Want to know what AI adoption really looks like inside a company?

Ask the leaders in the middle.

They are close enough to strategy to understand the intent and close enough to the front lines to hear the real questions. They know where teams feel excited, where they feel overwhelmed, where the process breaks, and where AI creates actual value.

I sat down with four 2026 Emerging Leaders who were handpicked by their as leaders to watch:

• Lane Koetter, Client Advisor, The Jacobson Group

• Kieva Kasek, Head of U.S. Operations, Simply Business

• Enoch Sutherland, Director of Digital Workforce Enablement, CNA Insurance

• Tian Xue, Senior Director of Data Science and Product Data Lead, SageSure

These are the people helping AI move from strategy to adoption. They are fielding the detailed questions, the fears, and the concerns directly. Their advice is especially valuable for peers who want to lead from where they sit.

A few themes stood out.

Leaders model the behavior that creates clarity and drives adoption. When leaders use the tools, show the work, invest in training, and create space for hands-on learning, teams gain confidence.

AI creates an incredible opportunity to move closer to the customer. Everyone says they want more customer-facing time. AI can help make that possible by reducing repetitive work and freeing people to focus on relationships, judgment, service, and business problems.

Transparency around ethics and measurement has to be built in from the beginning. Teams need to define the role AI plays, the role people play, the outcomes they expect, and how they will measure success before pilots scale.

What I loved most is that these emerging leaders are not waiting for AI strategy to come from the top. They are already shaping how adoption happens inside teams.

This episode marks the official end of Lead The Machine Season 1. Season 2 podcast episodes will be back in the fall. In the meantime, I’ll be writing and reflecting on what I learned this season. Thanks for listening.

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— Kirstin

AI is changing the work. Talent strategy will determine who benefits from it.

That is the real issue beneath so many conversations about AI at work. Tools can remove repetitive tasks, speed up analysis, and change how people make decisions. Leaders still need to rethink roles, skills, culture, and career development so AI creates more capability instead of more pressure.

This week on Lead The Machine, I’m sharing a special dual episode with Judy Busby and Noelle Codispoti, recorded at the Emerging Leaders Conference.

Judy is Senior Vice President of Executive Search and Corporate Strategy at The Jacobson Group. She brings the executive search and leadership lens to the conversation. We talk about why insurance remains such a meaningful career path, why the Emerging Leaders Conference matters for retention, and why every professional needs to think like the CEO of their own career.

Noelle is Head of the Academy and Director of Emerging Talent Programs at The Risk & Insurance Education Alliance. She brings the education and emerging talent lens. We talk about AI literacy requirements, how students are learning to use AI, and why the next generation needs to understand the tool while still owning the decision.

Together, these conversations land on a practical point: the human side of AI is talent strategy.

AI will change tasks. It will change job scopes. It will change how early-career professionals learn the business. Leaders need to develop people with more intention.

Judy talks about the skills that matter when task work shifts: emotional intelligence, critical thinking, empathy, and the ability to bring people together. Noelle talks about strategic thinking, analysis, culture, employee voice, and the importance of improving processes before layering AI on top.

The future of work will reward people who use AI well and still lead with judgment, accountability, and humanity.

Thanks for listening.

— Kirstin

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For those of us who have spent years saying “it’s all about the data,” it lands with a thud. The “told you so” moment feels tempting. It also does not help. Data readiness is a problem across every industry, and insurance carries extra complexity because of legacy, regulatory oversight, and the need to defend decisions that affect consumers.

Why there’s a gap between pilots and board-ready outcomes should be interrogated, and this episode delivers insight. You’ll gain a pragmatic view at how both organizational and industry dynamics significantly impact success or failure.

Two industry veterans offer a front-line view: Jeff Rieder, Partner and Head of Benchmarking at Aon, and Stefan Holzberger, Executive Vice President and Chief Operating Officer at AM Best.

Jeff brings a benchmarking lens on how insurers evolve through tech cycles, where job families shift, and why executive alignment and measurement determine whether adoption sticks.

Stefan brings AM Best’s lens on innovation, stability, and risk. He shares where insurers deploy AI first, why claims and back-office workflows move faster, and why underwriting adoption demands governance discipline and regulatory awareness.

