Cedars-Sinai's CDAIO on healthcare AI's second wave: workforce transformation, not just productivity

Ninety-seven percent of staff using AI tools at Cedars-Sinai report the technology has made an important difference in their work. That figure, cited by Mouneer Odeh, the Los Angeles health system's inaugural chief data and artificial intelligence officer, captures what he calls the first wave of healthcare AI: productivity gains, reduced friction, and measurable time savings. The harder work, he told Becker's Hospital Review, is the second wave now beginning to arrive.

Two waves, one window

The structural case for AI adoption in health systems is not subtle. An aging population is pushing demand upward while clinician supply cannot keep pace, and costs are outrunning margins across the sector. Odeh frames those pressures not as a slow-building crisis but as a narrow operational window.

"We have a very limited supply of doctors and nurses, and so it's really difficult to be able to meet that increasing demand," Odeh said on the Becker's Healthcare Podcast. "We're also seeing costs growing very, very rapidly. That's creating a tremendous amount of pressure on health systems across the country."

The first wave of Cedars-Sinai's AI response involved building the infrastructure capable of supporting that shift. Over roughly the past year and a half, the health system has invested in cloud infrastructure, constructed a HIPAA-compliant generative AI platform for internal use, and pushed data capabilities directly to clinicians, administrators, and operations teams, according to Becker's Hospital Review. The 97% adoption satisfaction rate reflects that groundwork paying off.

Workforce restructuring, not just acceleration

The second wave is a different kind of challenge. Odeh describes it as a restructuring of how work is organized and what individual jobs actually require, not simply a continuation of the productivity story. "It's changing the way we work and as a result, changing the jobs that we have," he said, per Becker's Hospital Review.

In clinical environments, that shift is already visible. Cedars-Sinai's investments in ambient AI and documentation tools are producing outcomes clinicians describe in personal terms: more present patient interactions and less cognitive overload. For operations and administrative leaders, those are not just wellness metrics. They correlate directly with retention, throughput, and care quality.

Outside clinical settings, a different dynamic is emerging. Non-technical staff are independently building AI-powered workflows and agents without relying on IT resources, a development Odeh describes as both an opportunity and a governance challenge. "We are seeing some power users outside of the IT function that are doing amazing things that just simply couldn't have been done without a technical background," he said, according to Becker's Hospital Review.

Democratization and its governance implications

The democratization of technical capability changes what health system IT and operations leaders need to manage. When non-IT staff can build agentic workflows, the question of oversight becomes more complex. Cedars-Sinai has so far kept its AI positioned primarily as a human-operated tool rather than an autonomous decision-maker, but Odeh is direct that this calibration will shift as the technology advances.

The broader social and ethical dimensions are not lost on him either. Odeh noted to Becker's Hospital Review that AI is already influencing policy discussions, energy infrastructure, and neighborhood-level economic dynamics. For health system leaders, that context matters when planning long-term workforce and technology strategies.

The pattern Cedars-Sinai is describing mirrors broader themes in global digital health development. Expanding access through technology, reducing structural bottlenecks, and connecting more communities to care are goals shared by health systems and ministries of health operating at very different scales and resource levels, reflecting a cross-sector convergence on AI as an operational necessity rather than an optional upgrade.

What this means for your team

• Audit current AI tool adoption rates and satisfaction across clinical and non-clinical staff. If your numbers don't approach Cedars-Sinai's 97% benchmark, identify where friction in the HIPAA-compliant tooling stack is blocking uptake.
• Establish a governance framework now for non-IT staff building agentic workflows. The democratization of AI capability is accelerating faster than most IT governance policies were written to handle.
• Review ambient AI and documentation tool deployments for measurable cognitive load and retention outcomes, not just speed metrics. Those outcomes are becoming central to clinician retention arguments.
• Evaluate your AI infrastructure for second-wave readiness: cloud architecture, data accessibility across roles, and policy for autonomous versus human-in-the-loop decision boundaries.