If you want to understand why Idaho is uniquely positioned in the national AI landscape, start with three institutions: Boise State University, Micron Technology, and Idaho National Laboratory. Together, they anchor a technology stack that runs from energy and semiconductors all the way to workforce development and applied research. The Atoms and Algorithms panel at the Boise Metro Chamber's 2026 Regional Leadership Conference brought leaders from all three to the same stage to talk about what that collaboration looks like in practice and what it will take to make it even stronger.
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Moderated by Jessica Hagan of KTVB, the panel featured Nancy Glenn, vice president of research and economic development at Boise State University; Scott Gatzemeier, corporate vice president for front end expansion at Micron; and Todd Combs, deputy laboratory director for science and technology and chief research officer at Idaho National Laboratory.

Idaho Has the Full AI Stack. That Is Rare.
Scott Gatzemeier offered one of the panel's most clarifying frameworks: a five-layer view of the AI infrastructure stack, and where Idaho sits within it. At the base is physical infrastructure, power, water, utilities. Above that is semiconductors, the chips and memory that make compute possible. Then come data centers, then the models themselves, and finally the applications that businesses and consumers actually use.

What makes Idaho distinctive is that it has players at every level of that stack. There are only two places in the United States where cutting edge semiconductor research is actually conducted, and one of them is Boise. Nuclear energy research at INL is not being replicated anywhere else in the country at the same scale. And at the application layer, where AI agents are increasingly being used to build other AI agents, Idaho's growing startup ecosystem is positioned to move fast.

"Idaho uniquely spans the AI stack," Gatzemeier said, "from energy and chips through infrastructure and applied AI, with Micron and INL anchoring the bottom layers and a growing Boise startup ecosystem delivering real world applications at the top."

He noted that the bottom layers of the stack, infrastructure and semiconductors, require enormous capital investment and carry a natural competitive moat. The application layer, by contrast, is far more dynamic and far more open to disruption. That is where he expects the most rapid change, and where Idaho companies have the most opportunity to build something new.

The Partnerships Are Already Happening
When Hagan asked whether a meaningful three-way collaboration between Boise State, Micron, and INL was a realistic possibility, Todd Combs had a direct answer: it is already happening. All three institutions were partners on the federal semiconductor manufacturing hub called Smart USA before it was canceled. Now they are collaborating again on the Department of Energy's Genesis mission, a large initiative to double the productivity of R&D across roughly 28 topic areas and 99 subtopics, including semiconductors, geothermal, critical minerals, and nuclear.

A key feature of Genesis is that partnership is not optional. Proposals are required to involve at least two of three parties: universities, national laboratories, and industry. In practice, Combs noted, the expectation is all three. That structure plays directly to Idaho's strengths.

Nancy Glenn described Boise State's role in that ecosystem as the connective tissue. The university has long served as the convener between industry, national labs, and research institutions, helping translate what partners need into what students and faculty are working on. The materials science program is a direct example: built in partnership with Micron, it turned out to be equally relevant to the extreme environment material needs at INL. One investment, multiple beneficiaries.

Boise State and INL formalized their broader collaboration through a memorandum of understanding that spans well beyond nuclear, covering power systems, critical minerals, geothermal, electric vehicles, energy storage, and cybersecurity. The university also recently received National Science Foundation funding through its Junction hub for entrepreneurship to build the foundation for exactly these kinds of cross-sector partnerships, and a new research traineeship AI program is integrating AI fluency across disciplines, not just in STEM.

What Makes a Partnership Actually Work
The panel spent meaningful time on a question that often gets glossed over: what does it actually take for institutions with very different cultures, regulatory environments, and incentive structures to collaborate effectively? The answers were grounded and practical.

Combs identified three prerequisites. First, a common problem to solve. Partnerships formed around a vague intent to collaborate almost always fail. Second, complementary capabilities. If the institutions are doing too much of the same thing, they compete rather than build on each other. Third, a clear answer to the question of what is in it for each organization. Every partner needs to be able to see what they gain by being at the table.

Glenn pointed to something Idaho has that cannot be manufactured: proximity and accessibility. She can call Scott Gatzemeier or Todd Combs directly. Gatzemeier and Combs both sit on the engineering dean's advisory council at Boise State. Those relationships are what allow ideas to move quickly from conversation to collaboration. Gatzemeier added that Micron has contributed significant fabrication equipment to Boise State over the years, deepening a partnership that now spans more than two decades.