Leadership, culture, and talent development emerge as the common thread. AI does not move through an organization on its own. Companies need leaders who set direction, teams who build foundations, and talent strategies that expand skills instead of amplifying anxiety. It is no surprise that ROI confidence remains low when organizations still struggle to connect data readiness, governance, and adoption behavior to measurable outcomes.

Three takeaways from the episode:

1. Treat data readiness as an operating priority, not a side project.

2. Define board-ready success measures early, then manage to them with leadership alignment.

3. Build governance and talent development in parallel so adoption scales without breaking trust.

Thank you for listening.

- Kirstin

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The rubber meets the road when we need to prove ROI with any new technology. It’s inevitable and it’s hard. Rob Cressy is an AI enablement coach who works with leaders and teams that want measurable performance from AI. I like that he brings a human-first approach, and anchors AI adoption in identity, vision, and values. Then turns that into execution through systems.

AI results come from systems, not dabbling

Rob and I talk about a pattern I see across industries. Teams treat AI like a search box. They ask a question, accept the first output, and stop. When the output disappoints, they blame the tool, the data, or the moment. Rob calls this a foundation problem. He says: “the first prompt is the start, not the end.”

He also gives a clear reason executives should care. AI adoption creates performance spread inside the same team. Rob shares an example where one person doubles output using AI. That person creates a gap that compounds when peers stay on old workflows. Leaders feel the impact in slower delivery, lower win rates, and more friction across the organization.

We also talk about what leaders can do right now. Rob recommends a simple discipline. Teams should list daily friction, choose one workflow to improve, then ship a small win. He pushes leaders to build a roadmap from the work people already dislike, and he reinforces a systems lens. Systems scale, and clarity scales.

Rob offers one prompt I expect to reuse: “What is hidden and non-obvious?” He uses AI to surface blind spots, simplify systems, and reduce unnecessary complexity. He treats AI as a tool that supports structured execution, not a replacement for leadership.

If you lead a team through AI adoption, this episode will help you build a foundation, improve output, and keep the work human.

— Kirstin

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This week on Lead The Machine, I recorded a special dual interview on site at the Emerging Leaders Conference in Nashville with Wendy Davis Johnson and Marguerite Tortorello. We used the moment to look backward and forward. We traced the early days of the Insurance Careers Movement (ICM), and we connected that work to the talent and skills demands AI creates right now.

I’ve shared before that I helped co-found ICM, now in its 11^th year. It’s one of my proudest accomplishments.

Wendy, author and corporate strategist, took us back to the beginning. She described how she and Brian Duperreault looked for a topic that mattered, researched diversity and leadership visibility, and then found the deeper signal. The industry faced a looming talent gap as experienced professionals retired and fewer young people entered the field. Wendy described how that research led to a simple conclusion. The industry needed a louder voice, and leaders needed to treat talent as a strategic priority.

Marguerite, Executive Director of ICM, described what happened next. ICM grew from humble beginnings into a global collaboration. Today, 22 countries participate in Insurance Careers Month, over 1,000 organizations engage in the initiative, and the Emerging Leaders program has produced more than 1,000 alumni across functions, roles, and career stages. Companies now treat February as a kickoff for year-long talent planning, not a one-off recruiting campaign.

We also talked about AI. Headlines focus on layoffs and replacement. I see a different path for winning. Organizations win when they invest in people, build AI fluency, and connect that fluency to real customer and operational outcomes. Marguerite described how companies already run intentional upskilling efforts, and she highlighted the role of legal, compliance, government affairs, and regulators in responsible adoption. Wendy reinforced the core reality. AI accelerates research and analysis, and leaders still differentiate through human judgment and relationships.

If you care about talent strategy, leadership development, and the human side of AI at work, this episode will give you history, context, and a grounded view of what winning looks like.

— Kirstin

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This week on Lead The Machine, I spoke with Dakota Koontz, Executive Director at Housing Heroes Hub, about what it takes to lead inside broken systems, and how AI can reduce the load for leaders who carry too much.

Now… this may seem counter to my consistent advice to fix broken processes first, rather than slap AI onto inefficient workflows.

This is different.

