e360

“The impact of healthcare IT goes far beyond technology.”
— Jim Davis, Vice President & Chief Technology Officer, PeaceHealth

Technology in healthcare is never just about technology. It's about the people and outcomes it supports.

Jim Davis shares how working in healthcare IT transformed his perspective, from supporting critical care environments to enabling care across entire regions.

When every system, application, and infrastructure decision can impact patient care, success isn't measured by the technology itself. It's measured by the outcomes it helps deliver.

The most effective technology partnerships don't start with solutions. They start with understanding the challenges and working together to solve them.

“The singularity is here now.”
— Jim Grubb, Chief Technology Evangelist, Cisco

For years, AI singularity has been framed as a future event. Jim Grubb argues it may already be here.

In this video, Jim shares a personal example of how AI has expanded his ability to communicate ideas. While the insights were always there, AI helped bridge the gap between expertise and ex*****on.

The technology didn't replace his thinking. It amplified it.

The most valuable AI use cases aren't about replacing people. They're about combining human creativity, judgment, and experience with machine efficiency to achieve outcomes neither could deliver alone.

The future of AI may not be human versus machine. It may be human and machine.

“When you love what you do, you love the people that you do it with, and you love the people you do it for, great things happen.”
Mike Strohl, Chief Executive Officer, e360.

AI is creating opportunities that once felt impossible.

In this video, Mike shares why the conversations happening today around AI are bigger than technology alone. They are conversations about advancing healthcare, accelerating scientific discovery, and helping organizations solve problems with real human impact.

The technology matters. But the people behind it matter more.

Progress happens when talented teams are aligned around a mission, committed to innovation, and focused on creating meaningful outcomes for the organizations and industries they serve.

This is what makes AI transformative. Not just the capability itself, but the ability to apply it with purpose.

The future will belong to organizations that combine innovation with people who genuinely care about the impact they create.

“We have to overcome recency bias, and we have to realize that it's holding us back.”
— Ted Harrington, Ethical Hacking Expert and Bestselling Author

One of the most dangerous biases in security is recency bias: the tendency to believe that recent outcomes reflect reality.

If nothing has gone wrong lately, it's easy to assume everything is working as intended. But in cybersecurity, the absence of a visible problem is not proof of protection.

Ted Harrington compares this mindset to avoiding routine health checkups. Ignoring potential issues doesn't make them disappear. It simply reduces the opportunity to address them before they become more serious.

The same principle applies to security.

Strong security programs aren't built through reactive investments after an incident occurs. They're built through consistent, proactive actions taken over time.

The organizations that are most resilient aren't waiting for a breach to validate their strategy. They're continuously strengthening it before one occurs.

“Building clinical trust with AI starts with governance.”
— Jim Davis, Vice President & Chief Technology Officer, PeaceHealth

AI adoption in healthcare isn't just a technology challenge. It's a trust challenge.

Jim Davis emphasizes that before clinicians can rely on AI-driven insights, they need confidence in how those models were trained, validated, and governed.
That trust isn't built overnight.

It starts with transparency, clear communication, and applying AI in lower-risk scenarios before expanding into more critical areas of care. As confidence grows, so does the opportunity to leverage AI for more advanced use cases, from clinical workflows to diagnostic support.

The future of AI in healthcare will depend on more than model performance. It will depend on governance, accountability, and the trust organizations build along the way.

Technology adoption happens faster when trust is built first.