The phrase gets used a lot: AI embedded in the workflow. It shows up in vendor pitches, conference keynotes, and industry reports. But what does it look like on a Tuesday morning in a claims operation? And how do you know if what you have qualifies?
The distinction matters more than it might seem. Workers’ comp insurers that have AI embedded in their workflows are getting fundamentally different results than those running AI as a separate tool that someone must remember to use. Understanding the difference is the first step to getting there.
Bolted On vs. Built In
AI that is bolted on looks like this: an adjuster finishes reviewing a claim, then opens a separate application to run it through an AI analysis tool, reviews the output, closes the tool, and goes back to the claims system. The AI might produce useful information, but it’s an extra step, it depends on the adjuster remembering to do it, and the output doesn’t automatically connect to anything else in the workflow.
AI that is built in looks different. The system surfaces the analysis as part of the normal claims view. No separate application, no extra step. When a new claim comes in, it’s automatically classified, triaged, and routed based on risk factors the AI identifies from the documentation. When a medical report is uploaded, relevant codes and findings are extracted without anyone doing it manually. When a claim pattern suggests litigation risk, the adjuster sees a flag, not a buried report they have to go looking for.
The technology might be identical in both scenarios. The difference is whether the workflow was designed around it.
What This Looks Like Across the Claims Lifecycle
At intake: AI reads the first notice of loss, extracts key information, identifies missing fields, flags potential fraud indicators in the narrative, and routes the claim to the right adjuster before a human has touched it. What used to take 45 minutes of initial review gets compressed to a starting point the adjuster can validate in five.
During triage: Predictive models score incoming claims for severity, litigation risk, and recovery complexity. High-risk claims get flagged immediately and assigned to experienced adjusters. Straightforward claims move toward faster resolution. The system is deciding where human attention is most needed, not just processing transactions.
Through medical review: NLP-powered tools read medical documentation and extract diagnosis codes, treatment patterns, and return-to-work indicators without manual data entry. Bill review automation validates charges against fee schedules in real time. The adjuster sees what matters, not a 300-page file.
In return-to-work communication: Virtual assistants handle routine status updates and injured worker inquiries around the clock, so adjusters aren’t pulled away from complex cases to answer questions that a system can answer accurately. Communication keeps moving. Relationships stay warm. Return-to-work timelines improve.
How to Know Where You Actually Are
A useful benchmark: how many steps does it take for your adjuster to access AI-generated information about a claim they’re actively working? If the answer is more than one (if they must navigate to a different system, run a separate query, or export data to get the analysis) the AI is bolted on, not built in.
Another question: does your AI output automatically trigger anything, or does it just produce a report? Embedded AI changes what happens next in the workflow. Standalone AI produces information that someone may or may not act on.
The organizations that are getting the most out of their technology investments aren’t necessarily the ones with the most advanced AI, rather they’re the ones who tackled the harder work of redesigning their operations around it. That’s the real competitive advantage in workers’ comp right now, and it’s more available than most executives realize.
Curious how True integrates AI directly into policy and claims workflows? Senior Solutions Advisor Ryan Smith can walk you through it.
Sources
- Risk & Insurance: Breaking Down Data Silos — How Integrated Analytics Transform Workers’ Compensation Outcomes
- Sedgwick: Natural Language Processing and the Digital Claims Transformation
- True Insurtech: Harnessing the Power of Big Data to Fuel Growth in Workers’ Comp
- McKinsey & Company: Charting a Path to the Data- and AI-Driven Enterprise of 2030