Dakota works with housing leaders who face rules layered on rules, outdated technology, and constant pressure to stay compliant while serving vulnerable communities. He also names the part leaders rarely say out loud. The job includes heavy emotional labor. Leaders manage tenants, staff, funding risk, and public scrutiny, often at the same time.

These leaders aren’t in a position to fix government systems and regulations. They must adapt, ensure compliance and advocate for their constituents - while being overworked.

We focus on that business problem and talk about the technology that can help.

Dakota shares concrete ways teams use AI:

• Convert long intake packets into digital forms.

• Reduce back-and-forth with cleaner inputs.

• Prepare for board, funder, and stakeholder conversations with mock dialogues.

• Improve prompting with a standard operating procedure (SOP) mindset so output stays consistent.

He also makes AI adoption accessible. He does not come from a traditional tech path. He invested the time, built skill through repetition, and taught thousands of people how to do the same.

This episode focuses on clarity, dignity, and workload relief. It also shows how leaders can use AI as a tool while keeping the work human.

Thanks for listening.

Kirstin

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Bill Walsh, the CEO of Mediafly, has played a meaningful role in my leadership journey. I met him more than a decade ago when he served on the board at Valen Analytics, and he has been a steady mentor for me since then.

Bill brings 30 years of executive leadership across enterprise software, analytics, logistics, and AI-driven solutions. He has led organizations through growth, product innovation, M&A, and cultural change. He has also served on public and private company boards, taught as an adjunct professor, and coached senior leaders.

So what does “Board-Ready AI: Leadership, Data, and Risk” mean?

Bill shares an adoption approach leaders can apply immediately: crawl, walk, run. Leaders learn the basics, use AI personally, and create a safe internal environment where teams can practice. Teams build confidence internally, and leaders use what they learn to shape customer-facing AI with clearer requirements and better discipline.

Bill also makes the data requirement concrete. AI models depend on the quality of the underlying data. Leaders need accurate, consistent, complete, timely, and unified data to produce reliable outputs. Bill does not ask teams to make data perfect. But they do need to reduce errors, remove duplicates, clarify definitions, and connect silos in the places that matter most.

He offers a practical lens for where to start: high value and low friction. He shares examples such as sales content personalization, predictive forecasting, churn analysis, recommendation engines, and knowledge and search. These use cases often deliver value without requiring a full rebuild of every system.

We also talk about the board conversation. Boards prioritize risk and governance. Management teams prioritize innovation and speed. Bill advises leaders to bring transparency, explainability, and trusted expert input into board discussions so the company can move with discipline and direction.

If you lead a team through AI adoption, or you support leaders who do, this episode gives you a clear framework you can use.

Thanks as always for tuning in.

Kirstin

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I love intergenerational conversations about AI and the future of work, especially with Millennial leaders like John Hyde, a real estate and hospitality project management consultant. AI brings us a rare opportunity to learn from each other, across generations and levels of an organization.

John brings a Millennial perspective to questions many leaders are wrestling with right now: What is leadership supposed to look like when everyone has access to the machine? What happens to work when technology keeps promising more flexibility and efficiency, but people still feel stretched, blurred, and exhausted?

John’s right on point. The Wall Street Journal published an article showcasing one of the biggest studies of AI’s effect on work. The ActivTrak study included 164K workers and 443 million work hours from 1,111 employers. The bottom line:

• Rather than easing workloads, AI is intensifying activity across the board.

• Focused work dropped 9%.

• We’re getting caught up in the AI prompts of “Do you want to now consider this or that” at the end of most AI output.

One of the ideas that stayed with me from this conversation is that leadership can no longer be about gatekeeping. For a long time, leaders often held power because they controlled access to information, tools, relationships, or decision-making.

But in the age of AI, that model is eroding quickly. If everyone has access to powerful tools, then the real question becomes: what are you bringing that is unique and additive?

John makes the case for people enablement. The leaders who matter most going forward will be the ones who help others grow, develop judgment, build confidence, and become more capable in how they use technology.

A few of the themes we explored:
🤝 what technology cannot replace in human work
📈 why younger generations are rethinking identity, work, and success
🛠️ how leaders can help people leave stronger than when they arrived

At the heart of the episode is a simple but important idea: AI should make us more human, not less. As a deeply religious person, this reflects John’s interest in keeping humanity at the center.

