Human Capital Gains

The future of pediatric medicine may come down to one radical idea: stop moving data to the cloud and move the AI to the data instead.Tim Chou, former leader of Oracle’s cloud business and a Stanford professor, explains how privacy-preserving, distributed AI could help hospitals detect rare diseases earlier, close specialist gaps, and improve outcomes for children worldwide.In this episode of Human Capital Gains, Gary Lamach and Marko Horvat explore:

• Why healthcare’s biggest AI challenge isn’t data scarcity, but how to compute on data that can’t be centralized
• How distributed learning can power privacy-preserving models for ultrasounds, EKGs, MRIs, and blood analysis
• The potential of patient digital twins to support earlier diagnosis
• How AI could help identify conditions such as congenital heart defects, FSGS, and focal cortical dysplasia
• Why healthcare may need to move from “data archaeology” toward intelligent, real-time decision support
• How combining clinical information with social and environmental context could enable care “from zip code to genetic code”
• Why the future of AI in medicine is more about augmenting clinicians than replacing them

Chou also shares the vision behind the Pediatric Moonshot: building a distributed AI infrastructure connecting children’s hospitals around the world to reduce healthcare inequity, lower costs, and improve outcomes.If you’re interested in the future of healthcare, medical AI, rare disease detection, or how technology can expand access to world-class expertise, this episode is essential listening.Learn more at PediatricMoonshot.org.

In this conversation, Geoffrey M. Roche discusses the evolving landscape of healthcare education and workforce development, emphasizing the need for strategic partnerships between academic institutions and employers. He highlights the importance of agility in education, the role of apprenticeships, and the integration of AI to enhance healthcare delivery.

Takeaways

• Employer involvement drives innovation
• Apprenticeship models enhance workforce readiness

Chapters

• 00:00 Introduction to Healthcare Solutions
• 03:04 Driving Agility in Healthcare Education
• 05:50 The Importance of Strategic Partnerships
• 09:08 Navigating Accreditation Challenges
• 12:12 Addressing the Nursing Shortage
• 14:49 Leveraging AI in Healthcare
• 19:02 Investing in Workforce Development

CFOs are racing to adopt AI, but most are still chasing hype instead of results. The real winners in 2026 will not be the companies that buy the most tools - they will be the ones that pick the right use case, prove ROI, and upskill their teams fast enough to turn AI into performance.

Gary Lamach is joined by Doug Orsagh of OneStream and Marko Horvat for a sharp, practical conversation on what AI readiness actually looks like inside the finance function. They break down why so many AI projects stall, why "replace headcount" is usually the wrong story, and how CFOs can stop treating AI like a magic trick and start treating it like a business transformation plan.You'll discover why urgency around AI is exploding now, how to move from random experimentation to a strategy tied to the strategic plan, and why starting with a specific business problem - like improving forecast accuracy or reducing inventory days on hand - beats launching broad, unfocused pilots. Doug shares the framework that works: define the problem, connect the right tools, measure the outcome, and keep the board focused on concrete gains rather than vague promises.The episode also digs into the hardest questions CFOs are facing right now: what evidence proves a company is AI ready, how to evaluate whether a use case is actually delivering value, and why workforce upskilling has become one of the biggest barriers to progress. Marko adds the board-level and finance leadership perspective, pushing the discussion toward how AI budgets should be judged across functions, not just within finance.If your team is struggling to get traction with AI, this conversation is a reality check.

The opportunity is real, but so is the pressure - and the companies that win will be the ones that combine data transparency, disciplined execution, and a workforce prepared to use AI as an augmentation engine, not a replacement fantasy.Essential listening for CFOs, finance leaders, and board members who need to move AI from buzzword to measurable business value.

AI won’t transform your workforce because you bought a tool. It will transform it when leaders, learning teams, and business stakeholders finally align on the same question: what outcome are we actually trying to change?Rob Lauber, CEO and founder of XLO Global, shares the hard-earned lessons he’s learned leading learning and workforce transformation at McDonald’s, Yum! Brands, Singular Wireless, and beyond. From a $50 million-a-year education initiative to a high-stakes merger that required training 120,000 people in just 12 days, Rob makes the case that learning only matters when it solves a real business problem.We break down:

• Why ROI is the wrong first question and what to measure instead
• How McDonald’s reimagined Hamburger University by starting with the harshest business case
• The difference between compliance training, business enablement, and true workforce transformation
• Why AI adoption keeps stalling after the hype cycle
• The mindset, skill set, tool set model for making AI useful at work
• How CFOs evaluate funding requests and why learning leaders need to speak their language

Rob also explains why so many AI rollouts fail: organizations announce the tool, expect behavior change, and skip the hard part of preparing people and processes. If your team is being asked to “just upskill everyone” or “just AI that,” this conversation shows why that approach breaks down and what to do instead.This episode is especially valuable for CLOs, L&D leaders, HR teams, operations executives, and anyone responsible for turning learning into measurable business impact. If you want a practical, executive-level view of what actually drives adoption, credibility, and results, this one belongs at the top of your queue.

