The TechEd Podcast

Your organization doesn’t have a data problem. It has an intelligence problem: the gap between having information and being able to act on it with speed, clarity, and confidence. That gap shows up everywhere: hospitals, schools, manufacturers, and any team drowning in dashboards while leaders still wait on someone to “find the story.”
Rick Anderson (Chairman & CEO, The Hive Health) is back to show just how much AI has impacted one organization in 12 months. Enter Corby Furrer (Harvard AI fellow, builder since college) and Will Furrer (former NFL quarterback turned COO). Together, they've built what they call a "trade intelligence platform" - not another analytics tool, but a system that encodes economic expectations, reconciles them against purchasing reality in real-time, and tells people exactly what action to take when behavior drifts off course.
Intelligence isn't about regression models anymore. It's about knowing what "good looks like," verifying AI assumptions through human-in-the-loop, and translating observations into stories that change behavior when delivered by the people who speak the right language (physician to physician, engineer to engineer, teacher to teacher, not consultant to administrator). Sustainable change requires three legs: understanding the rules of the game, seeing what's actually happening (not what's supposed to happen), and coaching insights through stakeholders who can shift behavior.
AI scales when it creates shared clarity people can validate and act on repeatedly, not when it generates reports that collect dust behind the CEO's desk.
In this episode:
Why your organization can have endless dashboards and still lack decision-grade intelligence. 
What must be true for leaders to trust AI results enough to act on them. 
How a data observation becomes a story for the change agent that actually drives behavior change. 
How coding and product building changes when AI can generate code, and why knowing “what good looks like” matters.
Why one-time improvements fade, and what it takes to build a repeatable system.
3 Big Takeaways from this Episode:
1. Decision-grade intelligence starts with clear expectations and a next action. The bridge from data to intelligence = what should happen, reconciling it against what is happening, and using that gap to drive the next corrective step. The takeaway is widely applicable: if you cannot state the intended economic or operational outcome, you cannot reliably diagnose variance or drive consistent performance.
2. If the improvement is not repeatable, it is not a solution. Build a system that codifies the work, monitors performance against targets, and keeps savings from reverting once the project ends. The real value in AI projects is durable behavior change and ongoing detection of the next opportunity, not a one-time finding.
3. Insights only matter when they are delivered to the change agent as a story that drives action. A data observation has to become a narrative that the person who can change the behavior will actually respond to. In the AI era, that elevates a specific skill stack: storytelling, curiosity, and building, because trust and adoption live or die in communication and execution, not in the existence of a model. 
Links & resources
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The U.S. Department of the Interior manages the nation’s most consequential assets—public lands and waters, energy resources, and critical minerals—making it a crucial center for AI capabilities, national security, and workforce opportunity.
In this episode of The TechEd Podcast, host Matt Kirchner sits down with Doug Burgum, U.S. Secretary of the Interior, to connect the dots between Interior's responsibilities and the next generation of innovation in the U.S. Today, Interior manages 500 million acres of public land, plus subsurface and undersea resources, territories, and the nation’s historic sites, national parks, Fish & Wildlife, and offshore energy footprint.
All of those resources are tied to America's opportunity to innovate in areas like artificial intelligence. Secretary Burgum frames AI data centers as “intelligence factories”, industrial-scale facilities that convert electricity into intelligence, and argues the next wave of competitiveness will be decided by scalable energy and the materials supply chain behind it.
We get into rare earth minerals, nuclear power, the tech and energy race with China, and the opportunities for today's students to pursue cutting-edge careers.
The episode also widens the lens to the country’s long-term innovation narrative. Burgum ties today’s tech inflection point to America 250 and the Theodore Roosevelt Presidential Library—a reminder that public lands, history, and national ambition can be part of how we inspire the next generation to build.
In this episode:
The shift from AI as software to AI as physical infrastructure — and why land, power, and materials suddenly matter
Why data centers are becoming “intelligence factories” — and what that changes about how AI scales
The truth about rare earth minerals — (why they aren’t actually "rare") and why processing is the real bottleneck
The nuclear energy race with China — and why speed, not discovery, is the deciding factor
Where the real career opportunities are emerging — far beyond software, deep into energy, minerals, and infrastructure
Resources in this Episode:
Visit the U.S. Department of the Interior
More resources from this episode:
Bureau of Indian Education
Theodore Roosevelt Presidential Library
More notes & resources on the episode page! https://techedpodcast.com/burgum2

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Most leaders have a vision, a plan, and the authority to move it forward, but real momentum shows up when you understand how culture is being shaped through trust and influence behind the scenes.
Host Matt Kirchner sits down with Dr. Ben Johnson, Assistant Superintendent for Secondary Schools at Bismarck Public Schools, and Bobby Dodd, Assistant Principal at May River High School, co-authors of Intentional Influence. They break down how influence really spreads inside an organization, in schools, in business, and in industry, and why the people with the most impact are often not the ones with the biggest titles.
At the center of the conversation is their cultural mapping framework—making the invisible influence network visible. You’ll hear how to identify formal and informal influencers, classify commitment on a five-point scale, and invest your time where it will actually shift the culture instead of just managing noise.
In this episode:
How to move a team from compliance to commitment—without pressure, politics, or performative buy-in
Why “trust is the currency of culture,” and how to build it in everyday leadership moments
The cultural mapping basics: formal vs. informal leaders, a five-point commitment scale, and understanding how influence flows throughout your organization
The difference between positional power and personal power, and why titles can create action without creating true alignment
“Energy vampires” and the “pinging effect”: how attitudes spread through a team, and how strong leaders respond in a way that protects momentum
3 Big Takeaways from this Episode:
1. Lasting change is a culture outcome, not a plan outcome. Compliance can produce short-term execution, but commitment is what sustains new behaviors when nobody is watching. The work is to build alignment and trust so people internalize the “why” and carry the standard forward.