This conversation will resonate with anyone trying to lead well, adapt thoughtfully, and think clearly about what work should become from here.

Thanks for reading, listening, and being part of the conversation.

Kirstin Marr

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What happens when recruiting becomes AI talking to AI?

That is the question at the center of my newest Lead the Machine episode with Jessica Peskin, CEO of Global Recruiters of Denver.

It is one of the most practical and timely conversations I’ve had on the show because it tackles a growing problem hiding in plain sight: companies are adding more AI to hiring, but that does not automatically mean they are getting better at identifying talent.

In many cases, the opposite is happening.

Candidates are using AI to write resumes. Employers are using AI to write job descriptions. Then automated systems are evaluating both sides and deciding who gets seen. The process may look efficient, but it often strips out the very thing that matters most in hiring: human judgment.

Jessica brings a rare lens to this issue. She is a recruiter and two-time founder and experienced operator who has worked across real estate, insurtech, and insurance growth organizations. She has seen firsthand how businesses scale, how leaders hire, and how often the best people do not fit neatly into a keyword match.

One of the most compelling ideas in this episode is the importance of the gray area. That is the space where transferable skills, resilience, judgment, and growth potential live. It is also the space many systems are designed to filter out.

We talk about:
🧠 How companies are over-automating one of the most human decisions they make
📄 How candidates flatten their own story into polished but generic resumes
🚫 The story of a hiring leader who applied for his own open role and was rejected by the system
🎙️ Why leaders need to become better interviewers, not just better users of hiring tools

Jessica also shares one of the most surprising pieces of advice from the episode: tell your story honestly, including failure. In a hiring market filled with sameness, that kind of authenticity can be the signal that sets someone apart.

The bigger message is one I come back to often: AI can help organize, streamline, and scale. But it should not replace discernment and human connection where it matters most.

If you are building a team, looking for your next role, or thinking about the future of work, this conversation will resonate.

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Thanks for reading and listening.

Kirstin Marr

This week on Lead The Machine, I’m truly honored to share my conversation with Brian Duperreault — former CEO & Chairman of AIG, and a leader who has shaped modern insurance through CEO roles at Marsh & McLennan, ACE (now Chubb), and Hamilton. He’s currently Executive Chairman of Cedar Trace.

Brian and I begin where real transformation always begins: people. We talk about why talent remains a true C-suite priority, and why it becomes even more urgent in the age of AI. This is personal for me: Brian and I worked together through the Insurance Careers Movement (ICM), a CEO-led collaboration created to attract and develop the next generation of insurance professionals, including the Emerging Leaders Conference.

I helped co-found ICM, now in its 11^th year, which is led by the American Casualty Property Insurance Association, The Jacobson Group and AM Best. Over 1,000 companies from 20 countries participate each February for Insurance Careers Month.

Be optimistic and pragmatic

From there, we turn to AI as the next major technology shift leaders must navigate. Brian offers a grounded perspective: big tech transitions are often slower than we expect, deliver less than we assume early on, and cost more than planned. But they are still inevitable.

“I’m an optimist,” Brian shares. The leaders who win are the ones who engage early, shape adoption responsibly, and keep their organizations relevant as the competitive bar rises.

We also discuss how AI shows up in practical ways: augmentation over replacement, using technology to reduce drudgery, improve decision quality, and free people to focus on judgment and relationships — the parts of the work that actually differentiate.

Finally, Brian shares a simple message for professionals coming up the ranks: do your job well, stay curious, raise your hand for the hard work, and become the person leaders can count on during change. In a world where tools evolve quickly, those habits are what keep you relevant.

It’s not often we get to hear from a business titan in an informal setting. Brian’s broad view of AI, risk, and the leadership talent required to navigate AI offers perspective and forward-thinking ideas. His recent biography Faith & Purpose: The Life & Vision of Insurance Icon Brian Duperreault, written by Wendy Davis Johnson (also an ICM co-founder) is well worth reading.

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Thanks for engaging with Lead The Machine.

— Kirstin

AI-native products are forcing a reset because they’re built on cleaner data, tighter workflows, and clearer value propositions. When AI levels the playing field, weak foundations, technical and organizational, are exposed quickly.

