AI has become one of the most overused and misunderstood terms in healthcare. From automation and machine learning to large language models and agents, the line between promise and reality has become increasingly blurred.
In this Office Hours session, Natalia Arzeno-Gonzalez, Chief Data Scientist at Infinx, helps healthcare leaders cut through the noise by setting realistic expectations for what AI can and cannot do in revenue cycle management today. The discussion explores why AI is not a plug-and-play solution, how data quality and workflow maturity influence outcomes, and why many initiatives fall short even with significant effort and investment.
This session is designed for organizations that want to adopt AI responsibly, avoid costly missteps, and focus on practical, achievable progress rather than chasing hype.
Learning Objectives
By the end of this session, attendees will be able to:
- Differentiate between AI, machine learning, automation, and large language models in practical terms
- Understand the limitations of AI in fragmented and inconsistent healthcare data environments
- Set realistic expectations for timelines, outcomes, and performance improvement
- Align AI adoption with operational readiness rather than marketing promises
Request a demo of our revenue solutions to learn more.