On the infrastructure side, Combs and Gatzemeier described ongoing conversations about co-locating AI infrastructure, particularly as data centers, semiconductor manufacturing, and energy generation increasingly need to sit close together. Even informal coordination, staying current on where each other is with microreactors and SMRs, helps both organizations make better decisions.

AI Inside the Fab and the Lab
Gatzemeier offered a window into how deeply AI is already embedded in Micron's operations. Inside the Boise fab, an automated handling system moves thousands of lots, each containing 25 wafers, through 1,500 process steps over a nine-month production cycle. At any given moment, more than 100,000 wafers are in process. The system routing all of that is 99 percent automated, functioning something like a real-time traffic system for an entire city, except the stakes are measured in atoms. If a wafer is misprocessed, the system automatically routes it to a remediation path without human intervention.

Micron also runs multiple large language models across the organization, including Copilot, Claude, and an internal model trained on company data. Process modeling is another major application area, using AI to predict and prevent deviations at the atomic level, where a film off by a single angstrom can render a wafer unusable.

At INL, the goal is to get every member of the lab's staff using AI on a daily basis, a target set by Lab Director John Wagner. Currently around 3,000 to 3,500 staff are using tools on at least a weekly basis. The lab has deployed Claude, ChatGPT, Copilot, and supercomputer-based AI systems, and recently opened API access to software developers to enable deeper integration. Combs noted that API usage at the lab cost around $5,000 in January, grew to $10,000 to $12,000 in February, and hit $35,000 in March. A million dollars a month, he said, is not far off.

The Talent Pipeline: Fluency Without Losing the Fundamentals
The panel's closing conversation turned to people, specifically what the AI-ready graduate looks like and where the talent pipeline still has gaps. The panelists were aligned on a tension that does not have an easy resolution: students need to be fluent in AI tools to be competitive, and they also need the subject matter expertise to use those tools well. One without the other is a problem.

Gatzemeier described the gap he sees most often: engineers who have the technical depth but have not yet learned to integrate AI tools into their workflows. He also offered a practical technique for managing AI's tendency to hallucinate: ask it how it knows what it just told you. The tool will surface its sources immediately, allowing the user to trace the logic and identify where the reasoning went wrong.

Combs put it plainly when addressing university audiences: if you know these tools, you are going to be able to do genuinely exciting work as an intern. If you do not, you will end up doing the boring stuff. But he was equally clear that turning off critical thinking is a serious risk. AI is only as good as the person prompting and probing it.

Glenn outlined what Boise State is doing to respond: training programs for faculty and graduate students on integrating AI into research and coursework, a new master's degree in applied AI that spans all disciplines rather than just computer science, and a consistent emphasis on validation, knowing not just how to use a tool but how to test whether its output is actually correct.

Combs also floated a more structured vision for the future: a formal program where undergraduates spend their first two years building foundational skills at Boise State, then rotate through summer experiences at INL and Micron before deciding whether to enter the workforce or pursue graduate study. The applied experience, he argued, would make every decision that follows more informed.

The panel closed on a note of genuine optimism, grounded not in aspiration but in what is already underway. The institutions are talking, the partnerships are forming, the federal government is actively funding collaborative work, and Idaho's position in the national AI landscape is stronger than most people outside the state realize. The work now is to make sure the people side keeps pace with everything else.

Nancy Glenn, Scott Gatzemeier, and Todd Combs delivered these remarks at the Boise Metro Chamber's 2026 Regional Leadership Conference on April 20, 2026. This blog post was prepared from a partial transcript using the help of AI.

AI adoption is not a future consideration for Idaho's business community. It is already happening, in operating rooms, insurance call centers, and technology firms across the Treasure Valley. The Actionable Intelligence panel at the Boise Metro Chamber's 2026 Regional Leadership Conference brought that reality into sharp focus, with three leaders who are not theorizing about AI but actively deploying it across their organizations.

Moderated by Laura Smith, vice president of PR and Government Affairs at Idaho Central Credit Union, the conversation featured David McFadyen, president and CEO of St. Alphonsus; Mark Ruszcyk, president of Regence Blue Shield of Idaho; and Tom Beeles, president and CEO of Allied Business Solutions. What they shared was a candid, practical account of what is working, what surprised them, and what every organization needs to do before taking its next step with AI.