This episode features Gary Lamach in conversation with Marissa Parker, Chief Operating Officer of Stradley, Ronan, Stevens & Young. They dig into why AI adoption in law firms is less about technology and more about people, process, and trust.Marissa shares how her firm built a practical, human-centered training program for generative AI, including a 13-session series and a hackathon that helped lawyers move from skepticism to real use cases.

• Marissa Parker’s role at Stradley Ronan
  • As COO, Marissa partners with the Management Committee across talent, IT and security, administration, and knowledge management.
  • She frames her job as spotting problems and solving them.
• Why lawyers are especially skeptical of AI
  • Marissa says skepticism is built into the profession.
  • Early reactions centered on confidentiality, ethical duties, and fear of replacement.
• People, process, technology comes first
  • She emphasizes that AI rollout should start with people, then process, then technology.
  • GenAI works best as a complement to lawyers, not a replacement.
• “Vinny” as a practical AI assistant
  • The firm uses a tool called Vincent, which Marissa jokes about as “my associate Vinny.”
  • The point is speed and support for repetitive or late-night tasks, not full automation.
• Why many AI pilots fail
  • Gary notes the common pattern of buying tools before bringing people along.
  • Marissa agrees that adoption fails when firms skip training and cultural change.
• How Stradley Ronan built internal adoption
  • Head of knowledge management Sarah Huravett helped lead the program.
  • The firm created a 13-session training series for lawyers and some business professionals.
  • The sessions covered LLM basics, attorney ethics, and realistic use cases.
• The hackathon created momentum
  • The program culminated in a hackathon focused on three problem-solving concepts.
  • Teams worked on real-world pain points and presented solutions in a showcase format.
  • The competitive element helped generate FOMO and internal enthusiasm.
• Best use cases today
  • Legal research, document review, contract review, and NDA standardization.
  • Marissa says content generation is still more useful for idea generation than for reliable legal output.
• The email analogy
  • Marissa compares GenAI adoption to the early skepticism around email in law.
  • Over time, email became standard once confidentiality and security norms were established.
• ROI and pricing are tied to adoption maturity
  • The firm avoided expensive enterprise products early and used free tools and pilots first.
  • Marissa says pricing decisions make more sense once the firm has a proven adoption foundation.
  • A major opportunity is reducing time spent on work clients do not want to pay for, especially time entry.
• Near-term vs long-term disruption
  • Short term: support work, back-office tasks, first-draft generation, and data retrieval.
  • Long term: large-scale document digestion, due diligence, contract management, and highly accurate analysis of complex data.

AI will not replace your team, but it will expose whether your people can think, adapt, and collaborate in a world where skills expire in months, not years. Kirsten Moorfield, co-founder of Cloverleaf, reveals why the future of work belongs to organizations that build courage, relational curiosity, and human-centered AI into everyday workflows.Kirsten has spent nearly a decade building Cloverleaf, an AI coaching platform that uses psychology-based assessments like DiSC, CliftonStrengths, and Enneagram to coach people inside Microsoft Teams, calendars, and email. She explains why the original “AI coach” positioning struggled in 2018, why the market is finally ready now, and how automated coaching can support better decisions in the flow of work instead of adding another layer of generic content.You’ll discover why the old model of L&D is breaking, what the newest research says about the shrinking shelf life of skills, and why foundational human skills are becoming more valuable as AI automates tactical work. Kirsten shares how learning leaders, talent teams, and executives should rethink development, measurement, and accountability when completion rates and seat time no longer tell the real story.We break down:

• Why ChatGPT can be a great or terrible coach depending on how it is prompted
• Why “agility” may matter less than courage when teams face ambiguity
• How AI can personalize coaching using real employee context, not just broad training libraries
• Why so many AI initiatives fail when people and processes are left behind
• How HR, talent, performance, and development are converging into one connected system

This conversation also gets practical about the stakes. If organizations keep treating AI like a shiny tool instead of a strategic shift, they risk wasted spending, low adoption, and frustrated employees. But when leaders use AI to support human connection, curiosity, and better collaboration, they create stronger teams, smarter development, and a more competitive business.Essential listening for HR leaders, L&D professionals, talent strategists, and anyone trying to make AI actually work for people—not just for hype.

