The TechEd Podcast

For decades, the standard HR playbook has been attract, retain, develop. Jason Desentz thinks the order is wrong.
As Chief Human Resources Officer of Toshiba America, Desentz starts with the people already inside the business. In technical fields where experienced employees can take a year or more to train, retention is not simply an HR metric. It affects how quickly a company can grow, respond to new demand and capitalize on emerging markets. That equation is becoming even more important as AI infrastructure drives investment in energy and advanced technology while manufacturers compete for a limited pool of skilled technical talent. 
Desentz brings an unusually business-first perspective to HR. He evaluates people decisions against ROI, challenges his team to experiment with AI, and argues that HR leaders need to understand the technology, operations and economics of the companies they serve. At the same time, his approach is deeply human: listen to employees, get creative about the employee experience, invest in development and give people opportunities to try something new. From rebuilding pathways into manufacturing to preparing 6,000 Toshiba employees across the Americas for AI, this conversation explores what changes when people strategy becomes business strategy. 
In this episode:
Why the return of U.S. manufacturing is colliding with a technical talent pipeline weakened by decades of offshoring
How AI-driven data centers are creating new workforce demand across energy, infrastructure and field service
Why Desentz puts retention before attraction when thinking about talent strategy
How Toshiba evaluates the ROI of retaining highly specialized employees who can take more than a year to train
Why CHROs need to understand the CEO, operations, technology and business economics, not just HR
How high-school co-ops, technical education and experiential learning can rebuild pathways into manufacturing careers
3 Big Takeaways
1. Your workforce strategy is part of your growth strategy.
Toshiba sees significant opportunity as AI and data-center investment drives demand for energy generation, storage and infrastructure. But capturing that opportunity requires having specialized technical talent available when demand arrives. For a CHRO, workforce capacity becomes a strategic constraint that has to be planned alongside growth.
2. Calculate the business value of retaining technical expertise.
Some Toshiba field-service employees require more than a year of training to service complex equipment. Desentz estimates losing one could cost roughly $100,000, before accounting for the time required to rebuild that expertise. That changes the economics of retention: spending creatively to improve an employee’s experience can be far less expensive than replacing specialized capability.
3. Build AI capability by giving employees real problems to solve.
Toshiba launched a six-course AI-readiness curriculum through Toshiba University, but Desentz didn’t stop at instruction. His HR organization formed teams to build AI agents around actual business needs, including payroll, attendance and recruiting. Employees learned the technology by applying it, while Toshiba surfaced tools it could potentially deploy in the business.
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In 2025, Americans gave $617 billion to charitable causes. If your school has a bold vision for students, the money may be out there. The bigger question is whether you have an idea people want to invest in.
Too many organizations approach philanthropy by leading with what they need, chasing the biggest perceived “deep pockets,” or treating the conversation like a transaction. That can make fundraising feel uncomfortable for the person asking and uninspiring for the person being asked.
Michael Frohna has spent three decades helping organizations raise millions of dollars, and his approach challenges many of those assumptions. He shares what actually drives people to give, what separates a routine request from a transformational opportunity, and how education leaders can build the kind of vision and relationships that attract serious philanthropic support.
In this episode: 
Why one donor was upset Michael didn’t ask for enough
What $617 billion in annual giving means for education
Why philanthropists fund a vision, not an equipment list
What makes some education initiatives inspire major investment while others fall flat
How to move philanthropy from a transaction to a long-term partnership
3 Big Takeaways from this Episode:
1. You raise a million dollars by having a million-dollar idea. Philanthropists aren’t looking to fund a need. They’re looking for a vision that shows what their investment can make possible. Before asking for a transformational gift, make sure the idea itself is transformational and that your organization is prepared to deliver on it. 
2. The great paradox of fundraising: People dread asking for money, but people love to give. Michael has had million-dollar donors apologize that they couldn’t do more, and the only donor he ever upset was upset because Michael didn’t ask for enough. Stop viewing the ask as taking something from someone and recognize that you may be giving them an opportunity to make an impact they deeply value. 
