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. 

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© 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 startups
• How manufacturers are building custom applications faster than ever
• When it makes sense to build software—and when buying is the better strategy
• How reusable software components accelerate product development
• Why data sovereignty has become a critical consideration
• The hidden costs of maintaining custom software
• How cloud delivery models and hybrid architectures are changing engineering workflows
• Why APIs and data hubs may become more important than traditional file formats
• The 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 advantages
• How digital thread strategies create richer AI training data for future innovation

Key Takeaways

AI 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.

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© 2026 Razorleaf Corp. All Rights Reserved.

Software licensing may not be the first thing engineers want to discuss, but it can have a massive impact on budgets, productivity, and digital transformation success.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott are joined by Paul Empringham, VP of CAD, Simulation & PLM at NYBS Consulting, to unpack the increasingly complex world of engineering software licensing. Drawing on decades of experience working with organizations including Rolls-Royce, BAE Systems, Red Bull Racing, JCB, and Sunseeker, Paul explains why many companies are unknowingly overspending on CAD, PLM, simulation, and engineering software—and how better data can dramatically improve licensing decisions.

The discussion covers the evolution from perpetual licensing to subscriptions, named-user models, and cloud licensing, along with practical strategies for optimizing license usage, avoiding costly compliance mistakes, and negotiating smarter renewals. Whether you're an engineering leader, IT professional, procurement specialist, or PLM administrator, this episode offers actionable advice that can save your organization significant time and money.

Key Topics Discussed:

• How engineering software licensing has evolved from perpetual licenses to subscription and cloud-based models
• The differences between concurrent, named user, and subscription licensing
• Why engineering, IT, and procurement all play critical roles in software purchasing
• What software license optimization actually means—and why usage data matters
• Common licensing mistakes that lead to unnecessary costs and compliance risks
• How organizations can negotiate license "remixes" instead of purchasing additional software
• The future of engineering software licensing, including token-based consumption models

Key Takeaways:

Know How Your Software Is Really Being Used

Most organizations don't actually know how many licenses they need. Monitoring real-world usage reveals idle licenses, underutilized features, training gaps, and opportunities to reduce costs without impacting productivity.

Don't Buy Bigger Bundles Than You Need

Vendors often encourage customers to purchase larger software bundles with additional features. Without usage data, companies frequently pay for capabilities they rarely—or never—use.

Compliance Is More Complicated Than You Think

Many licensing violations happen accidentally. Geographic restrictions, shared logins, outdated contract terms, and improper license usage can all create compliance issues that organizations don't discover until an audit.

License Data Creates Better Negotiations

Understanding actual license utilization gives organizations leverage during renewals. Instead of automatically renewing everything, companies can negotiate license remixes, exchange unused products, or right-size their environment.

The Licensing Landscape Is Still Changing

Engineering software continues to evolve toward subscription, cloud delivery, and token-based consumption. Organizations should understand where their software vendors are headed before making long-term purchasing decisions.

Memorable Quotes:

"Most companies simply don't know who's using which software—and where."

"Knowledge is power when you're negotiating software licenses."

"Don't be afraid to ask for a license remix. Everything is negotiable."

"Start small, understand your usage, and build from there."

Connect With Us:

Have questions about software licensing or want to continue the conversation? Leave a comment or reach out to the Stay Sharp team.

If you enjoyed this episode, be sure to subscribe, leave a review, and share it with a colleague who manages engineering software.

Stay Sharp in Digital Engineering is the podcast exploring the people, technologies, and ideas shaping the future of product development, manufacturing, and digital transformation.

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Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2026 Razorleaf Corp. All Rights Reserved.

Getting New Methodologies Off the Ground: Lessons from Digital Engineering Transformation with Chris Finlay

Implementing a new engineering methodology sounds straightforward on paper. In reality, it often means navigating organizational resistance, changing long-standing habits, proving value quickly, and building momentum one success at a time.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with Chris Finlay, Vice President of Engineering Innovation at SAIC, to discuss his journey helping organizations adopt Model-Based Systems Engineering (MBSE), digital engineering, digital thread strategies, and AI-enabled engineering practices across the defense industry.

