Data fragmentation blocks commercial insurers from capturing AI's value
Commercial insurers cannot unlock artificial intelligence's potential without first consolidating their data across multiple channels and systems. Cameron Scott, VP of Sales and Marketing at ISI, said the foundation for AI success starts with a unified data strategy, not with the technology itself.
"The AI can only be as good as the data behind it," Scott said in an interview with Insurance Business TV.
The fragmentation problem
Insurers today receive customer interactions through brokers, direct channels, and managing general agents. Data arrives through uploads, accounting postings, and multiple legacy systems operating in isolation. This fragmentation creates operational friction and blocks the insights AI needs to work.
Manual workarounds compound the problem. Teams re-key data, duplicate effort, and lack real-time visibility into risk accumulation across their book of business. Leaders cannot respond quickly to market shifts when data sits trapped in separate silos.
The strategic risk extends beyond efficiency. Without consolidated data, insurers miss opportunities to apply AI at scale. They also face higher compliance and underwriting risks when fragmented data prevents rapid assessment of exposures.
Building the foundation
Scott recommended two approaches to unify data. Insurers can deploy a data lake or data warehouse to consolidate existing systems. Alternatively, they can migrate data into a single platform with consistent data structures. The latter approach has proven more effective for real-time underwriting and risk evaluation.
A unified data foundation supporting similar structures delivers measurable benefits across the policy lifecycle. AI can triage and prioritize submissions, surface early risk indicators, and flag missing information. It can also automate routine workflows and generate insights across the full portfolio.
Strong alignment between business and IT teams is essential. Modernization projects will disrupt operations temporarily, but the efficiency gains justify the transition.
Where AI delivers today, and where it doesn't
Insurers are deploying AI with human oversight built in. The technology accelerates underwriting by enabling teams to move faster with more confidence. AI surfaces missing information rapidly and highlights risks early in the submission process.
Fully automated underwriting decisions remain largely unrealized. Few insurers have deployed AI systems that bind and book risk without human review. Scott said industry policies require human oversight for any AI decision, and that approach will likely persist.
Meeting evolving customer expectations
Brokers and policyholders are demanding four things from insurers: speed, transparency, self-service capabilities, and consistency across channels.
Insureds expect faster quotes and faster decisions. They want visibility into underwriting status and real-time communication throughout the policy lifecycle. Self-service capabilities allow them to access information independently. Consistency means the same experience whether they work through brokers, direct channels, or MGAs.
Insurers that consolidate their data and deploy AI strategically can meet these demands. Those operating on fragmented legacy systems cannot.
For more information on data consolidation and AI in insurance, visit AI for Insurance and AI Data Analysis.
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