3. The goal isn’t to make someone your donor. It’s to become one of their organizations. Major philanthropy isn’t transactional. Listen for what matters to the giver, bring them close enough to experience the impact for themselves, and continue engaging them long after the gift so they see your mission as part of their own.
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Every manufacturer says they need people. So why, after decades of talking about the skills gap, do so few workforce development models consistently deliver the talent employers actually need?
Tony Davis believes the answer is surprisingly simple: employers have to stop sitting on the sidelines. As Assistant Vice President of Program Scaling and National Director for FAME USA, Tony is helping manufacturers across the country build talent pipelines by putting industry in the driver’s seat alongside education. The result is a model that blends paid work experience, technical education and professional behaviors into one employer-led system.
In this episode, Matt and Tony explore how the Federation for Advanced Manufacturing Education (FAME) grew from Toyota’s workforce strategy in Kentucky into a national initiative of the Manufacturing Institute, why professional behaviors deserve the same emphasis as technical skills, what visitors experience inside the flagship Kentucky FAME facility, and how employer collaboration is helping scale one of the country’s most successful advanced manufacturing workforce models.
In this episode:
Why Toyota was the perfect place to launch an efficient, lean, and optimized workforce development model
The "soft skills vs. hard skills" debate
The unbelievable impact of treating a classroom or lab like a workplace
Inside the flagship FAME chapter in Kentucky
Why every manufacturer - not just large enterprises - should get involved in these programs
3 Big Takeaways from this Episode:
1. Manufacturers should lead workforce development, not simply participate in it. The most effective workforce programs begin with employers defining the skills they actually need, rather than reacting to a curriculum after it’s already been built. FAME flips the traditional model by making manufacturers true partners in recruiting, curriculum and continuous improvement.
2. Professional behaviors are developed through culture, not coursework. Communication, accountability, leadership and critical thinking aren’t mastered in a single class. They’re reinforced every day through immersion, expectations and real workplace experiences alongside technical training.
3. Building a workforce model is one challenge. Scaling it is another. Expanding from a successful local program to a national network requires more than enthusiasm. Systems, quality assurance and continuous improvement ensure students in every chapter receive the same high standard of preparation.
Resources in this Episode:
FAME USA
The Manufacturing Institute
National Association of Manufacturers (NAM)
Inside Jefferson County Community & Technical College FAME AMT Program
Connect with our guest online:
FAME USA on LinkedIn |  Connect with Tony on LinkedIn
Find more notes & resources on the episode page!

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Static career maps, spreadsheets and disconnected HR systems can't keep pace with how quickly jobs are changing. Today’s leading employers are rebuilding their workforce architecture around skills. As companies rethink talent, career growth and workforce strategy, becoming a skills-based organization is emerging as a fundamental shift in how enterprises structure roles, create career pathways and develop their people.
In this episode, Matt sits down with Liz Eversoll, CEO of Career Highways, to explore how a skills-based approach can help large enterprises solve one of their biggest workforce challenges: understanding the talent they already have and responding to change faster. They discuss how organizations can map and continuously improve role architecture in a fraction of the time, deliver personalized learning aligned to each employee’s career goals, and give people greater visibility into lateral moves, upward mobility and entirely new career pathways across the enterprise. At the same time, leaders gain real-time intelligence into workforce capabilities, emerging skills gaps and where learning investments will have the greatest business impact.
The conversation also explores the technology making this possible. Liz explains why deterministic AI, grounded in business context rather than public data alone, is essential for trusted workforce intelligence. She argues that as AI automates more routine work, people with deep business context become even more valuable. The future isn’t about replacing employees. It’s about giving them better information, accelerating career growth and freeing them to focus on the work where human judgment creates the greatest value.