That’s why my recent conversation with Ivan Latanision, Chief Product and Marketing Officer at Allvue, is especially timely.

Ivan has built products in highly regulated, private equity (PE)-backed environments where AI can’t just sound impressive, it has to earn trust, customers, and revenue.

We talk candidly about why so many AI initiatives stall, and it often comes down to something deceptively simple:

We don’t communicate the value of data well.

Teams talk about models and architectures. Boards care about revenue, risk, and returns. But you can’t deliver to those expectations without high quality data. When those two conversations never connect, AI becomes an expensive experiment instead of a business advantage.

The second critically important lesson from our discussion:

New products fail when leaders lose direct contact with customers.

We talk about why real AI ROI requires structured, ongoing customer feedback, and why that feedback can’t stop at the product team. CEOs, CFOs, and boards need to hear directly from customers about what’s working, what isn’t, and where AI actually creates value.

We also dig into:

• How AI must be embedded in real workflows, not bolted on

• Monetization discipline matters as much as model performance (i.e. don’t give away AI for free)

• How faster prototyping leads to new revenue streams

If you’re leading through this moment and trying to move from AI curiosity to AI impact, this episode will resonate. You’ll learn how to reconnect strategy, execution, and customer reality before the market does it for you.

Thanks for listening and being part of the Lead The Machine conversation.

— Kirstin

But Richard challenges a common assumption. This isn’t a food production problem. It’s a logistics, nutrition, and systems problem made worse by supply chains that are siloed and lack resilience. The human cost is devastating, stripping families not only of food, but of dignity.

The World Food Bank opens up markets for small farmers to build economically sustainable farms that feed their families and communities. They bridge the gaps in agricultural trade that allow farmers to become “bankable” in the eyes of financiers and insurers who can help them grow their business.

One of the core beliefs behind Lead The Machine is this: before you reach for technology, you have to understand the business problem. That’s exactly what this week’s episode does.

From there, the conversation turns to what can change.

This is where technology, and AI in particular, becomes a game changer. Used responsibly, AI enables hyper-local insight, faster response to risk, and new ecosystem models that support small farmers instead of isolating them. Richard explains how AI helps create virtuous cycles: healthier soil, lower risk, better access to markets, affordable insurance, and more stable income.

This episode isn’t just about awareness. It’s a call to participate. There is real opportunity for technologists, builders, operators, and investors to apply leading-edge AI in agriculture and help solve one of the most important problems of our time.

🎧 Listen to the full episode here and let me know what stood out to you.

— Kirstin

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This week on Lead The Machine, I had the opportunity to sit down with Colleen Campbell, Founder and CEO of A-Human-I. Colleen is one of the most grounded, practitioner-oriented leaders I’ve spoken with about AI adoption.

Colleen has spent decades leading large-scale transformation efforts across industries and complex enterprises. She’s been in the room where strategy meets reality, and where AI ambition collides with organizational friction, cultural resistance, and unclear accountability.

Why We Stall

Colleen is refreshingly direct about why so many AI initiatives stall. It’s not because the technology doesn’t work. It’s because organizations treat AI like a software deployment instead of what it really is: an enterprise change that reshapes how people work, how decisions get made, and how value is created.

In our conversation, she breaks down why involving employees early and often isn’t a “nice to have”, it’s the difference between momentum and resistance. When people feel AI is happening to them, adoption slows. When they’re invited to help shape it, trust builds and capability grows.

We also tackle one of the biggest misconceptions in AI: that governance slows innovation. Colleen explains why the opposite is true. Clear governance, aligned leadership, and defined decision rights actually enable speed by preventing tool sprawl, disconnected pilots, and wasted investment.

This episode is packed with practical insight for leaders who are tired of experiments that never scale and are ready to turn AI into real impact.

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As always, thanks for being part of this community.

— Kirstin

Welcome to a purpose-driven conversation with Laston Charriez, a longtime CPG marketing leader turned educator and advocate for transforming how we grow food, nourish people, and undo bad practices.

Laston shared something that takes real courage to say out loud:
“I was part of the problem, and now I can be part of the solution.”

After decades working inside food systems optimized for cost, scale, and efficiency, he began asking harder questions about what those systems were actually delivering to consumers. Longer lifespans, but not healthier ones. That realization led him to focus on healthspan, and on how agriculture, CPG, and technology are evolving together.

