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Scaling Expertise in a Constrained Claims Workforce

Industry experts discuss the evolution of claims management as organizations leverage artificial intelligence to support adjusters, not replace them, while improving outcomes for all stakeholders.
By: | April 22, 2026

The conversation around AI in workers’ compensation has covered familiar ground: staffing shortages, rising caseloads, and pressure on adjusters. These challenges are well understood. What’s shifting is how organizations are beginning to act on them, moving from awareness to deliberate approaches for applying expertise at scale.

Satish Narayanan, Chief Data & Information Officer at MedRisk, frames that shift through three pillars of responsible AI in claims management: codification of expertise, identification of critical decision points, and AI-driven empathy. Together, these pillars reflect how AI can support, not replace, claims professionals in ways that improve consistency and outcomes across the claims lifecycle.

Pillar 1: Codifying expertise in a stretched workforce

Satish Narayanan, Chief Data & Information Officer, MedRisk

A central challenge behind staffing constraints is the concentration of experience. Claims organizations rely heavily on professionals with decades of institutional knowledge, yet that expertise is inherently difficult to replicate or scale. “There are folks who have been doing this for 20, 30 years,” Narayanan said. “There is a huge amount of human expertise involved. How do we codify that in our models?”

Much of that expertise is also absorbed by administrative work. Viviane Ruiz, President of WC, Casualty & Managed Care at Davies Group, described the goal as redirecting capacity: “The way we’re looking at AI and how it influences the staff shortage is how do we get it to assist with administrative tasks so that the skill set workforce that we have can focus on their skill-focused work.” Applying that expertise through AI supported tools, while reducing routine administrative work, helps organizations reinforce consistency and best practices across claims without overextending their teams.

Pillar 2: Focusing limited resources on critical decisions

Viviane Ruiz, President of WC, Casualty & Managed Care, Davies Group

As adjusters take on larger caseloads, the allocation of their time becomes increasingly consequential. Not every step in the claims process requires the same level of expertise, but certain decisions carry disproportionate weight in determining outcomes. Narayanan is clear that AI’s role here is to inform, not act. “The model recommends the next best action, but doesn’t take the next best action,” he said. “That human in the loop to make the decision is key.”

In practice, that means AI handles pattern recognition and data synthesis in the background while surfacing what matters most to the adjuster. “What we’ve been looking at is how does AI come in and assist with identifying patterns on the claim or appointments,” Ruiz said. “It gives the adjuster relevant information in a usable way, so they can stay focused on the judgment driven, human side of the work.” The result shifts AI from decision-maker to decision support, helping adjusters focus their expertise where it has the greatest impact.

Pillar 3: Preserving empathy at scale

Workload pressure has real implications for claimant experience. Time constraints, not lack of intent, can limit the consistency and quality of interactions with injured workers, and Narayanan’s third pillar addresses that gap directly. “When AI relieves the experts who are servicing claimants from the tediousness of reading a 200-page medical bill or a 500-page document, they display more claimant empathy as well,” he said. “If it’s codified right… with the nuanced data points, then the model is codified with claimant empathy.”

By reducing cognitive and administrative burden, AI creates more capacity for adjusters to engage thoughtfully with claimants, while incorporating claimant-centered data into models supports more consistent, informed decision-making across high-volume environments.

The bottom line

Staffing constraints continue to shape the day-to-day reality of claims organizations, but they are also accelerating a shift in how expertise is applied. “It’s not a replacement, it’s not disruptive,” Ruiz said. “It’s a supportive tool that helps align treatment, the injured worker, the injury, and the claim while keeping people in control of decisions.”

AI, implemented with clear boundaries and human oversight, is emerging as a tool for scaling expertise, improving consistency, and enabling adjusters to focus on the moments that matter most. For organizations navigating workforce pressures, the opportunity is not simply to do more with less, but to apply expertise more deliberately across the lifecycle of a claim.

To learn more, visit: https://www.medrisknet.com/.

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This article was produced by the R&I Brand Studio, a unit of the advertising department of Risk & Insurance, in collaboration with MedRisk. The editorial staff of Risk & Insurance had no role in its preparation.

MedRisk is the leader in physical rehabilitation for the workers’ compensation industry. Our clinically based program ensures evidence-based care, reduces costs, and promotes return to work.

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