From CAD chaos to clean engineering context.

What happens when two founders attack two of hardware engineering’s most stubborn bottlenecks: manufacturing documentation and requirements management?

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Chris Barton, Co-Founder and CTO of Drafter, and Janis Vavere of Trace.Space about what AI is actually changing in engineering right now, and what still demands deterministic precision, human review, and trust. They get into why legacy tools are failing modern teams, where AI is already delivering real value, why requirements are not going away, and how the first true “OpenAI moment” for engineering may be much closer than most people think. 

Timings

00:00 Introduction to Chris Barton and Janis Vavere

00:29 Chris Barton on Drafter and the manufacturing documentation problem

01:04 Janis Vavere on Trace.Space, requirements engineering, and why the category stopped innovating

02:50 The 2022 OpenAI moment: bullishness, skepticism, and early experiments

05:38 Why engineering AI was not good enough three years ago

05:40 Chris on precision, determinism, and why hallucinations are unacceptable in engineering drawings

07:03 How AI changed software development inside Drafter

09:02 How Trace.Space engineers went from skepticism to heavy AI usage

11:10 Where AI is visible inside the product versus buried in the stack

13:53 Deterministic outputs, human review, and reliable AI for engineering

15:54 Whether startups like Drafter and Trace.Space should build their own models

18:18 How these startups coexist with and challenge Siemens, PTC, and Dassault

19:14 Chris on the “gray area” between CAD and manufacturing where work still runs on PDFs, email, and spreadsheets

20:32 Drafter’s 2D strategy and why 2D drawings still dominate the physical world

22:14 Janis on agentic iteration between requirements, drawings, and simulation

23:17 Why legacy requirements tools are frustrating modern engineering teams

25:47 Are the founders more bullish now than they were four years ago?

28:07 Will AI collapse roles across design, manufacturing, and simulation?

28:54 Do requirements still matter in a world of trade studies and optimization?

30:16 Chris on first-principles engineering, design intent, and why requirements do not disappear

31:21 Infinite design space exploration and what AI is unlocking

32:39 Advice for younger engineers: where to work and how to stay relevant

35:45 Audience Q&A: does clear design intent require explicit functional specification?

36:43 Have we had the OpenAI moment for engineering yet?

38:49 Why AI-native tools are exposing just how far behind legacy engineering software is

41:09 Customer digital maturity: from Excel-and-email workflows to agent-first engineering

44:22 Does adopting one AI-native tool trigger broader digital transformation?

46:12 The customer epiphany moment after using Trace.Space or Drafter

46:47 Where to meet Janis and Chris at upcoming events

48:00 Closing remarks and AWS sponsorship mention

Configuration Management is still one of the hardest problems in PLM—and this panel doesn’t sugarcoat it.

In this episode of the Future of PLM Podcast, Michael Finocchiaro is joined by Rob Ferrone, Brion Carroll, Jim Brown, Oleg Shilovitsky, and Eric Schrader (Propel) to break down why BOMs still don’t match, why “single source of truth” is mostly fiction, and where AI might actually help.

Key themes:

• Why CM ≠ BOM management
• The myth of a single version of truth
• Variant chaos and effectivity complexity
• Why most companies still fail at adoption
• AI, product memory, and the future of CM

If you work in PLM, engineering, manufacturing, or digital thread—this is a must-watch.

👇 Drop your thoughts in the comments:
Where is configuration management breaking down in your org?

⏱️ TIMELINE

00:00 – Intro + panel lineup
00:09 – What is configuration management (5 definitions)

03:12 – Biggest false beliefs about CM

• “We have a single source of truth” (we don’t)
• CM seen as bureaucracy vs performance lever
• Methodology ≠ success (adoption is the issue)

06:47 – Minimum data model for CM

• Identity, effectivity, baseline, traceability
• Why data governance matters more than tools

10:22 – Where CM actually lives (PLM, ERP, MES, everywhere)

• The “octopus problem” across systems

15:12 – Hardest real-world CM problems

• Variant management = BOM chaos
• Effectivity vs configuration confusion
• Software + firmware breaking traditional models

21:53 – Debate: Effectivity (date vs serial vs lot)

• Why “it depends” is unavoidable
• Safety vs cost trade-offs

24:09 – Configuration rules debate

• 150% BOM vs model-based approaches
• Why rules drift over time

26:10 – Digital thread reality check

• Why duplication is inevitable
• Importance of product identity

30:09 – As-designed vs as-built vs as-maintained

• Where control breaks down (hint: service)
• Why “as maintained” is the weakest link

39:38 – AI in configuration management

• Change impact analysis
• Data structure vs AI hype
• “AI is useless without governed data”

48:55 – When is the ChatGPT moment for PLM?

• Simplicity vs complexity
• People problem vs technology problem
• Product-as-agent concept

59:10 – Final thoughts

• Data governance as the core issue
• Why we’re still having the same debates after 20 years

🎯 Key Takeaways

• There is no single source of truth—only closest approximations
• Variant + effectivity = core chaos engine
• CM failure is mostly organizational, not technical
• AI will help—but only if data is structured and governed
• The real frontier: making CM consumable across the enterprise

📢 Follow / Connect

• LinkedIn: Michael Finocchiaro
• More content: DemystifyingPLM.com
• Events: Threaded! Conference Series

The conversation explores the impact of AI, particularly LLMs, on engineering design and development. It delves into the integration of AI into engineering workflows, the empowerment of engineers through AI, and the potential for AI to transform engineering disciplines. Both Neural Concept and nTop platforms are discussed in terms of their AI integration and impact on engineering workflows.