Chris shares candid stories from the front lines of transformation, including early MBSE projects, lessons learned overcoming resistance, and the practical strategies that helped move digital engineering from experimentation to enterprise adoption.

Key Topics Discussed

The Accidental Start of Digital Engineering

Chris recounts how one of his earliest experiences with what would later be called MBSE started simply as a way to manage overwhelming engineering complexity. By leveraging modeling tools and connecting data across engineering artifacts, his team unknowingly created one of their first digital threads long before the term became commonplace.

Why Resistance is Normal

New methodologies often challenge established workflows and expose inefficiencies. Chris explains why resistance from program leaders and engineers isn't necessarily opposition—it’s often a rational response to perceived risk. Successful transformation requires empathy, patience, and meeting people where they are.

The Power of Quick Wins

One of Chris's strongest recommendations is to avoid trying to "boil the ocean." Instead, organizations should identify a manageable use case, demonstrate measurable value quickly, and build momentum through visible successes.

The Story of the Skeptical Product Owner

Chris shares a memorable example of an engineering leader who entered a model review determined to prove MBSE didn't work—only to immediately identify critical issues that the model exposed. The experience became a powerful demonstration of how digital engineering can reveal problems earlier and at lower cost.

Finlay's Five Firsts of Digital Engineering

Chris outlines the principles that have guided his transformation efforts for over a decade:

1. Digital engineering is not a substitute for good systems engineering.
2. Digital engineering exposes bad engineering practices faster.
3. Connected data enables the digital thread—but avoid overconnecting everything.
4. You cannot hire your way out of transformation; upskilling is essential.
5. Organizations that haven't started their digital engineering journey are already behind.

The Gray Beard Phenomenon

One of the most interesting insights from the discussion is Chris's "Gray Beard Phenomenon." Engineers working closely with digital models rapidly become subject matter experts because they gain access to the system's "crystal ball"—the connected knowledge captured within the model.

Measuring and Celebrating Success

Transformation efforts gain traction when organizations track meaningful metrics and celebrate progress. Chris emphasizes that cultural change requires both measurable outcomes and visible recognition of teams that deliver value.

Key Takeaways

• Digital engineering succeeds when it solves real problems, not when it is adopted for its own sake.
• Cultural transformation is often more challenging than technology implementation.
• Early wins build credibility and momentum.
• Metrics help drive behavioral change and secure organizational support.
• Upskilling existing teams is often more effective than hiring new specialists.
• Celebrating success is critical for sustaining transformation efforts.

About the Guest

Chris Finlay is Vice President of Engineering Innovation at SAIC, where he leads enterprise digital engineering transformation initiatives spanning MBSE, digital thread, digital twin, and AI-enabled engineering. With more than 20 years of experience in defense systems engineering, Chris has helped scale digital engineering capabilities across major organizations and mission-critical programs.

Connect With Stay Sharp in Digital Engineering

Have questions about digital engineering transformation, MBSE adoption, or digital thread strategies? Leave a comment, connect with the Razorleaf team, or reach out to continue the conversation.

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© 2026 Razorleaf Corp. All Rights Reserved.

Human-Centered PLM, Practical AI, and the Future of Digital Engineering

What happens when you take a traditional PLM conference out of a corporate convention center and place it inside a Spanish bodega with a dirt floor? According to the attendees of the Share PLM Summit 2026, you create an environment where people—not technology—become the focus of the conversation.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome Razorleaf International team members Ashish Kulkarni and Luc Van Helmerijck to share their firsthand experiences from the Share PLM Summit in Jerez de la Frontera, Spain. The discussion explores standout presentations, emerging AI use cases, organizational change management, and why Share PLM continues to redefine what a modern industry conference can be.

