Cutting Through The Hype: Webinar Recap

If your inbox has been flooded with AI pitches lately, you’re not alone. LLMs. Agentic AI. Self-improving models. The buzzwords are flying, and it’s getting harder to separate what’s real from what’s just good marketing.

Earlier this week, Clare Maher, Authenticx’s Director of Product Marketing, led a webinar that the healthcare industry honestly needed: “Cutting Through the Hype: How to Choose AI That’s Accurate, Trusted, and Actually Works.”

If you missed it, here are a few key takeaways that attendees walked away with.

Start With the Problem, Not the Technology

Most AI projects fail because organizations begin with an AI solution and try to work backward. We broke down why that approach caps your value from the start. Getting specific about the problem you're actually solving changes everything

The 98% Problem

Most healthcare organizations manually review about 1% of patient and member interactions. The signals hiding in the other ~98% of calls, chats, and conversations? Going unnoticed. Pairing unstructured data with the right AI turns those blind spots into leading indicators and reactive triage into proactive strategy.

The Highest Use of AI Isn’t Automation — It’s Intelligence

The organizations getting the most from AI aren’t using it to simply automate tasks and call it a day. They're using it to analyze and surface critical information at scale, information that then powers decisions, workflows, and responses across the entire business. That’s where the compounding value lives, and it’s a fundamentally different way to think about what AI is for.

Accuracy Isn’t Optional

For AI to deliver on that kind of strategic integration, it has to be accurate — full stop. An AI you can only partially trust is one you can only partially use. We talked through what accuracy really means in practice: what to ask vendors, why out-of-the-box performance on your own data is the only benchmark that matters, and how to evaluate a vendor’s process for maintaining and improving accuracy over time. Always push for a proof of concept.

How to Think About ROI

AI ROI isn't one size fits all. We covered both the hard metrics leaders should expect to move in year one and the compounding value that builds quietly over time, like the problem you caught before it became a crisis.

The full recording of our latest webinar is available now. Watch it here.