Episode Description
Guest Bio
- Gia Sawko | Chief Claims Officer at Signal Mutual
- Decades of claims experience in workers' compensation
- Instrumental in navigating how AI reshapes the adjuster's role
- Focused on preserving and amplifying human judgment in claims work
The Conversation
Yvonne, Rafael, and Gia get into what's happening in claims work right now. The job is harder than ever. The tools haven't caught up. And there's a path forward if we're willing to think differently about training, technology, and what we ask of the people doing this kind of work.
Key Discussion Points
The Claims Desk Hasn't Been Redesigned in 20+ Years
The claims adjuster's job has become exponentially more complex. Medical knowledge requirements have expanded. Regulatory demands keep multiplying, and they're harder than ever to navigate. Case management now demands synthesis across medical history, pharmacy data, functional capacity, and emerging compensability questions. But the actual desk, the systems, and the workflows look almost identical to what they did two decades ago. The infrastructure hasn't kept pace with our industry's complexity.
Why Don't We Train Claims Adjusters Like Apprentices?
Gia makes a good point: we should treat claims adjuster development like other industries treat apprenticeships, such as skilled trades. New adjusters need structured mentorship, graduated complexity, and time to build judgment alongside technical knowledge. Right now, they often get thrown into the deep end. If we reimagined onboarding the way apprenticeship programs do, with clear progression and accountability, we'd build stronger professionals. People who feel accomplished master their craft and stay in the industry.
AI as Support, Not Replacement
The highest-value use of AI in claims isn't automation; it's augmenting what adjusters already do. AI can flag high-risk claims and surface comorbidities. It can summarize documents. But the adjuster owns the relationship with the claimant and the critical judgment calls. The combination between the person and the algorithm is where we'll get the best outcomes.
Implementation Challenges Are Real
You can't pause operations to implement AI. Decision fatigue is a risk when adjusters get too many warnings. And AI is only as good as the question asked. We need to teach adjusters how to ask better questions and build trust through testing. This is the "how" nobody's really solved yet.
AI Literacy Is Non-Negotiable
Every adjuster needs to become proficient in AI language now. Start small, test on easy tasks, validate against what you already know. Those who build this proficiency will be positioned better than those who don't. The window is now.
Deucing Every Point
Gia is a serious tennis player! Ask her about a match where she lost 6-0, 6-0—and she'll tell you about the deuces, the rallies that mattered, the quality of every point. Lean in to hear what the court might teach us about adjuster mastery.
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