How to Build AI Products That Actually Scale

Hi everyone, 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. — Kirstin

Hi everyone, 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

When only 13% of companies feel confident measuring AI ROI, you know two things: you are in a hype cycle for a new technology, and operational issues prevent progress. AM Best’s report, “Artificial Intelligence Appears to be Ready, But Most Insurers Are Not,” makes that reality hard to ignore. Sure enough, 45% of insurers cite data readiness as a top challenge. 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: * Treat data readiness as an operating priority, not a side project. * Define board-ready success measures early, then manage to them with leadership alignment. * Build governance and talent development in parallel so adoption scales without breaking trust. Thank you for listening. - Kirstin

Hi everyone, 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

Hi everyone, 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 11th 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