Takeaways

• AI as an accelerator in engineering
• AI empowerment in engineering

Chapters

• 00:00 Introductions and Company Overview
• 06:41 AI as an Accelerator in Engineering
• 13:13 AI Empowerment in Engineering
• 20:44 Integration of Disciplines through AI
• 30:25 Integration of Tools and Platforms
• 37:40 Role of AI in Engineering

The conversation explores the impact of AI in two distinct industries: maritime vessel design and medical imaging. Both Shahroz Khan and Roger Johnston discuss the integration of AI into their respective fields, highlighting the specific challenges and opportunities they have encountered. They also delve into the regulatory framework and the role of AI in the development process. The conversation delves into the application of AI in medical and maritime industries, highlighting the challenges and opportunities for digital transformation. It also explores the impact of AI on job prospects for younger generations and the potential for AI-driven innovation in both sectors.

Takeaways

• Narrow focus on specific markets allows for deep integration of AI into industry-specific challenges
• AI has revolutionized the development process in both maritime vessel design and medical imaging, leading to significant advancements
• Regulatory frameworks play a crucial role in shaping the deployment and use of AI in these industries AI's role in medical and maritime industries
• Challenges and opportunities for digital transformation
• Impact of AI on job prospects for younger generations

Chapters

• 00:00 Intrinsic Context and Specificity in AI Models
• 43:36 Digital Maturity in Healthcare and Maritime Industry
• 51:12 Impact of AI on Customer Transformation

The conversation covers the early adoption of AI in business, the impact of AI on software development, the integration of AI across business functions, the changing landscape of product development, and the role of AI in digital transformation in manufacturing. It also discusses the challenges and opportunities in implementing AI, the importance of grounded AI decisions, and the future of AI in manufacturing.

Takeaways

• Early adoption of AI in business
• Impact of AI on software development

Chapters

• 00:00 Introduction to Maintain X and Machine Metrics
• 05:19 AI Integration Across Business Functions
• 11:30 Advice for Younger Generation in AI Era
• 16:50 User Interaction with AI in Software
• 22:04 Future of AI in Manufacturing
• 27:40 Digital Maturity in Manufacturing
• 33:09 Upcoming Trade Shows and Events

The conversation delves into the introduction and comparison of OPC UA and MQTT, the genesis and impact of MQTT on industrial automation, the purpose and evolution of OPC, the complementarity of MQTT and OPC UA, the role of MQTT in industrial data publishing, the importance of open standards and security, and the future of AI and adapting to new technology.

Takeaways

• MQTT and OPC UA are complementary protocols
• AI needs data to be effective

Chapters

• 00:00 Introduction to OPC UA and MQTT
• 05:47 MQTT and OPC UA Complementarity
• 11:11 The Role of MQTT in Industrial Data Publishing
• 16:11 The Importance of Open Standards and Security
• 32:35 Adapting to New Technology and the Future of AI

The conversation covers the impact of AI on code development and digital maturity in manufacturing. It delves into the skepticism and adoption of AI, real-time intelligence layer in manufacturing, AI's role in Node-RED flows, its impact on software development, development processes, agile development, continuous deployment, the OpenAI moment in manufacturing, challenges with current protocols, safety and auditing in manufacturing, digital maturity and adoption of FlowFuse, automation and robotics in manufacturing, and risk and uniqueness in startups.

Takeaways

• AI's Impact on Code Development
• Digital Maturity in Manufacturing

Chapters

• 00:00 Introduction to Prove It Event
• 06:01 Real-time Intelligence Layer in Manufacturing
• 11:01 AI and Agile Development
• 17:25 Challenges with Current Protocols in Manufacturing
• 22:28 Automation and Robotics in Manufacturing

The conversation delves into the initial skepticism of AI and its eventual impact, the integration of AI in development, the touch points of AI in Inductive Automation, the challenges and limitations of LLMs, and the digital maturity and customer adoption of AI in the industry.

Takeaways

• Initial skepticism of AI gave way to its impactful integration in development.
• AI touch points in Inductive Automation and the challenges of LLMs highlight the evolving landscape of AI in the industry.

Chapters

• 00:00 Initial Skepticism and AI Impact
• 06:22 AI Touch Points in Inductive Automation
• 13:31 Digital Maturity and Customer Adoption

The conversation covers topics related to industry 4.0 innovation, product development and acceleration, as well as the adoption of AI technology. It also delves into the challenges and collaboration in the industry, along with impressive presentations at ICC and the importance of innovative technology and open standards.

Takeaways

• Industry 4.0 innovation
• Product development and acceleration
• AI technology adoption

Chapters

• 00:00 Challenges and Collaboration in the Industry

The conversation delves into the impact of AI on manufacturing, addressing initial skepticism, continuous transformation, and the progression of digital maturity in the industry. It also explores the role of AI in product development, code development, management, decision-making, and creativity. The discussion highlights the integration of AI in Litmus, its impact on manufacturing, and the need for continuous transformation in the industry.

Takeaways

• AI Adoption in Manufacturing
• Continuous Transformation

Chapters

• 00:00 The Power of Live Demonstrations
• 07:35 AI's Role in Code Development
• 13:14 Addressing AI Anxiety
• 18:53 AI Integration in Litmus
• 25:27 AI's Role in Democratizing Technology
• 31:36 Continuous Transformation in Manufacturing
• 37:05 Progression of Digital Maturity