Modern vehicles are increasingly equipped with advanced technologies, including AI-powered sensors, image-based diagnostics, and automated damage detection, which enable faster and more accurate claims assessments.
Recent research from Helwan University, Cairo, presents an academic Cost Estimation System that combines computer vision models with structured customer data to assess vehicle damage severity and estimate repair costs, illustrating the potential of these advanced technologies to accelerate claims processing and mitigate fraud risk.
At the same time, the research highlights ongoing challenges in integrating heterogeneous data sources, standardizing damage classifications, and operationalizing AI outputs within complex insurance workflows.
According to CCC Intelligent Solutions Holdings Inc.’s 2025 Form 10-K filed with the SEC, automotive claims in the U.S. now account for approximately two billion cumulative days of claims cycle time annually, reflecting the growing operational complexity facing insurers and underscoring how rising complexity continues to strain claims operations even as insurers invest in AI-enabled workflows.
Complementary empirical research from the Department of Artificial Intelligence, Sungkyunkwan University, Seoul, Korea, demonstrates that despite advancements in automation for vehicle damage classification, the field continues to face challenges related to fragmented datasets, uneven classification performance across damage types and vehicle parts, and the difficulty of capturing diverse real-world lighting and environmental conditions.
Even with state-of-the-art neural networks and transfer learning, public datasets for car damage analysis remain scarce, hindering industry-wide adoption of automated claims processing. A systematic literature review from RMIT University, Australia, finds that most publicly available datasets are small and imbalanced, typically fewer than 15,000 images, and offer limited coverage of real-world variability, including diverse lighting conditions, camera angles, and overlapping or multi-region damage scenarios.
On a recent episode of the ‘AI in Business’ podcast, Daniel Faggella, Founder and Head of Research at Emerj Artificial Intelligence Research, sat down with Marc Fredman, Chief Strategy Officer at CCC Intelligent Solutions, to discuss how rising complexity, labor shortages, and fragmented workflows are reshaping the future of auto insurance and claims.
Their conversation highlights two critical insights for insurers navigating complexity and talent challenges that will be analyzed in this article:
- Improving ecosystem-wide transformation: Integrating models, data, and networks into a single orchestration layer to move insurers toward true end-to-end automation across the insurance economy.
- Improving talent shortage across the claims and repair ecosystem: Integrating AI into high-skill roles to bridge the growing talent gap, support retiring experts, and enable fewer workers to manage rising claims complexity.
Listen to the full episode below:
Guest: Marc Fredman, Chief Strategy Officer, CCC Intelligent Solutions
Expertise: Value Creation, Insuretech, Artificial Intelligence
Brief Recognition: Marc has been with CCC Intelligent Solutions for 12 years, joining in 2014 as SVP of Corporate Strategy and Development. In his current role, he leads both organic and inorganic strategy, having previously overseen capital allocation. During his tenure, he helped scale the business to $1B in ARR, tripling its revenue. Fredman holds a Master’s Degree in Finance and Marketing from Northwestern University.
Improving Ecosystem-wide Transformation
Marc opens the conversation by saying that claims teams succeed only when they balance three things at once:
- Accurate payouts
- Operational efficiency
- Strong customer experiences
Historically, these functions were siloed, which created coordination gaps, more complexity, strain on labor, and financial losses.
He argues insurers must treat every claim as a made-to-order supply chain that begins the moment the loss is reported — spanning repair shops, parts suppliers, manufacturers, medical providers, and internal teams.
The real transformation comes when insurers begin acting as orchestrators of this entire ecosystem, aligning all stakeholders to deliver accurate decisions, faster cycle times, and better customer outcomes.
Marc says insurers must recognize that they operate within a broader insurance economy composed of interconnected data, partners, and services. The real opportunity is not isolated AI use cases but orchestrating this entire ecosystem end-to-end.
Capitalizing on orchestration opportunities requires sophisticated AI, rich data, and a network that can coordinate decisions across all participants. Forward-thinking insurers are already shifting toward the vision Marc describes as a holistic, transformational orchestration model.
Marc further explains that AI will reshape every stage of the claims process, starting at the very first sign of loss. He describes claim handlers as “quarterbacks” who must coordinate information from consumers, tow truck drivers, repair shops, agents, and others — yet today they receive photos and updates from dozens of disconnected sources, making early decision-making difficult.
If insurers could gather all that information at the start and run it through multiple AI models, they could immediately determine whether the vehicle is a total loss or repairable, a critical but straightforward decision that shapes the entire routing of a claim. Early AI insights can also flag potential injuries and even identify subrogation opportunities long before they surface at the end of the claim, when evidence and witnesses often disappear.
He stresses that true transformation comes not just from individual AI models but from orchestrating when these models are used and how their outputs guide the next step.
To reinforce the urgency, he cites a striking statistic: U.S. auto claims now accumulate more than 2 billion total waiting days each year — double what they were just a few years ago — which he sees as proof that insurers are moving in the wrong direction on complexity.
According to Marc, the industry must move beyond isolated use cases and begin end-to-end AI-enabled routing, from first notice of loss to final payment and subrogation.
Improving Talent Shortage Across the Claims and Repair Ecosystem
Marc says the talent shortage in auto insurance isn’t limited to claims teams — it’s equally severe on the collision repair side, where most of the real cycle-time work happens. He notes that roughly 80% of auto accidents result in repairs. Yet, the industry is facing an acute shortage of skilled technicians at the very moment that repair work is becoming more complex due to improvements in EVs, ADAS systems, and advanced materials.
At the same time, many longtime claims adjusters, appraisers, subrogation specialists, and fraud investigators — professionals with 20 – 30 years of experience — are retiring, and fewer young people are entering the sector. He contends that this is a missed opportunity as both claims and repair roles offer strong career paths, but these haven’t been marketed well.
These roles also now require AI support; for example, collision technicians must navigate more than 5 million pages of repair procedures for tasks like welding or safely disconnecting an EV battery.
Marc says the industry must harness AI to make workers more productive and give them a clearer sense of career growth. Overall, he frames the issue not as an isolated gap, but as a broad, systemic labor challenge across the entire auto-claims ecosystem.
Marc concludes by saying that AI adoption in insurance is already substantial — his team has processed over 20 million claims with AI in production — but many large, sophisticated players still haven’t fully embraced it.
“From an innovation perspective, you need both on-ramps and off-ramps. The on-ramp is the mechanism to get innovation started — to identify a real use case and prove its value. The off-ramp is just as critical because it ensures that innovation actually makes its way into production. Too often, especially in insurance, companies experiment, and then the work sits on a shelf.
The real question becomes: what platform will help you orchestrate AI deployment alongside human expertise, in a way that consistently delivers what matters in claims — accurate outcomes, efficient internal operations, and a great customer experience.”
– Marc Fredman, Chief Strategy Officer at CCC Intelligent Solutions
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