Most companies do not have a strategy problem—they have an identity problem. Scott Roslyn reveals why the brands that scale fastest are the ones willing to rethink who they are before the market forces them to. If you're leading through growth, reinvention, or AI-driven change, this conversation will hit home.Scott is the CEO of We Are Bill and Alignment & Company, and he has spent years helping organizations and executives make sense of transformation from the inside out. On the brand side, he works with companies at the moment of creation and reinvention. On the human side, he helps leaders navigate the more personal questions that show up when change gets real: What do I stand for? What needs to evolve? What does alignment actually look like when the pressure is on?You'll discover why Scott calls websites “lagging indicators” of a company's potential, how a brand can outgrow the story it tells about itself, and why the most effective companies are often the ones that stay small in spirit while serving global clients. He breaks down the thinking behind naming a consultancy “Bill,” the role of simplicity in strategy, and how being referral-driven and highly selective became a competitive advantage rather than a limitation.We also get into the overlap between brand transformation and people transformation. Scott shares why so many executives at the top feel isolated, why telling the truth gets harder as stakes rise, and why the next wave of leadership is less about performative success and more about fulfillment, adaptability, and trust.

• A company’s identity should keep pace with its growth; when its story lags behind reality, momentum suffers.
• Honest outside perspective helps leaders see what internal teams may have normalized or overlooked.
• Staying selective and intentionally small can become a powerful positioning advantage.
• Complex businesses need simple, compelling stories—not less sophisticated ideas.
• Leadership becomes difficult when personal identity and professional identity fall out of alignment.
• Senior leaders need trusted people who will tell them the truth, not simply agree with them.
• Feeling stuck can be part of adapting and growing, rather than a sign of failure.
• Sustainable success comes from building a business and life that feel aligned on the inside and outside.

Perfect for founders, CEOs, operators, and anyone navigating change in their organization or life. By the end, you'll understand why real growth often looks like reinvention first—and why the struggle you're feeling may be a sign you're headed in the right direction.

AI is no longer a side project. It’s about to decide which companies move faster, hire smarter, and outproduce everyone else. Brad Moss and Glenn Hopper break down what actually works when teams are trying to turn AI from hype into measurable advantage, without leaking data, breaking trust, or wasting months on the wrong build.Brad Moss, founder and CEO of Enhanced AI and creator of Fluxprompt, brings the operator’s view: how to use AI to drive real revenue-per-employee gains, why the best starting point is a single high-ROI task, and how micro-agents and guardrails make AI accurate enough for serious workflows. Glenn Hopper, head of AI research and development at Aventus Advisory Group, adds the finance and controls perspective, showing where AI can safely deliver the biggest impact, how to think about auditable outputs, and why domain experts still need to own the process.You’ll discover:

• Why curiosity and experimentation matter more than “AI training” alone
• The difference between using chat tools and building AI into actual business workflows
• How to pick the right first project so you get adoption instead of resistance
• Where AI is already changing finance, variance analysis, reporting, and data access
• Why low-risk, human-in-the-loop automation is the smartest way to start
• What MCP, integrations, and conversational layers unlock for companies with data in real systems
• How organizations should think about responsibility, accuracy, and skill attrition as bots take over more tactical work

Brad and Glenn also tackle the bigger strategic question: if AI is getting easier to use every month, what actually becomes the competitive advantage? Their answer is a future where every strong company becomes AI-first, with the best performers acting like orchestrators, designing smarter systems rather than just using better tools.If you’re trying to move from “we should do something with AI” to something that actually changes the business, this conversation gives you the clearest path forward. Essential listening for leaders, finance teams, operators, and anyone responsible for making AI real inside a company.

Most finance transformations fail for one reason that has nothing to do with the software. Tom Hood says the real blocker is mindset - and if you lead with tools before people are ready, you slow the entire shift down.

Gary Lamach and Tom Hood unpack why the future of finance is being shaped less by technology alone and more by how teams think, upskill, and collaborate. Tom shares the framework behind Riding the Waves of Transformation, the surprising research that says 80% of transformation success is people, and how finance leaders can create real buy-in before AI ever goes live.You’ll discover:

• Why fewer than one in five finance teams are proving real ROI from AI
• How to shift skeptics by showing them an outside-in view of the market
• The mindset, skillset, toolset model that flips transformation from resistance to momentum
• Why CFOs are becoming co-pilots to the CEO, and why controllers and FP&A teams are being pulled into that change
• The T-shaped skills finance professionals need now, including strategic thinking, storytelling, collaboration, and business acumen

Tom also breaks down how teams are using agile, bottom-up experiments like internal Shark Tank-style idea sessions to surface hundreds of AI use cases, eliminate “soul-crushing work,” and build confidence through pilots instead of big-bang rollouts.If you lead a finance team, work in accounting, or are responsible for AI adoption inside an organization, this conversation is a practical roadmap for turning fear into momentum and transformation into measurable business impact.Tom Hood is Executive Vice President of Business Engagement and Growth at the AICPA, where he helps finance and accounting leaders prepare for what’s next. He’s widely recognized for his thought leadership on technology, leadership development, and the future of finance.Perfect for CFOs, controllers, FP&A leaders, accountants, and anyone trying to make AI adoption actually stick.