Key Topics Discussed:

A Different Kind of PLM Conference

Unlike traditional industry events, Share PLM intentionally creates a more human-centered experience. The unique venue, informal atmosphere, and collaborative discussions encourage meaningful conversations about the people side of digital transformation.

The Mood Barometer: Measuring Change Management Success

One of the most memorable presentations came from Andreas Wank of Pepperl+Fuchs, who shared his company's PLM implementation journey through a "mood barometer." Instead of focusing solely on project milestones, the presentation tracked employee sentiment throughout the project lifecycle, highlighting the critical role organizational change management plays in successful PLM deployments.

Practical AI Use Cases in PLM

Antonio Casaschi from ASSA ABLOY presented eight real-world AI applications being used across the organization. Rather than discussing AI as a future possibility, the presentation demonstrated practical ways AI can help companies better capture, organize, and leverage institutional knowledge.

Key takeaways included:

• AI should augment people, not replace them.
• The greatest value comes from solving business problems, not simply deploying technology.
• Organizations should focus on current-generation AI capabilities rather than outdated perceptions of AI tools.

AI Happens With People, Not To People

A presentation from Share PLM co-founder Helena Gutierrez emphasized a powerful concept:

"Make AI something that happens with people and not to people."

Her framework highlighted how organizations can automate commodity tasks while elevating uniquely human contributions such as creativity, decision-making, and strategic thinking.

PLM's Role in the AI Era

While AI dominated many conversations, attendees agreed that PLM remains foundational. Rather than replacing PLM, AI is creating new opportunities to improve adoption, simplify implementation, and enhance productivity across product development organizations.

The Concept of "Cognitive Surrender"

Industry thought leader Jos Voskuil explored both the promise and risks of AI, introducing the concept of "cognitive surrender"—the growing tendency for people to outsource thinking to AI systems. The discussion balanced optimism about innovation with caution about maintaining critical thinking skills in an AI-driven world.

Networking That Actually Matters

Beyond the presentations, attendees highlighted the value of the event's networking opportunities. The informal setting fostered meaningful conversations among consultants, software providers, customers, and industry leaders, leading to potential partnerships, customer discussions, and collaboration opportunities.

Episode Highlights:

• Why Share PLM's unconventional format continues to resonate with attendees
• How Pepperl+Fuchs used employee sentiment to guide a PLM transformation
• Eight practical AI use cases being deployed at ASSA ABLOY
• Real-world examples of AI augmenting product lifecycle management
• The growing debate: Is AI evolutionary or disruptive for PLM?
• Lessons learned from industry leaders navigating digital transformation
• How Razorleaf's Clover integration platform sparked conversations across the event

Featured Guests:

Ashish Kulkarni
Director of Growth, Razorleaf International

Luc Van Helmerijck
Business Development and Strategic Alliances Leader, Razorleaf International

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If you enjoyed this episode, subscribe to Stay Sharp in Digital Engineering and join us as we explore the technologies, processes, and people shaping the future of product development and manufacturing.

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© 2026 Razorleaf Corp. All Rights Reserved.

The Company Behind the Software You Use Every Day 

If you've worked with CAD, PLM, simulation, or digital engineering software, chances are you've relied on Tech Soft 3D technology—even if you've never heard of the company. In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with Jonathan Girroir, Technical Evangelist at Tech Soft 3D, to explore the foundational technologies that power hundreds of engineering applications across the industry. 

Jonathan Girroir explains how Tech Soft 3D provides the "building blocks" behind many of the world's leading engineering software platforms, including data translation, visualization, simulation, and graphics technologies used by more than 750 companies. From major software vendors to aerospace manufacturers and emerging startups, Tech Soft 3D enables organizations to access, visualize, and exchange complex engineering data more efficiently. 

The conversation also explores the company's unique position within the digital engineering ecosystem, where customers, competitors, and partners often overlap. Jonathan Girroir shares how Tech Soft 3D works with industry leaders such as Autodesk, Siemens, and Dassault Systèmes while simultaneously helping software vendors and manufacturers build next-generation engineering solutions. 

