within the defense industrial base. If your organization works with the Department of Defense or plans to enter the defense supply chain, this conversation will help you understand what today's changes mean—and how to prepare for what's next. 🔔 Subscribe for more conversations about digital engineering, PLM, cybersecurity, manufacturing, AI, and digital transformation. Music 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.

Software licensing used to be one of the most predictable parts of an engineering organization's technology budget. You bought licenses, planned annual maintenance, and forecasted costs years in advance. But as AI assistants, cloud platforms, token-based licensing, and API-driven workflows become the norm, that predictability is disappearing. In this episode of Stay Sharp in Digital Engineering, co-hosts Juliann Grant and Jonathan Scott welcome back Paul Empringham, VP of CAD, Simulation, and PLM at NYBS Consulting, to explore how software licensing is evolving—and why engineering leaders need to start thinking differently about budgeting, governance, and AI consumption before costs spiral out of control. Building on their previous discussion about traditional licensing models, Paul explains why the industry's move to cloud platforms and tokenized licensing creates both opportunities and new financial risks. As AI becomes embedded directly into CAD, PLM, and engineering applications, organizations will need better visibility into software usage, stronger governance, and new strategies for managing unpredictable consumption-based costs. In this episode you'll learn: Why cloud and SaaS platforms are fundamentally changing software licensingHow token-based licensing differs from AI token consumptionWhy AI assistants may create unpredictable engineering software budgetsThe hidden cost of API calls, automation, and AI-powered workflowsHow vendors are moving toward usage-based licensing modelsWhy engineering leaders need guardrails before deploying AI at scaleThe importance of measuring software usage before negotiating renewalsWhy visibility into licensing has never been more importantKey Discussion Topics The shift to cloud-first engineering software Software vendors are rapidly moving customers away from on-premises installations and toward cloud platforms. While this simplifies upgrades and accelerates feature delivery, it also reduces customer visibility into actual software usage. Understanding tokenization Paul explains the growing trend toward token pools, where engineering teams consume tokens for advanced capabilities across multiple software products instead of purchasing individual specialty licenses. This provides greater flexibility—but also introduces new complexity. AI changes everything As AI assistants become embedded inside CAD, PLM, and simulation tools, companies may soon be paying for both software licenses and AI consumption. Unlike traditional licensing, AI usage can fluctuate dramatically, making annual budgeting far less predictable. Managing the unknown Engineering organizations need better data than ever before. Understanding who uses which tools, how often they use them, and where AI is being adopted will become essential for controlling costs and preparing for future licensing changes. Memorable Quote "For the first time in our lives, I don't think we can predict what our software is going to cost us." Featured Guest Paul Empringham Vice President of CAD, Simulation & PLM NYBS Consulting Paul specializes in engineering software licensing, optimization, and governance for global manufacturing organizations. He has helped companies maximize software investments while navigating increasingly complex licensing models across the CAD, PLM, and simulation landscape. Music 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.

What if the most expensive part of your digital engineering environment isn’t the software license? In this episode, we sit down with Eric Smith, VP of Services at Razorleaf, to peel back the curtain on the "hidden" costs of running complex systems like PLM, MES, and CAD. We move beyond the initial project setup to explore the "sustainment" phase—where time, money, and productivity often drain away unnoticed. Whether you are in engineering, IT, or manufacturing leadership, this conversation provides a new framework for understanding why your environment is becoming increasingly complex and how to manage the unexpected challenges of modern digital infrastructure.   Key Takeaways: ●       The Sustainment Trap: While initial implementations get all the budget, the real costs reside in ongoing maintenance, break-fix cycles, and the "interoperability spaghetti" of modern system architectures. ●       The Productivity Tax: Every update, patch, or system hiccup causes interruptions. Research shows that "deep work" recovery takes an average of 23 minutes after a distraction, making seemingly minor tech updates a massive hidden expense. ●       IT vs. Engineering: We break down where the line should be drawn. IT excels at commoditized tasks (security, networks, cloud infrastructure), but "Digital Environment Sustainment" requires niche domain knowledge that often falls into a gray area between departments. ●       The "Why Now?" Factor: The landscape has shifted from isolated systems to deeply integrated microservices, cloud-hybrid architectures, and heightened security requirements, making maintenance significantly more complex than it was a decade ago. Main Points: ●       Defining Digital Environment Sustainment: Moving past "managed services" to a model that accounts for the specific, specialized needs of engineering and manufacturing software. ●       The Architecture Explosion: How the shift from a single server to containerized microservices has exponentially increased the complexity of maintaining even a single application. ●       The Human Cost: Discussing the importance of Change Management (OCM) and the high overhead of onboarding new engineers when systems are constantly evolving. ●       Specialized Expertise: Why traditional, fixed-team managed services may not be enough to handle the deep subject matter expertise required for complex CAD/PLM/MES environments. Are you seeing these hidden costs in your organization? Let us know your "horror stories" or where you’ve seen the biggest drain on productivity. Drop us a comment or send us a note! If you found this episode helpful, please like, subscribe, and share it with your engineering team—it helps us keep the content coming.  Music 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.