2. Cultural mapping helps you lead the real organization, not just the org chart. Influence runs through informal networks of credibility and relationships, and the highest-impact people often do not have the biggest titles. When you identify formal and informal influencers and where people sit on a commitment scale, you can invest your time where it will actually shift the culture.
3. Influence spreads fast, so leaders have to manage energy and momentum intentionally. “Energy vampires” and the “pinging effect” are real, and unchecked negativity multiplies through the network. The goal is not to label people, but to understand what’s driving resistance, address it directly, and redirect influence toward the commitments the organization is trying to build.
Resources in this Episode:
Get the book Intentional Influence: Harnessing Cultural Mapping to Build Commitment
More resources on the show notes page: https://techedpodcast.com/influence
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Higher education is shifting toward a connected model where colleges and universities function as one learner ecosystem. The goal is simple: make credentials stackable, transfer predictable, and pathways flexible enough for learners to move in and out of education as their careers evolve.
In this episode of The TechEd Podcast, Matt Kirchner speaks with Dr. Katherine Frank (Chancellor, University of Wisconsin–Stout) and Dr. Sunem Beaton-Garcia (President, Chippewa Valley Technical College) about how their institutions have developed streamlined pathways for learners that support lifelong learning.
They break down how institutions can design on-ramps and off-ramps, align programs across tech/community college and university systems, expand credit recognition, and keep partnerships active so transfer works in real life (no more "credits to nowhere"). The conversation also expands to what this shift means nationally as technology and workforce needs change faster.
Watch this episode on YouTube!
In this episode:
What a connected model for colleges and universities actually requires in program design and policy
How to make transfer predictable and student-friendly without lowering academic standards
Why stackable credentials and credit for prior learning matter more as learners move in and out of education
How to get around the red tape that has traditionally prevented colleges and universities from creating streamlined transfer pathways
What higher education leaders should do next if they want to build the new model in their own region
3 Big Takeaways from this Episode:
1. A connected model keeps learners moving across colleges and universities. Stackable credentials, credit for prior learning, and predictable transfer reduce the stop-and-start pattern that derails working adults and career-changers. When pathways are designed for entry, exit, and return, education becomes a long-term system learners can use throughout their careers.
2. Transfer works at scale when it becomes an operating habit, not a one-time agreement. The UW–Stout and CVTC alignment shows what changes when institutions treat pathway design as ongoing work with shared ownership and recurring check-ins. That consistency is what makes transfer feel clear to students and sustainable for faculty and staff.
3. This model makes it easier to keep programs aligned as technology and jobs change. Modular, competency-aligned pathways let institutions update portions of a program without rebuilding the entire structure. It is a practical way to respond faster to industry signal while protecting rigor and program quality.
Resources in this Episode:
Read the op-ed co-written by Drs. Frank and Beaton-Garcia: "Reframing Higher Education"
➡️ Find more resources on the episode page: https://techedpodcast.com/disruption/
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What actually happens inside those massive Amazon facilities—and how do products arrive at your door with such astonishing speed?
In this episode of The TechEd Podcast, host Matt Kirchner explores these questions with Amanda Willard, Strategic Workforce Development, and Logan Schulz, Senior Manager of Reliability & Maintenance Engineering at Amazon. They take us behind the scenes of the advanced robotics, mechatronics, and automation systems that power Amazon’s fulfillment network—and the skilled technicians who keep the entire operation running.
Amanda and Logan share how the Reliability & Maintenance Engineering (RME) team prepares the workforce behind this technology, including Amazon’s mechatronics and robotics apprenticeship. They reveal what today’s technicians actually do, the durable skills that matter most, and how Amazon develops talent capable of maintaining one of the world’s most complex automation ecosystems.
Listen to learn:
How Amazon uses robotics, AMRs, vision systems, and miles of automation to move products at remarkable speed
What actually happens inside the RME apprenticeship, from 12 weeks of training to 2,000 hours of structured mentorship
Why durable skills like troubleshooting, analytics, and system connectivity matter more than any specific technology
How data, AI, and predictive maintenance are reshaping the technician’s role
What technical educators should teach now to prepare learners for next-generation automation careers
3 Big Takeaways from this Episode:
1. Maintenance roles have shifted from mechanical work to high-level cognitive problem-solving. Technicians at Amazon diagnose interconnected networks, sensors, PLC systems, and smart devices alongside mechanical equipment. This evolution requires system-level thinking, the ability to interpret data, and strong analytical abilities—skills that anchor long-term career growth.
2. Apprenticeships are a business strategy that strengthens the entire talent pipeline. Amazon’s mechatronics and robotics apprenticeship builds internal talent, increases employee retention, and prepares the workforce for future technology needs. With industry certifications, structured mentorship, and extensive hands-on training, the program creates a sustainable pipeline of highly skilled technicians.
3. Durable skills prepare learners for technologies that don’t exist yet. Troubleshooting methods, programming fundamentals, data analytics, and understanding how systems interconnect form the foundation technicians will rely on as automation accelerates. As AI, predictive maintenance, and IoT devices expand, adaptability and analytical reasoning will matter more than the specific robots or tools a technician first learned on.
Resources in this Episode:
Learn more about Amazon Reliability & Maintenance Engineering
Learn more about the Amazon RME Mechatronics & Robotics Apprenticeship program
Find more resources on the episode page! https://techedpocdast.com/amazon
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