Finally, the discussion turns to artificial intelligence and the future of engineering data. Rather than focusing on chatbots and assistants, Tech Soft 3D is investing in ways to prepare, structure, and leverage engineering data for machine learning applications, helping organizations unlock insights from CAD models, metadata, manufacturing information, and product structures. 

Key Topics Discussed: 

• What Tech Soft 3D does and why it's embedded in so many engineering applications 
• The role of data translation, visualization, and interoperability in digital engineering 
• Supporting CAD, PLM, simulation, and manufacturing software ecosystems 
• How manufacturers use software components to build custom internal applications 
• The importance of engineering data access and data quality 
• Tech Soft 3D's relationships with major software vendors and technology partners 
• Cloud, desktop, mobile, and hybrid deployment strategies 
• The future of AI and machine learning in engineering environments 
• Why organizations should begin experimenting with AI tools today 

Memorable Quote:

"We're the company that everybody uses but nobody knows." 

Key Takeaways:

• Tech Soft 3D Powers the Industry Behind the Scenes 
• Many of the engineering applications professionals use every day rely on Tech Soft 3D technology for data translation, graphics, simulation, and visualization capabilities. 
• Engineering Data Is the Foundation for AI 
• As AI adoption grows, the ability to access, clean, structure, and understand engineering data becomes increasingly important. Tech Soft 3D is focused on enabling machine learning applications that work directly with engineering models and product data. 
• Manufacturers Are Building More Custom Software 
• Beyond software vendors, many large manufacturers are creating internal applications tailored to their unique workflows, leveraging software components rather than building everything from scratch. 
• AI Literacy Matters 
• Organizations don't need all the answers today, but they do need to start experimenting. Understanding how AI tools work and where they can provide value will be essential for future competitiveness.  

About Our Guest: 

Jonathan Girroir is Technical Evangelist at Tech Soft 3D. With more than two decades of experience in 3D data translation, simulation, and computer graphics, he helps organizations accelerate innovation through engineering software technologies and digital engineering solutions. 

 Listen, Subscribe, and Stay Sharp 

Enjoyed this episode? Subscribe to Stay Sharp in Digital Engineering for conversations with industry leaders, technology innovators, and practitioners shaping the future of product development, manufacturing, and digital transformation. 

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© 2026 Razorleaf Corp. All Rights Reserved.

Is your manufacturing business leaving defense contracts on the table?

In this episode of Stay Sharp: Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with two returning experts to break down one of the most misunderstood opportunities in manufacturing: the Defense Industrial Base. Whether you're running a five-person shop or a 500-person operation, there is a lane for you in the DIB, and today's guests explain exactly how to find it.

Guests:

Steve Nichols heads the public sector and defense group at Razorleaf, bringing decades of experience across technology, operations, strategy, and business development spanning organizations from startups to large enterprises including Silicon Graphics, CACI, MRI Software, and Razorleaf Government Solutions.

John Biagioni is the President of Lampin Corporation and brings deep manufacturing expertise shaped by a career that started on the shop floor as a machinist. He has nearly a decade of leadership experience as president of Dynisco, Viatran, and DJ Instruments, holds four patents in sensing and rheological instruments, and is a published authority on operational strategy.

What You'll Take Away:

• What the Defense Industrial Base actually is, including the NIB, the hidden DIB, the Marine Industrial Base, and sub-categories most SMBs never hear about
• Why the DLA currently has 22,000 non-bidded parts and what that gap means for small manufacturers
• How CMMC certification works, what it costs ($50K to $200K+), and why you can actually include that cost in your first bid
• The difference between CUI and classified information, and why the safe move is to protect everything
• Why poor data quality on aging weapon systems creates both friction and opportunity for SMBs
• What it means to be the "easy button" for a prime contractor, and why that's a smarter goal than chasing prime status yourself
• How the variable capacity model, or "Uberization" of the DIB, could give smaller shops a real entry point into defense contracts
• State-level grants (including $30,000 programs in Massachusetts and Connecticut) that can offset CMMC compliance costs
• First steps any SMB can take today: SAMs registration, getting a CAGE code, connecting with prime supplier portals, and joining DIB-focused industry groups

Key Insight:

The government's goal of building a Civil Reserve Manufacturing Network means the Department of Defense is actively looking for shops with verified process capabilities, not just finished products. If your shop can demonstrate capacity, you could be "put in stasis" and called on when demand spikes.

