Having participated in Insurance Innovators, it became clear that the industry is entering a pivotal moment in the evolution of claims. The discussions, case studies, and demonstrations reinforced a shared recognition. Artificial intelligence is no longer a distant promise but a practical capability ready to be embedded into claims operations.
The event highlighted both the progress already made and the urgency to act. With carriers and partners acknowledging that the barriers of siloed data and fragmented workflows can now be overcome. This article reflects on those insights and explores how AI is reshaping claims. Not as a theoretical exercise, but as an operational reality that is beginning to deliver measurable improvements in accuracy, efficiency, and customer outcomes.
Breaking Down Data Silos: Why Legacy Claims Workflows Erode Trust and Inflate Costs
Historically, claims functions have been held back by data locked in silos and brittle sequential workflows residing in core systems. Each party in the value chain has not exposed their process or data to the other, resulting in multi-touch, multi-email exchanges to establish and fix data and context issues. These challenges have existed for years and eroded trust and impacted both expense and claim loss ratios.
Technology combined with improved systems of engagement can now put an end to that prehistoric way of working without necessarily changing the underlying technology. Just supercharging it.
Those in the value chain who share data and extend their workflows into others will win by reacting faster and reducing cycle times. This approach delivers a vastly improved customer and employee experience while lowering operating costs.
AI in Claims: From Manual Bottlenecks to Real-Time Decisioning and Reduced Indemnity
The claims function has been slowed by manual data entry, fragmented systems, and the endless circulation of emails, PDF’s and excel files. But we’ve reached a turning point. AI is finally mature enough, and our data is clean enough, to remove friction at scale. What’s changing is not the technology itself but our ability to operationalise it. AI is already transforming intake, assessment, investigation, and settlement. From real-time triage routing at FNOL to claim value estimating and copilots that remove the administrative burden from adjusters and provide support for the Next Best Action, the early wins are clear. Accuracy improves, decisions are faster, and adjusters gain freedom to focus on empathy, negotiation, and training junior colleagues. This also enables more broker and underwriter interactions, positioning claims as the product—where technical claims handlers can truly add value.
What’s emerging now is an industry shift from reporting hindsight to intervening in real-time. Claims analytics is no longer about MI packs and rear-view dashboards. It’s about predicting escalation, spotting vulnerability, identifying fraud clusters early. As well as matching each claim to the right handler quickly. Crucially, the tools only work when embedded inside workflows. Otherwise, we’re just making more expensive PowerPoints or fancy excel. People trust what they help shape, so involving adjusters early, positioning AI as a copilot rather than a critic or replacement and proving value through small wins are what shift behaviour. When teams feel augmented rather than audited, they lean in. When they feel replaced, they resist.
Evolving Claims Skills in the AI Era: Building Scalable Ecosystems and Proactive Decisioning for the Future
As claims becomes a more AI driven profession, the skills profile is changing too. Today’s handler is an investigator, negotiator, teacher, and claim workflow expert who must interpret claims outputs. They also need to know when automation should be overridden and manual intervention is best to resolve nuanced cases. And at the operational level, the carriers who will win the next era are the ones who build scalable models, integrated ecosystems, and proactive decisioning loops and continuous improvement built in. Especially during large-scale events, where preparedness and rapid, remote assessment matter most.
The message for the market is simple: don’t wait for the perfect transformation strategy. There are a lot of choices to make that impact the claims business—FNOL, triage, repair allocation, reserve accuracy, legal propensity, fraud, or leakage—prove it, learn from it, and scale.
The future belongs to those who connect people, process, data, and AI into a single, living claims ecosystem that reduce time, lower costs, improve quality and can be demonstrated using good data.