AI Is Already Inside Idaho's Healthcare System
David McFadyen opened with a look at how St. Alphonsus approached AI from the start: by targeting the work that consumed the most time without directly touching patients. Billing and coding, which accounts for 15 to 16 percent of the workforce at many health systems, was the first area. Prior authorization denials that once required staff members to manually scour medical records for hours, sometimes days, can now be resolved in seconds.

Passive listening tools have changed the dynamics of clinical visits as well. When a physician is with a patient, the visit is now documented automatically, often more accurately than a physician could reconstruct at the end of a long day. The result: providers get home earlier, or they see more patients. The time is recovered either way.

On the imaging side, St. Alphonsus has been feeding scan data into an AI system for two years that reviews the entire image, not just the area tied to the patient's presenting complaint. Gallstones, incidental findings, early warning signs the patient did not come in for: the system flags them and routes them back to a radiologist for review. The patient is then informed and given the choice of whether to act.

McFadyen also described how AI is transforming care for complex patients seen by multiple specialists. Reviewing a chart well enough to have a meaningful conversation with a patient and their family could take a provider 30 to 45 minutes. AI can now surface a comprehensive summary in seconds, helping providers catch things that might otherwise be buried in a sprawling, difficult to navigate chart.

Six Years In: What Regence Has Learned About Adoption
Mark Ruszcyk brought the longest institutional perspective on the panel. Regence Blue Shield of Idaho has been on its AI journey for roughly six years, beginning with an attempt to build its own large language model. The work has focused on three areas: provider engagement, member engagement, and empowering employees to improve the experience for both.

One of the most visible changes has been in customer service. When a member calls with a question about coverage, agents no longer need to toggle between multiple screens to find contract details, policy documents, and claims data. AI agents surface all of it in real time while the conversation is happening, enabling a response that is specific, accurate, and immediate.

Prior authorization, long a source of friction between providers and insurers, has seen dramatic improvement. Ninety-six percent of prior auth approvals at Regence are now processed simultaneously. Even faxed forms from provider offices are handled through optical character recognition, which reads thousands of different form types and routes approvals in seconds rather than days.

Ruszcyk was candid about what surprised him along the way. He described sitting with a client two years ago who was already using AI in ways Regence had not yet built, and realizing that the speed of adoption in the market was far outpacing internal projections. The lesson: while building carefully and responsibly, it is equally important to keep watching what customers and competitors are already doing.

He also shared how the company navigated a significant shift in its coding workforce, from roughly 400 coders to 47, without eliminating a single job. Those employees were transitioned into cybersecurity and AI roles, keeping headcount flat while redeploying people into areas of growing organizational need.

What a Small Team Can Build in a Weekend
Tom Beeles offered a ground-level view of AI adoption at a smaller organization, and what he described was striking in its practicality and speed. Allied Business Solutions began by documenting all of its internal processes four years ago, which in hindsight proved to be the foundation for everything that followed. That documentation became the basis for a knowledge bot that any employee can now query to find answers to operational questions that previously required tracking down the one or two people who had memorized how things worked.

The company has since built agents that handle email inquiries from dealer partners automatically, pulling the requested information from the accounting system and sending a reply without anyone touching the request. IT ticket triage, which once relied on a human to assess severity, assign the right technician, find an opening on their calendar, and notify the customer, is now fully automated. The system reviews the ticket, evaluates the last thousand service records to identify the best qualified technician, checks their calendar, schedules the appointment, and sends a confirmation to the customer. If urgency requires a live response, the system routes it there immediately.

Beeles's biggest takeaway was not about speed or accuracy. It was about scale. A small team that understands AI can accomplish in a weekend what used to require months and significant outside spend. One employee built a network security prospecting tool in a single week that would have cost the company a thousand dollars a month as a software subscription. Another team built a working prototype of a custom CRM in one week, a project that a decade ago would have been a year-long engagement with an outside vendor.

How to Bring People Along Without Losing ThemAll three panelists have navigated the challenge of bringing a workforce along through significant change, and their approaches shared a common thread: do not lead with the technology.

Beeles learned this through experience. An early company-wide announcement about AI automation triggered immediate fear about job security. What worked instead was one-on-one conversations focused on a simple question: what part of your job do you hate? Identifying those specific pain points and then demonstrating a targeted solution, with the employee themselves presenting the win to their peers, built credibility in a way that no top-down rollout could.