Resources Mentioned:

DLA (Defense Logistics Agency) non-bidded parts spreadsheet available for public download at dla.mil

SAM.gov: System for Award Management, the starting point for any DIB entry

PIEE: Procurement Integrated Enterprise Environment for vendor registration

CMMC (Cybersecurity Maturity Model Certification): cybercoe.osd.mil

Razorleaf Government Solutions

Lampin Corporation

Contact the Stay Sharp team: [email protected]

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Subscribe to Stay Sharp: Digital Engineering on your favorite podcast platform and leave us a review. It helps us reach more engineers, operators, and manufacturing leaders doing the hard work of keeping industry sharp. Got a topic you want us to cover? Email us at [email protected] or drop a comment wherever you're listening. 

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What happens when you lock hundreds of PLM practitioners, digital engineering leaders, and top aerospace and defense minds in a room to talk about AI? You get a reality check. In this episode, Juliann Grant, Jonathan Scott, and Andrew Halley sit down to recap the packed two-day CIMdata PLM/PDT Roadmap 2026 Conference held just outside Washington, D.C.

Forget the polished vendor slides and standard generative AI hype—this is a look into the "messy data" reality that engineering teams face behind closed doors. The team breaks down real-world case studies from industry giants, handles the core questions of data governance, and discusses why AI won't magically fix a broken digital thread—but why you must start using it anyway.

Key Takeaways

• The "20-Year-Old" Analogy: AI in the engineering space right now behaves like a brilliant 20-year-old: it has an incredible memory but zero real-world intent or contextual experience. It requires guardrails and continuous guidance.
• Progress Over Perfection: Waiting for 100% perfect data before implementing AI leads to "analysis paralysis". Industry frontrunners are building out specific use cases, failing fast, and correcting workflows on the fly.
• Use-Case Driven ROI: High-impact applications must focus on process shrinkage. If an AI project cannot clearly map to saving manual time, it likely won't survive past the proof-of-concept phase.
• Monolithic vs. Best of Breed: While legacy PLM vendors continue pushing all-in-one monolithic architectures, end-users are loudly demanding interoperability and seamless integrations.

Main Points & Deep Dives

• Real-World Case Studies:  
  • Cummins: Implemented an AI-driven search capability across legacy engineering documentation to radically accelerate internal data retrieval.
  • Eaton: Used intelligent automation to shrink a complex RFP response process for custom turbochargers from four experts and six months down to one person and one week—without removing engineering review from the loop.
  • MIT Lincoln Lab: Reimagined their historical paper-based processes into a digital format, emphasizing that half of their engineering center resources go strictly toward driving user adoption, not just building the tech.
• The Interoperability Battle: A review of the exhibition floor, tracking how traditional PLM providers (Aras, Contact PLM, Dassault, PTC) match up against modern digital thread and integration specialists.
• The Governance Hurdle: How to safely address data exposure, restrict private customer data, and define explicit system access when introducing AI into enterprise architectures.

Guests & Resources Mentioned

●      Guest: Andrew Halley, Global Partnership and Alliances Leader, Razorleaf

●      Event Host:CIMdata (PLM/PDT Roadmap North America 2026)

●      Speakers Highlighted: Dr. Martin Eigner, Denise Fitzgerald (MIT Lincoln Lab), Diego Tamburini (CIMdata), Vishwajeet Uddanwadiker (Boeing)

Clean data in, clean insights out! If this episode got you thinking about cleaning up your own digital house, we want to hear from you.

