Actionable Intelligence: From Insight to Impact

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.

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