Stanford Medicine’s Justin Scord is urging technology teams to slow down on AI. His message is not anti-innovation. It is a call for stronger discipline, clearer data, and better planning.
Speaking at InfoComm 2026 in Las Vegas, Scord described a fast-moving workplace technology market. Managing collaboration across hospitals, clinics, classrooms, and event spaces brings constant pressure. New tools arrive quickly. Vendor claims can sound impressive. Yet large environments need systems that teams can support every day.
“There’s always so much change, so much technology, so many different manufacturers,” Scord told UC Today at InfoComm 2026 in Las Vegas. “Keeping that North Star and making sure we’re building systems that are scalable and supportable is really our focus.”
That warning matters for telecom and collaboration teams. Many now manage hybrid meeting rooms, video platforms, and intelligent endpoints. These systems increasingly touch unified communications, cloud calling, and enterprise networking. A poor technology choice can affect thousands of users.
Scord oversees collaboration and media services across Stanford Medicine’s Palo Alto ecosystem. His team supports patient rooms, conference spaces, classrooms, and live event broadcasts. If a room has a screen, microphone, or speaker, his team likely supports it.
However, Scord does not reject AI in workplace technology. He supports it when teams measure its value. “I’m all about the data,” he said.
Stanford’s internal data science team studies which AI models fit specific workflows. Scord believes the wider AV industry should ask similar questions. That approach helps buyers avoid tools built around marketing rather than real operational needs.
“Ask the manufacturers where they got the research and data to choose the models they’re using. That alone is going to weed out a lot of manufacturers.”
This point deserves attention from service providers and IT leaders. Many AI features now appear in meeting devices and collaboration platforms. They can summarize meetings, adjust cameras, reduce noise, and route tasks. These features can improve productivity when they match the workflow.
Yet the wrong model can create confusion. It may cost more than it saves. It may also introduce trust, privacy, or compliance concerns. In healthcare, those risks become even more serious.
Scord also discussed agentic AI, which can take action across workflows. He sees value when it supports teams with better insights. It should gather information, highlight patterns, and reduce routine work. It should not remove human responsibility from important decisions.
“Get educated around machine learning and AI at a high level,” he said. “Understand the limitations, and start slowly incorporating it into your workplace.”
For telecom and collaboration professionals, the message is clear. AI deserves serious evaluation, not blind adoption. Teams should ask vendors for evidence, not slogans. They should test real use cases before scaling deployments.
In complex environments, reliability still wins. AI can help modern workplaces operate better. But only when data, governance, and supportability lead the strategy.