In this episode, we talk about how AI has real promise in helping drive that transformation. Not in abstract ways, but through practical applications already taking shape, from improving soil health and crop yields, to reducing waste, to enabling more nutritious food at scale. This will help us avoid a global food shortage anticipated by 2050.

We also dig into the hard part: adoption. Farmers operate on thin margins. One failed experiment can put an entire year at risk. Data infrastructure and connectivity aren’t evenly distributed. And trust can’t be rushed. We talk about how status quo carries risk too — climate volatility, degraded soil, and rising costs make inaction just as dangerous.

This conversation is about leadership, accountability, and systems-level change. It’s about recognizing when incentives are misaligned, having the humility to help fix them, and seizing the opportunity when technology brings positive change within our grasp.

🎧 Listen to “AI, Ag, and the Future of Food: Increasing Healthspan, Not Just Lifespan”

Laston Charriez is an Assistant Professor and Industry Liaison at Colorado State University. He previously held executive marketing roles at Proctor & Gamble, Charmin & Puffs, Sara Lee, and Western Union.

Thanks for being part of this community and for leaning into these bigger questions with me.

— Kirstin

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This week’s episode of Lead The Machine digs into one of the most important truths emerging: the future of industry, software, and work are now deeply intertwined with what emerges in the era of AI. And few people understand that intersection better than Tim Griffin.

Tim has spent his career helping companies rethink workflows, redesign software systems, and automate what’s broken. His perspective is unusually broad, from his early days at Accenture where he helped pioneer low-code software to co-founding a healthcare tech company, ALN Medical Management, to real estate investing.

He’s seen how systems fail, how people adapt, and why AI is forcing each of us to confront uncomfortable questions about cost, fairness, and the future of our jobs.

We talk about a lot in this episode, but three themes stood out:

1. AI in healthcare is inevitable, and insurers will drive much of the adoption.
Tim explains why payers will use AI for denials, risk prediction, and quality scoring whether providers are ready or not. And if insurers move faster than clinicians, patients will feel the friction.

2. Software as we know it won’t survive this era.
Tim breaks down why legacy systems can’t keep up. Not because companies don’t want to change, but because the logic embedded inside those systems locks everything in place. AI will eventually rewrite the model itself.

3. The future of work belongs to people who stay close to the customer.
Tim is blunt: some jobs will be displaced. The safest careers will be held by those who reduce friction, integrate systems, and understand real-world problems deeply.

This is a wide-ranging, honest conversation about what’s coming, and what leaders need to prepare for now.

Thanks, as always, for being part of this community.

— Kirstin

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In this week’s episode of Lead The Machine, I sit down with David Chaddock, a cybersecurity and compliance leader who has spent decades guiding organizations through high-stakes decisions where trust, risk, and innovation collide. David was previously at Accenture and West Monroe Partners. Now, he is Managing Partner and Co-Founder of Infinite Partners, a firm that provides fractional, interim, and on-demand trusted advisors.

What makes this conversation special is David’s perspective on how AI is reshaping leadership at every level. He makes a powerful point:

“Leadership has nothing to do with who sits at the top of the org chart anymore.”

AI has flattened knowledge hierarchies. Digital natives now lead with intuition and speed. Experienced professionals bring the judgment you can only earn by living through complexity. And organizations need both to move forward responsibly.

We talk about why AI projects stall, how incentives shape behavior, and why “progress over perfection” is becoming a non-negotiable principle for modern leaders.

We call out how sharing the outcomes included in executives’ bonus plans can unlock the kind of success that only comes when we are aligned. Most importantly, David shares what it looks like to build trust across teams when everything feels uncertain.

If you’re navigating AI change—or leading people who are—this episode will give you a fresh, pragmatic way to think about the road ahead.

Let me know what resonates for you.

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This week’s episode of Lead The Machine is one I’ve been excited to share. I sat down with Marty Welch, a seasoned insurance executive and CEO of HEMIC, whose career has been shaped by leading people through waves of technological change — from the first PCs to predictive analytics to today’s era of AI.

What stands out about Marty isn’t just his experience. It’s his clarity.