Ruszcyk described a similar approach at Regence, creating challenge teams where half a department was asked to assume a process would not benefit from AI while the other half explored how it could. Within months, everyone wanted to be on the AI team. He also emphasized the importance of celebrating small wins publicly, making incremental innovation visible so it becomes self-reinforcing across the organization.

McFadyen's approach at St. Alphonsus leaned into change management infrastructure. National and local councils were stood up to channel the enthusiasm of early adopters while giving the organization a way to evaluate and implement new tools safely. He made a deliberate choice not to spend energy trying to bring along the small percentage of staff who were firmly resistant, trusting that patient expectations and peer example would eventually move most of them.

"If we spend all of our time worrying about how to bring those people along," McFadyen said, "we are not going to change the systems fast enough."

Governance First, Always
​When asked what advice they would give to organizations just beginning their AI journey, the panel converged quickly on one prerequisite: assume your people are already using AI, and build a policy before you think you need one.

Ruszcyk walked through the foundational principles Regence established early: be responsible, test before releasing, protect member and provider data without exception, and watch for bias in outputs. The company tests new tools internally on a self-selected employee population before anything reaches the public. Beeles stressed the importance of limiting what AI agents can access, drawing a direct parallel to how organizations approach new hires. You would not give an intern access to everything on day one, and the same logic applies to an AI agent.

McFadyen framed governance not as a constraint on innovation but as the structure that makes innovation sustainable. The goal is to create an environment where employees feel curious and safe to bring ideas forward, with a clear pathway for those ideas to be evaluated and implemented properly rather than adopted informally in ways the organization cannot see or manage.

Ruszcyk put it plainly: go slow to go fast. Build the policies, build the foundation, and the governance structure will eliminate much of the conflict that tends to surface later when organizations try to retrofit guardrails after the fact.
Beeles offered a closing thought that tied the conversation together. Getting into AI early means having time to make mistakes and fix them. The organizations that are willing to move carefully but move now are the ones that will be positioned to lead when the tools mature and the stakes get higher.

David McFadyen, Mark Ruszcyk, and Tom Beeles delivered these remarks at the Boise Metro Chamber's 2026 Regional Leadership Conference on April 20, 2026. This blog post was prepared from a transcript using the help of AI.

What does AI actually look like when it moves from buzzword to business practice? That was the central question driving the Beyond the Hype panel at the Boise Metro Chamber's 2026 Regional Leadership Conference. Moderated by Rebecca Hupp of Boise Airport, the conversation brought together three panelists with very different vantage points: Don Day of Boise Dev and Valley Lookout, Gordon Jones, president of the College of Western Idaho, and Cameron Schaefer of HDR. What emerged was an honest, grounded look at where AI is delivering real value, where it is falling short, and what businesses and institutions need to do to stay ahead of it.

Credibility Is the New Competitive Advantage for News
Don Day opened with a candid look at how AI is reshaping the local news landscape, and not always for the better. He described a growing wave of nontraditional players using AI to repurpose news content for their own business goals, whether that is driving social media engagement, selling real estate, or running content mills built around manufactured outrage. The result is a noisier, harder to navigate information environment.

He offered a striking example: a news outlet recently used AI to translate and publish a story about the Idaho Air National Guard deploying troops, pulling a quote from Governor Little that turned out to be from a news release the previous year, and conflating details from a past deployment with a much smaller current one. The story was wrong in specific, verifiable ways.

"It puts a lot more burden on us to make sure we are being even more diligent and rigorous in our fact checking," said Day.

Boise Dev and Valley Lookout have drawn a clear line: all content will be written by humans. That does not mean avoiding AI entirely. Day described using it to analyze years of Boise City Council agendas for data that would have taken a human several days to compile, a task completed in a fraction of the time. The distinction the outlet has landed on is using AI as a research and efficiency tool while keeping the editorial voice firmly human.

Day also made a point that resonated throughout the room: as deepfakes and AI generated content become more convincing, in-person gatherings carry more weight, not less. When misinformation can be manufactured at scale, showing up in the same room as someone becomes its own form of credibility.

