Practical AI thinking for executive teams.

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Ten years ago, AI was not a part of life.

Three years ago, it was barely a consumer product.

Today, one in ten people use ChatGPT regularly.

Why AI Feels Brilliant One Moment and Useless the Next

Understanding the Jagged Edge of AI

If you have used generative AI for any length of time, you have probably experienced this contradiction:

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AI has sparked a lot of excitement—and confusion—in the world of software development. Headlines often focus on “vibe coding” or the idea that AI can magically replace developers. But the reality is far more interesting.

In this episode of The Conversation with Model Mind AI, Loren Horsager talks with developer and consultant Mark Caufman about what AI really changes in development work. The biggest shift isn’t that developers write less code—it’s that they think more intentionally about how software is built.

AI makes it easier to generate code, but it also raises the bar for quality. With AI assisting in documentation, testing, and refactoring, the old excuses for skipping those steps disappear. Good process becomes the accelerator, not the bottleneck.

Mark explains how structured approaches—like writing specs, clarifying requirements, and validating plans before implementation—dramatically improve outcomes. Instead of reacting to AI-generated code, developers become orchestrators, guiding systems with clear intent and constraints.

The conversation also highlights how AI reshapes solo development. Independent developers can now deliver enterprise-level rigor—tests, documentation, consistency—without massive teams. AI acts as a tireless collaborator that reinforces discipline rather than undermining it.

Finally, Loren and Mark explore what this shift means for the future of development careers. As AI handles more of the mechanical work, human skills like problem framing, system design, and critical thinking become even more valuable.

AI isn’t making developers obsolete. It’s making thoughtful development more powerful—and more human than ever.

I knew immediately that I was forever changed and that it was unlikely a day would go by without thinking about him, talking to him, or asking him for help.

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As 2025 comes to a close, many organizations are realizing that AI success wasn’t driven by flashy tools or massive transformations—it was driven by people.

In this episode of The Conversation with Model Mind AI, Angela Schultz and Loren Horsager reflect on what actually worked in AI adoption over the past year. As tools like Microsoft Copilot improved and became more accessible, AI stopped being theoretical. Employees began experimenting, asking better questions, and finding small wins that created real momentum.

One of the biggest pitfalls discussed is the temptation to assign AI to a single expert. While appealing on the surface, this approach often limits adoption. Instead, the most successful organizations focused on raising AI literacy across teams—empowering people closest to the work to identify opportunities and drive change.

The conversation also reinforces why AI should not be treated like a traditional IT project. Large rollouts often failed, while fast prototypes and short experiments helped teams assess value quickly and avoid costly mistakes.

Through real client examples, Angela and Loren show how practical, tailored AI use cases delivered meaningful results—and why this people-first mindset sets the stage for 2026.

From Idea to Impact: How One Leader Turned AI Into a Sales Coach

What started as a simple experiment became a blueprint for scalable AI adoption.

In this episode of AI at Work, Angela Schultz speaks with Nicole McMurray, Divisional Vice President at AppleOne, about how a nine-hour AI course sparked the creation of an AI-powered sales coaching assistant. Designed to reflect Nicole’s leadership voice and company values, the tool gives new hires consistent guidance, confidence, and support — without slowing the business down.

The conversation highlights a powerful truth: AI adoption doesn’t require technical backgrounds. It requires curiosity, education, and leaders willing to listen to their teams.

Full episode now available on Spotify and YouTube.

Mini-Series: AI at Work

Angela Schultz with Special Guests

“Upskilling in the Age of AI — A Human Approach to Technology”

In this week’s episode of the AI at Work mini-series, Angela is joined by Megan Starkey, strategic advisor, founder of RBD, and author of The Intelligent Organization. Their conversation dives deep into the people side of AI, the structural side of AI, and why companies struggle to unlock value even when the technology is ready.

Megan explains the difference between AI adoption (individual skills and productivity) and true organizational transformation (workflows, processes, decision rights, and culture). She also breaks down why adaptability—not technical expertise—is now the defining skill for modern leaders.

If your team is feeling the pressure to “catch up,” this conversation will help reset expectations, clarify priorities, and highlight what actually moves companies forward.

Full episode now available on Spotify and YouTube.

Mini-Series: AI at Work

Angela Schultz with Special Guests

“Upskilling in the Age of AI — A Human Approach to Technology”

In this episode of The Conversation with Model Mind AI, host Angela Schultz welcomes Dr. Elizabeth M. Adams to discuss how responsible AI, education, and human connection shape the future of work. Dr. Adams, Chief Engagement Officer of the Minnesota Responsible AI Institute, shares insights on making AI accessible to all—from seniors writing family stories to students exploring AI ethics.

Key Takeaways:
- Upskilling starts with curiosity and care—not just code.
- Responsible AI means understanding impact before innovation.
- Leadership in AI requires listening to employees and empowering them to contribute.
- The future of AI should center on joy, equity, and human thriving.

Episode 13 Recap: “AI Strategy Is a Marathon—Not a Sprint”

In Episode 13 of The Conversation with Model Mind AI, Angela Schultz and Loren Horsager draw parallels between building an AI strategy and running a marathon. It’s about pacing, endurance, and the discipline to learn while moving forward. From avoiding “wait-and-see” paralysis to strengthening cross-department collaboration, this episode breaks down how organizations can build sustainable AI momentum.