Vive 2026 and HIMSS 2026 are popular healthcare IT conferences. For CIOs and decision-makers, networking is the main draw—hallway conversations often surpass keynotes in value. The key takeaway from this year is clear: AI is still central, but attention has shifted from hype to results. The industry now focuses on delivering measurable outcomes and operational improvements with AI. For CIOs, two main priorities emerge: integrating AI into core operations and ensuring effective AI governance.
AI As Core Infrastructure
To realize AI’s benefits, healthcare organizations must move beyond treating it as a side pilot project. They are embedding AI into workflows and focusing on where it fits within operations, its integration with existing systems, and its scalability without increasing risk.
This integrated approach positions healthcare CIOs at the intersection of hospital operations, where they deploy technology solutions to strengthen both operational and clinical productivity, automate tasks, and reduce workforce burnout. For example, ambient documentation tools reduce physician burden while feeding structured data into records. Revenue cycle teams use AI to automate coding, prior authorization, and denial management. Clinicians use AI-driven decision support to surface risk, close care gaps, and standardize best practices. In each scenario, AI becomes more deeply embedded into workflows—sometimes natively in core EMR systems.
Building on operational improvements, healthcare vendors are also developing AI agents and solutions to enhance the patient experience. For example, some health systems are embedding AI agents in patient portals to answer billing questions, guide pre-visit preparation, schedule appointments, and provide basic triage. Others deploy AI agents for doctors to respond to patient messages, freeing staff from repetitive, low-level administrative tasks.
These integrations raise a crucial question for CIOs and healthcare operation leaders: where does AI truly belong in the workflow? If AI sits outside the EMR or requires users to toggle between systems, adoption drops. If it interrupts rather than supports decision-making, it creates friction. But when AI operates as part of the core infrastructure, tightly integrated and nearly invisible to the end user, it can simultaneously improve productivity and enhance patient experience.
AI Governance Is Getting Messy
As organizations embed AI more deeply, governance is becoming more complex. State regulations on AI use now vary. For example, Texas Chapter 183—Electronic Health Records (SB 1188)—is one of the first state laws to explicitly regulate clinical AI in the EHR. This law requires that if AI is used to generate clinical information, a licensed clinician must review and validate the information before it is included in the patient’s medical record.
When AI supports clinical documentation or decision-making, a qualified clinician must remain involved. AI-generated clinical content should never automatically populate the medical record; it must be reviewed and approved by a licensed clinician before being entered.
Texas leads in adopting AI restrictions, and it is not alone. Other states are also advancing distinct approaches to AI oversight:
- Illinois restricts AI use in certain clinical contexts, especially in mental health, limiting unsupervised decision-making.
- California has passed broader AI transparency and safety laws focused on risk reporting and disclosure.
- Utah requires disclosure of AI use and emphasizes consumer protection.
- Nevada limits certain AI therapy interactions that do not involve a provider.
- Colorado’s AI Act addresses high-risk AI systems, including healthcare, with a focus on anti-discrimination and governance standards.
State strategies diverge significantly. California and Colorado drive cross-sector frameworks. Illinois, Nevada, and Utah emphasize curbing AI misuse. Texas distinguishes itself by anchoring AI oversight in EHR workflows and clinician review, exemplifying the breadth of state responses.
At Vive 2026 and HIMSS 2026, the most important conversations will be about integrating AI deeply in the environment while governing it responsibly. CIOs are treating AI as core infrastructure, making governance tricky for organizations operating across multiple states with different regulations.