He understands, better than most, that the biggest barrier to AI adoption isn’t the technology, it’s fear. And fear thrives in silence.

Marty shares how great leaders build trust by bringing people into the process early, admitting what they don’t know, and aligning technology with mission rather than trend. He explains why learning is a more powerful frame than change, and how AI can finally free people to focus on the work that matters most, and how to keep empathy and connection at the forefront.

Marty is an amazing mentor for all of us in human-centered leadership in complex situations. If you’re navigating AI, leading teams through uncertainty, or simply trying to understand how to build trust in a world that’s moving fast, Marty’s insights will resonate.

Thanks for listening and for being part of this community.

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Matt Mosher is a Strategic Advisor to Stonybrook Capital and the former CEO of AM Best Rating Services. He brings a rare, honest perspective on innovation and the importance of leadership and culture.

Matt doesn’t just talk about what’s possible, he names the patterns. He breaks down four types of organizations and how they approach change:

1. Innovation-Driven – Early adopters that experiment, aren’t afraid to fail and shape the future.

2. Reactive but Willing – Fast followers that move once others prove it’s safe.

3. Cautious – Firms that struggle to modernize and lack internal clarity on how to adapt. Often constrained by siloed teams and legacy systems.

4. Coasting on Historical Success – Firms stuck in a comfort zone that don’t feel the urgency to change. Makes them vulnerable to major tech transformations.

He’s clear on what separates those who adapt from those who don’t. We talk about:

• Why innovation stalls and what to do about it.

• How to build organizational confidence around AI and data.

• What responsible leadership looks like under regulatory pressure.

The level of detail companies provide for their financial strength ratings is vast. Few people get a bird’s eye view with the depth that Matt has experienced.

This episode will help anyone leading AI or data transformation where the stakes are high, and the barriers are real.

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“I was shocked. I didn’t get a single journalist to tell me that this is a career worth pursuing.”

After years spent getting an undergraduate and graduate degree in English, with her sites set on being a journalist, Chloe Frantzis was told by everyone in the field that journalism is a dead-end career. Putting aside what that means for the current state and future of journalism, it was demoralizing to find out that her career aspirations weren’t going to work out as planned.

“The first time my best friend ever saw me cry was because of AI.”

In this Lead The Machine episode, Chloe shares what it felt like to have her dream career disrupted as she realized how quickly AI was reshaping the creative world she hoped to join.

The story doesn’t end there. Rather than cower away, Chloe embraced learning AI and used this new knowledge to rebuild her career path. Today, she works at BoxMedia.IO in London, helping make AI-generated content sound human again.

Are we shortchanging what Gen Z brings to the table?

Chloe shares a message every leader should hear about Gen Z and the future of work.

“People think we’re lazy or glued to our phones,” she told me. “But I’ve seen older adults’ way more glued to theirs. We actually crave work, structure, and collaboration. We grew up with volatility — the iPhone, social media, AI — so we’re not afraid of change. We expect it.”

Chloe believes her generation is built for what’s next. Having witnessed constant disruption, Gen Z doesn’t fear transformation. Rather, they anticipate it, adapt to it, and want to shape it.

We talk about rejection, reinvention, and how Gen Z is redefining creativity and resilience in the AI era.

Chloe’s story is a reminder that adaptability isn’t just a skill, it’s survival.

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That’s how my dad, Clay Marr, describes his early years at the dawn of Silicon Valley, from his time at Lockheed getting the first man on the moon and at Fairchild launching the semiconductor revolution.

Bottom line: AI requires the research discipline that made Silicon Valley what it is.

Would you believe that AI innovation is directly tied to winning World War II?

The technologies that shaped victory: early computing, radar, and the birth of systems thinking also laid the groundwork for Silicon Valley’s rise. It was bold, long-term, and funded by vision, not quarterly targets.

In this Lead The Machine episode, Clay describes how he carried that spirit forward when product research meant creating what didn’t yet exist.

We talk about what that spirit of invention can teach us today as AI goes through its own identity crisis, still a platform trying to figure out its product strategy.

From the era of semiconductors to the rise of AI, Clay’s perspective is a masterclass in how real innovation happens: through patience, accountability, and a willingness to experiment before the path is clear.

Plus, my dad’s story is straight up cool.