In this episode:
Why 3,500 roles and 230 career pathways can’t live in spreadsheets anymore
The career lattice: Finding your next role with an 80% skills match
SIGN, Canon and Liquid Insights: The technology behind deterministic AI
Why business context becomes more valuable as AI automates routine work
“Talk to your data”: The future of decision-level workforce intelligence
3 Big Takeaways from this Episode:
1. Becoming a skills-based organization changes how enterprises compete. Managing talent through job titles and static career paths is no longer enough. Skills-based job architecture gives organizations a living view of their workforce, helping leaders adapt faster, make better workforce decisions and align learning, hiring and internal mobility with changing business needs. It’s a fundamental shift in how large enterprises understand and develop talent.
2. Career health is a competitive advantage. When employees can clearly see where they can go, understand the skills they need and access personalized learning, they’re more likely to build their careers within the organization. That improves retention, preserves valuable business context and helps employers fill critical roles with people who already understand the business instead of constantly competing for outside talent.
3. AI works best when it’s grounded in business context. AI isn’t replacing workforce strategy. It’s making it smarter. Deterministic AI and enterprise knowledge give leaders trusted workforce intelligence while automating repetitive work that slows people down. As AI handles more routine tasks, employees with deep business context become even more valuable because they’re the ones who can interpret insights, improve processes and create lasting business value.
Resources in this Episode:
Career Highways - Learn more about their technology, solution, AI services and more
Find more on the show notes page! https://techedpodcast.com/eversoll/
Connect with our guest online:
Career Pathways LinkedIn |  Connect with Liz on LinkedIn
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Imagine a future where healthcare consists of engineers working alongside medical researchers and clinicians to build better ways to measure the body, model disease, predict risk and design more effective diagnostics and treatments. That's what Dr. Kristin Myers is doing in the field of women's health.
Digital twins have transformed manufacturing by allowing engineers to simulate systems, predict failures and optimize performance before making changes in the real world. Dr. Kristin Myers believes those same engineering principles could fundamentally reshape healthcare. As a mechanical engineering professor at Columbia University, Myers is applying computational modeling, AI and biomechanics to one of medicine’s most complex frontiers.
In this episode, Myers explains why women’s health has historically been difficult to study, how engineering disciplines are beginning to fill decades-long research gaps, and why technologies like digital twins, wearable sensors, machine learning and computational models may dramatically improve diagnosis, treatment and long-term patient outcomes. She also explores what this emerging field means for engineers, educators and the next generation of healthcare innovation.
In this episode:
Why digital twins could become as important in healthcare as they already are in manufacturing.
The engineering challenges that have slowed progress in women’s health research for decades.
How AI, wearable devices and longitudinal patient data could transform diagnosis and personalized medicine.
Why mechanical, electrical and software engineers all have a role to play in the future of healthcare.
What engineering educators should teach today to prepare students for tomorrow’s biomedical breakthroughs.
3 Big Takeaways from this Episode:

1. Engineering is becoming a core driver of healthcare innovation. The future of medicine won’t be built by clinicians alone. Myers explains how mechanical engineers, computational modelers, AI researchers and device designers are bringing new tools and ways of thinking to problems that traditional medical research has struggled to solve.
2. Digital twins are moving from factories to patients. The same technologies manufacturers use to simulate equipment and optimize production are beginning to model organs, pregnancies and disease progression. While clinical implementation remains years away in many applications, digital twins are already accelerating biomedical research and medical device development.
3. Tomorrow’s engineers will need both technical fundamentals and AI fluency. As AI reshapes engineering education, Myers argues that foundational engineering principles remain essential. Students must still learn how systems work from first principles while using AI to accelerate analysis, design and innovation rather than replace critical thinking.
Resources in this Episode:
ERVA (Engineering Research Visioning Alliance - NSF)
Report: Transforming Women's Health Outcomes through Engineering
Connect with our guest online:
ERVA Facebook  |  ERVA LinkedIn  |  Connect with Kristin on LinkedIn
More notes & resources on the episode page: https://techedpodcast.com/columbia/
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