Healthcare IT leaders face a transformation that goes beyond technical upgrades, demanding a cultural shift in how their work is perceived. For years, they operated behind the scenes, ensuring systems functioned smoothly, data remained secure, and hospitals avoided disruptions. Today, they must also make their contributions feel engaging and relevant.
At the 2026 CDW Summit in Dallas, two IT executives highlighted this tension between tradition and the new demands of AI-driven change. Carollo Engineers CIO Andrew Ivey and St. Luke’s University Health Network Associate CISO Krista Arndt discussed the matter in an informal, conversational style that differed from past formal panel discussions. Seated in front of a setup resembling a YouTube interview, Ivey framed the challenge bluntly: “I didn’t know that we were the fun people, man. Like, I was supposed to do one thing, now I have to worry about if it’s fun or not?”
Arndt said her technology organization has a phenomenal relationship with hospital leadership, and they talk constantly. She added that it’s something they focus on a lot in security: speaking the language of people that they serve so they really understand what’s going on. Those conversations, which might seem more casual than they were in the past, are extremely valuable in Arndt’s pursuit of getting her non-IT counterparts to receive her concerns about risk management. “They know there’s a lot of unknowns, but they’re really excited to investigate those reasonably and from a risk-balance basis,” she said.
From Theory to Practical Implementation
CDW’s leadership at the summit made clear that the industry has moved past debating whether AI will transform healthcare. The focus now lies in determining how to implement it effectively. Liz Connelly, CDW’s chief commercial officer, opened the event by rejecting the usual AI enthusiasm. She said, “Because the question is no longer whether AI will change business. The question is how organizations can use AI to deliver real and measurable business ambition outcomes.”
To support this claim, CDW introduced Hang Tan, its newly appointed chief strategy and transformation officer. Tan outlined six AI priorities that the company applies to its own operations, treating itself as “Customer Zero” to validate solutions before offering them to clients. The framework cuts through speculation:
- AI-ready infrastructure: Preparing systems across cloud environments, data centers, networks, and endpoints for AI agents requiring frequent updates.
- Data readiness: Ensuring secure access to relevant business data while managing AI-generated outputs.
- Platform integration: Deciding where to embed AI within existing software and where to develop new capabilities.
- Agent-based systems: Transitioning from isolated AI tools to shared, scalable productivity platforms.
- Enhanced security: Extending identity and access controls to include AI agents alongside users and devices.
- Cost accountability: Measuring AI’s impact on processes to demonstrate concrete value beyond initial expenditures.
Even the summit’s entertainment reinforced this practicality. Mid-session, Mike Grisamore, CDW’s senior vice president of vertical markets, introduced CH3T, a humanoid robot the company had unveiled six months earlier at HIMSS. Dressed in scrubs and a Team USA hat—a nod to CDW’s 2028 Olympics sponsorship—the robot’s stage appearance drew laughter. Grisamore teased, “CH3T, by the way, those are some sweet scrubs you got,” noting its initial pants-less debut had unsettled some nurses.
Yet the humor concealed a serious purpose. Grisamore positioned CH3T as a test case for “physical AI,” exploring how robotic assistants could enhance healthcare operations. The robot’s presence wasn’t mere spectacle; it signaled CDW’s belief that future healthcare IT will involve both software and human-machine interaction.
Risk Management and Collaboration
While CDW emphasizes outcomes, the human factor remains essential. At St. Luke’s University Health Network, her team uses simple risk-scoring systems, like traffic lights (green for low risk, red for critical), to explain technical vulnerabilities in plain terms to administrators. These discussions often revolve around trade-offs, such as balancing AI efficiency gains against workflow disruptions for clinicians. By framing risks as “known unknowns” rather than abstract threats, leadership can prioritize investments without overreacting to hype.
Ivey’s skepticism has led Carollo Engineers to adopt a “red team” strategy for AI projects. Before approving any initiative, his team simulates worst-case scenarios, such as AI misclassifying patient data or robotic assistants failing in critical situations, to test assumptions. This method has already exposed gaps in vendor claims, including an AI scheduling tool that overpromised accuracy in emergency triage. By integrating these checks early, Carollo avoids treating AI as a universal solution and instead views it as one component of broader digital transformation.
The difference between Arndt’s collaborative risk approach and Ivey’s structured skepticism shows how IT leaders are evolving without abandoning core responsibilities. Both methods share the same goal: ensuring AI delivers value while maintaining the stability healthcare IT has long provided. The challenge lies in bridging innovation and operational reliability, a defining issue in today’s healthcare technology sector.
The 2026 CDW Summit closed with a keynote from a pediatric oncologist who described how AI-assisted diagnostics had changed treatment planning in her department. Her remarks showed the summit’s central theme: AI’s impact isn’t just about efficiency; it’s about redefining what’s possible in patient care.
