Lifecycle Insights CEO and Chief Analyst Chad Jackson sits down with Joseph Laurine, PhD, AI Governance Lead at a major healthcare organization and former head of AI Governance and Assurance for the National Geospatial-Intelligence Agency (NGA). Joseph's career spans cryptologic intelligence, applied statistics, data science, and executive coaching — an unconventional path that's made him one of the more distinctive voices on responsible AI adoption.

The conversation covers a lot of ground, starting with what it actually means to be a change agent in highly technical environments. Joseph argues that trust and usability are equally critical to adoption — and that framing AI governance as an obstacle rather than an enabler is one of the biggest mistakes organizations make. He also speaks candidly about how he deliberately developed his people skills, including earning an executive coaching certification, to bridge the gap between technical depth and organizational influence.

On data, Joseph is direct: "garbage in, garbage out" is a phrase everyone uses but few executives truly understand. He makes the case that data quality is the single most important foundation for effective AI — and that skipping that work in the rush to be first to market is a bet most organizations will regret.

He also offers a memorable framework for thinking about where AI models stand today: LLMs are the social butterfly — great at conversation, not your go-to for engineering or biology. Small language models (SLMs) represent the journeyman level the industry is moving toward, and Joseph predicts that shift will be visible within the next 18 months.

Topics covered:

• What it means to be a change agent — and why trust and usability matter as much as technology
• Developing soft skills as a deeply technical person
• Why data governance is the true foundation of any AI initiative
• The limits of LLMs — and why domain-specific SLMs are the next frontier
• Digital twins as a safer environment for evaluating AI behavior
• Why being second to market with clean data may beat being first with a shaky foundation
• AI governance in healthcare vs. the intelligence community
• The role of IO psychologists and psychometricians in AI development

Are AI virtual companions the future of engineering work? Chad Jackson shares his takeaways from 3D Experience World, where Dassault Systèmes unveiled three AI assistants—each with distinct personalities tuned to different work modes. But what happens when exploration, execution, and expertise each demand a different interaction style?

The conversation then takes a turn into new research revealing a troubling gap in MBSE initiatives: two-thirds of organizations pursue model-based systems engineering for cross-functional collaboration, yet only 24% report achieving it. Chad and Josh unpack why expectations have shifted—and why delivering on the core promise of coordination remains elusive for most teams. What’s causing the friction? The answers point to challenges that extend beyond MBSE to nearly every engineering transformation initiative.

Plus, a preview of an upcoming conversation on merging Agile prototyping with systems engineering, and why putting imperfect work on the conference room table might be the breakthrough struggling teams need.

• Why the “forest over trees” perspective matters in aerospace design and manufacturing
• How condition-based maintenance using structural health monitoring could revolutionize aircraft operations and reduce costly downtime
• The trade-offs between investing in current versus future technology through real-world disaster relief planning tools
• Overcoming resistance to systems engineering adoption in established organizations
• The critical need for multidisciplinary thinking in engineering education and practice
• Balancing traditional mentorship with openness to unconventional approaches

• The Unpaid Labor of Change: Why change agents are essential but rarely have the official title or time allocated.
• The Translator Role: How successful leaders bridge the gap between C-suite vision and engineering execution.
• MBSE ROI: A case study on how live model reviews led to a “zero-action” PDR and saved weeks of back-and-forth.
• The $68 Million Mistake: What happens when programs skip systems engineering in Phase 1.
• Over-Modeling Pitfalls: Why you shouldn’t model COTS parts down to the resistor—or try to model two humans talking in a bunker.
• AI & MBSE: The future of using AI to generate model templates and accelerate development.

• Choosing the leadership track: Why caring about people is the tell for whether management is a fit.
• What a “change agent” is (and why it usually becomes an extra job on top of engineering work).
• Three essentials for successful change: a crisp “why,” realistic capacity, and trust (including how to reduce fear by framing change as upskilling).
• Programs vs. change: Why big commitments make tradeoffs unavoidable—and why the decision is almost never “clear.”
• Systems engineering reality check: SE provides value, but outdated document-heavy processes can turn it into a burden in a fast-change world.
• MBSE and modeling depth: Why “model everything” breaks down, how to think about rigor vs. agility, and why interfaces are the anchor.
• “SysML 2.0 / ‘CS ML 2.0’” implications: How stronger standardization can make models more computer-friendly for analytics and AI-era workflows.

In this episode, we sit down with McKray Jones, a Technical Program Manager and Deputy Director of Engineering, to explore the evolving role of the “Change Agent” in modern engineering organizations. McKray shares his unique journey from the Marine Corps to leading complex hardware integration teams, offering a fresh perspective on why the engineering industry is facing a leadership vacuum and how to fill it.

We dive deep into the challenges of transitioning from traditional waterfall models to accelerated, MVP-driven hardware development. McKray explains how to bridge the gap between software agility and hardware reality, the critical role of empathy in technical leadership, and when organizations should make the leap to Model-Based Systems Engineering (MBSE).

Whether you are an engineering leader, a systems architect, or an aspiring change agent, this conversation offers actionable insights on building trust, managing complexity, and keeping the business viable while driving innovation.

• Defining the Change Agent: Moving teams from long development cycles to revenue-generating MVPs.
• The Human Side of Engineering: Why empathy, listening, and understanding business flows are critical skills for technical leaders.
• Hardware vs. Software: Adapting Agile and “user story” concepts to physical hardware systems with long lead times.
• MBSE Strategy: When to switch from document-based engineering (Excel/SharePoint) to models (Cameo/Capella) and how it aids in certification.
• The Modern Engineer: How the profile of a successful engineer has shifted to demand higher EQ and communication skills.

• AI-Native Workflows: We deconstruct the concept of “AI Scaffolding”—a method to provide engineering models with a “memory” by using document-based inputs to maintain constraints and context.
• Reframing Resistance: Chad introduces the “Judo Throw” of resistance, explaining how savvy change agents use pushback to build investment and uncover project-threatening flaws early.
• The Systems Engineering Struggle: Why the #2 challenge for MBSE is the learning curve, and how AI-powered “vibe coding” might help simplify SysML modeling.
• Research Findings: Insights from our recent study and what we’re learning from the 300+ respondents.

• Follow Chad
• Follow Lifecycle Insights
• Benedict Smith's post on AI-native workflows.

• Lifecycle Insights | InsightEX

• Defining the Change Agent: What it takes to be a revolutionary leader in engineering organizations.
• The Origin Story: Guy’s path from FPGA design in Israel to aviation and autonomous vehicles in North America.
• Embracing Failure: Why hiding mistakes kills innovation and how transparency builds safer, smarter teams.
• Systems Engineering Strategy: A look at Document-Based approaches vs. Model-Based Systems Engineering (MBSE).
• Design to Test: How defining test procedures before development begins saves time and money.
• The V-Model: Mastering the integration and verification phases (the “Right Side of the V”).