The Five Failure Modes Holding Back AI Agents
Many AI agent projects stall in pilot mode. Learn five common failure points and how to design AI agents that scale, cut costs, and drive business results.
Martin Higgins, Centric Consulting’s insurance practice lead, wrote for Insurance Thought Leadership on why data latency costs insurers more than they realize.
The article, “Insurers Need Real-Time Data Capabilities,” takes on a problem most carriers recognize but haven’t fully solved. Collecting data isn’t the hard part; acting on it before it goes stale is.
“Fraud rings don’t operate on half-hour cycles; they execute in minutes,” Higgins said. He noted that batch-processing delays and siloed systems aren’t just operational friction; they’re a direct window of exposure.
He pointed to first notice of loss (FNOL) triage, claims severity scoring, and fraud assessment as the highest-stakes areas where latency translates into dollars lost rather than recovered.
Higgins argued that building real-time data capabilities doesn’t require replacing core systems. Change data capture, a real-time data layer, and AI-driven enrichment from external sources — weather data, fraud signals, claims history — can layer onto existing infrastructure.
The organizational will to start, he concluded, is what most insurers still lack.
