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