#152: OpenBOM's New AI Agents Solving the Problems Everyone Ignores

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 PDMThe 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 PLMFrom 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 ConnectionThe 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 AwayIn 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 CountingIt’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 BetweenPLM 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 MBSEThe 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 TransformationSome 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 BaseThe 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 TechnologyTechnology 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 GuestsWith 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 TakeawaysThe 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 HighlightsTraceability 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 UsGuest: 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 EngineeringThe 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 TakeawaysThe 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 GuestSteve 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 TopicsThe shift to cloud-first engineering softwareSoftware 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 tokenizationPaul 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 everythingAs 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 unknownEngineering 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 GuestPaul Empringham Vice President of CAD, Simulation & PLM NYBS ConsultingPaul 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.

Artificial intelligence has dramatically lowered the barrier to software development, making it easier than ever for manufacturers, engineering organizations, and startups to build custom applications. But just because you can build your own software doesn't always mean you should.In Episode 143 of Stay Sharp in Digital Engineering, co-hosts Juliann Grant and Jonathan Scott welcome back Jonathan Girroir, Technical Evangelist at Tech Soft 3D, for a forward-looking discussion about how AI-assisted coding is reshaping engineering software development. Together they explore the growing "build versus buy" dilemma, why software components are becoming more valuable than ever, and how organizations should think about protecting their intellectual property while embracing modern cloud architectures.From AI-generated code and engineering toolkits to data hubs, APIs, and digital thread strategies, this episode examines the technology trends that will influence how manufacturers develop, deploy, and manage engineering software over the next decade.In this episode, you'll learn:Why AI-assisted coding is fueling an explosion of engineering software startupsHow manufacturers are building custom applications faster than everWhen it makes sense to build software—and when buying is the better strategyHow reusable software components accelerate product developmentWhy data sovereignty has become a critical considerationThe hidden costs of maintaining custom softwareHow cloud delivery models and hybrid architectures are changing engineering workflowsWhy APIs and data hubs may become more important than traditional file formatsThe growing role of standards like STEP AP242, QIF, and Universal Scene Description (USD)Why connected engineering data is becoming one of manufacturing's greatest competitive advantagesHow digital thread strategies create richer AI training data for future innovationKey TakeawaysAI isn't replacing engineering software developers—it's making software creation dramatically more accessible. Organizations now have the opportunity to prototype and deploy specialized engineering applications much faster than in the past.However, successful organizations won't simply build everything themselves. They'll carefully evaluate where commercial software provides mature capabilities and where custom development creates true competitive differentiation.The conversation also highlights an important shift occurring across manufacturing: engineering data is evolving from isolated files into connected, API-driven data ecosystems. Companies that invest in clean, connected digital thread data today will be better positioned to leverage AI tomorrow.Whether you're evaluating PLM modernization, developing engineering applications, or planning your organization's AI strategy, this episode offers practical guidance for making smarter technology decisions.Be sure to subscribe for more conversations on digital engineering, PLM, AI, MBE, digital thread, manufacturing, and engineering technology.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.