📩 Connect with us: Drop a comment or reach out directly at [email protected]. 

⭐ Support the show: If you found value in this deep dive, please give us a review or follow the podcast on your favorite platform.

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© 2026 Razorleaf Corp. All Rights Reserved.

Master Data Management Explained: Why Clean Data Matters More Than Ever

Data fuels every digital initiative — but who’s actually managing it?

In this episode of Stay Sharp in Digital Engineering, Jonathan Scott and Juliann Grant sit down with Razorleaf’s Ann Garcia to unpack the fundamentals of Master Data Management (MDM) and why it plays a critical role in modern digital engineering environments.

With more than 30 years of experience across medical device, consumer packaged goods, and manufacturing industries, Ann brings a practical perspective to one of the most misunderstood topics in digital transformation: data ownership, governance, and lifecycle management.

The conversation explores what master data actually is, how it differs from transactional and reference data, and why organizations struggle to define and manage it consistently across systems like PLM, ERP, and manufacturing platforms.

The team also dives into:

• The difference between master data, transactional data, and reference data 
• Why data governance often becomes more complex as organizations grow 
• The role of master data during PLM and ERP implementations 
• How mergers, acquisitions, and global operations impact data quality 
• Why defining data standards early matters for digital transformation 
• The relationship between clean master data, digital thread initiatives, and AI readiness 
• How organizations balance speed with data accuracy during system rollouts 

Ann also shares real-world examples of how poor data definitions and inconsistent ownership can create downstream challenges — and why many companies underestimate the effort required to maintain clean, trusted data across the enterprise.

As AI and analytics become increasingly important in manufacturing and engineering environments, this episode highlights a foundational truth: clean insights require clean data.

If your organization is investing in PLM, ERP, digital thread, AI, or enterprise modernization, this conversation offers an important look at the often-overlooked infrastructure behind successful digital transformation.

Featured Guest

Ann Garcia – Data management and governance professional with 30+ years of experience supporting manufacturing, medical device, CPG, and enterprise transformation initiatives.

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© 2026 Razorleaf Corp. All Rights Reserved.

What’s driving the future of manufacturing execution, MRO, and digital operations in aerospace and defense? 

In this special on-site episode of Stay Sharp in Digital Engineering, the Razorleaf team recaps their experience at the iBase-t Excelerate 2026 conference in National Harbor.

Jonathan Scott and Juliann Grant share insights from customer presentations, emerging AI strategies, MES modernization efforts, and the growing push toward model-based manufacturing. From practical upgrade strategies to global deployment challenges, this episode explores how manufacturers are balancing innovation with operational realities.

The event highlighted a clear industry shift: manufacturers are under increasing pressure to modernize quickly while maintaining quality, compliance, and production continuity. Discussions throughout the conference focused on pragmatic digital transformation, data integration across the “golden triangle” of ERP, PLM, and MES, and how AI can be securely and effectively embedded into enterprise systems.

The team also discusses:

• GE Aerospace’s large-scale Solumina deployment for MRO and sustainment 
• iBase-t’s practical AI strategy and security-first approach 
• Real-world MES upgrade lessons from Honeywell and Razorleaf 
• Global rollout strategies across multiple manufacturing sites 
• Model-based work instructions and AI-powered data ingestion 
• Integration architecture challenges and the role of connected data 
• Why manufacturers are moving faster than ever to eliminate paper processes 

Beyond the technology, the episode captures the collaborative spirit of the iBase-t community — where even competitors openly share lessons learned to improve manufacturing outcomes across the industry.

If you’re involved in MES, manufacturing operations, aerospace and defense, or digital transformation initiatives, this episode offers a grounded look at where the industry is heading next.

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Stay connected with Stay Sharp in Digital Engineering for conversations focused on digital transformation, manufacturing technology, PLM, MES, AI, and the future of product innovation. 

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© 2026 Razorleaf Corp. All Rights Reserved.