Integration Beats Experimentation Every Time
Cameron Schaefer offered what may have been the most operationally useful framework of the panel. When asked how AI will separate high-performing companies from the rest over the next three to five years, he pointed to three things: integration over experimentation, governance and trust, and AI fluency across the entire organization.

On integration, his argument was straightforward. Experimenting with AI is easy. Building it into business processes, service models, and new lines of work is where the real differentiation happens. And that starts with identifying problems first rather than chasing the technology.

"Don't say, AI can do this, let me find a problem to solve," Schaefer said. "Identify the problem first."

On governance, he pushed past the idea of simply having an AI policy. What matters is whether the organization has a clear vision for where it wants AI to take it, guardrails that enable safe experimentation, and a culture where people feel encouraged rather than intimidated to try new tools. One of HDR's standing policies: employees are personally responsible for every word of AI generated output, whether they wrote it or not.

On fluency, Schaefer made a point about scale. Individual contributors using AI to multiply their output is not the same as an organization multiplying its output. Pockets of AI champions can actually create misalignment if the rest of the team is not brought along. He also raised mentorship as a structural opportunity, pairing younger staff who are fluent in AI tools with senior staff who bring years of technical depth and client relationships, with each group learning from the other.

He added one more dimension: talent attraction. Increasingly, top candidates are asking not just about salary and benefits, but about what technology access they will have, how much compute, how many agents, what the organization's AI strategy looks like. That conversation is already happening, and it will only become more common.

Higher Education Is Preparing Students for a Workforce That Is Still Taking Shape
Gordon Jones spoke to the unique pressure facing institutions like CWI, which must prepare students for jobs that are changing faster than curriculum cycles typically allow. His approach is grounded in two commitments: ensuring every student develops digital competencies, and making sure CWI itself is leaning into AI as an organization, not just teaching about it from the outside.

He described technical advisory committees and ongoing employer relationships as the primary mechanism for staying connected to what the workforce actually needs. For students who want to go deeper, CWI offers dedicated programs in AI and cloud computing. But the baseline expectation is that every student, regardless of field, will leave with the digital literacy to engage with these tools.

Jones also raised one of the panel's more sobering observations: the entry level job is disappearing. The tasks that have traditionally given new graduates their first real exposure to a workplace, discounted cash flows, research synthesis, basic analysis, are increasingly automatable. His concern is not just about those jobs vanishing, but about what replaces the learning that used to happen inside them.

His proposed response: colleges becoming employers in their own right, building in structured work experience so students arrive at their first job already having done something. He pointed to institutions now requiring a percentage of contract positions to go to students as one early version of this model.

Where AI Is Actually Working Right NowThe panel closed with a round of personal examples, and the range was instructive. Schaefer described building a custom meal planning agent that factors in his family's schedules, dietary preferences, and preferred grocery stores, generating a full weekly meal plan and shopping list automatically. Jones talked about using AI to quickly prepare for public forums, generating anticipated questions and consistent messaging around complex community issues. Day described automating ad trafficking at Boise Dev, a laborious manual process that previously required a dedicated staff member.

Hupp noted a counterexample worth remembering: she and her husband used AI to compare insurance policies and received a confident answer that was ultimately wrong because the tool lacked the full context of their situation. It was a useful illustration of a theme the panel returned to repeatedly. AI will give you an answer no matter what. It is the human responsibility to check whether that answer is right.

Day put it plainly: AI is wrong a lot, and you notice it being wrong more the more you know about the subject. Building in fact checking, asking the tool to say it does not know when it does not, and treating every output as a draft rather than a final product are the habits that separate effective users from ones who will eventually get burned.

The Parting Advice Was Simple: Start Now
​When asked for closing thoughts, the panel landed in a consistent place. Jones encouraged using AI with your kids, getting them familiar early while staying engaged with them in the process. Schaefer noted that AI will not criticize you, making it a uniquely low stakes environment to ask basic questions and learn. Cameron offered the framing that has stuck with many people thinking about this moment: the best time to start was a year ago. The second best time is today.

The panel was not a celebration of AI, nor a warning against it. It was something more useful: a practical, honest conversation among people who are navigating the same questions as everyone in the room, and finding out, one use case at a time, what actually works.
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Don Day, Gordon Jones, and Cameron Schaefer delivered these remarks at the Boise Metro Chamber's 2026 Regional Leadership Conference on April 20, 2026. This blog post was prepared from a partial transcript using the help of AI.