Insurers lead AI adoption by putting people first

Why the insurance industry is leading AI adoption

Insurance has always been a data business. From actuarial tables to advanced risk models, the industry measures uncertainty and prices it with discipline. That DNA explains why 99% of organizations report they are integrating AI across operations, and 71% of employees say they are embracing it. The signal behind the numbers: a people-first culture that puts professionals and policyholders at the center.

Bridging workforce readiness gaps

Adoption is high, but effectiveness still lags. Seven in ten leaders believe their workforces need to use AI more effectively, and many are pushing for targeted upskilling alongside modernization of legacy systems to support AI at scale.

Agentic AI frameworks are gaining traction. Think of orchestrated, goal-driven agents that work within guardrails, escalate when confidence is low, and keep humans in control. The aim is adaptability without losing accountability.

Applying AI today

Usage-based auto insurance is a clear example. Opt-in driving data from smart devices feeds scoring models so safer drivers can earn lower premiums. Insurers get sharper risk assessment and pricing. Customers get transparency, fairer rates, and incentives that reward good behavior.

Beyond telematics, carriers are applying AI to core workflows that move the loss ratio and customer experience:

• Underwriting: third-party data enrichment, faster submissions, and risk flags before bind.
• Claims: triage, fraud signals, document ingestion, and straight-through processing for low severity.
• Service: conversational support with handoffs to licensed agents when needed.
• Operations: forecasting, scheduling, and producer enablement.

If you want a quick primer on usage-based insurance mechanics, see the NAIC overview here.

Looking ahead

AI is not just about automation or cost. The bigger opportunity is to question assumptions and rebuild processes around outcomes. That could include rethinking claims workflows, reinventing risk models, and developing products tuned to new exposures.

Climate-driven events are a pressing case. As fires, floods, and storms intensify, carriers need more granular views of location risk, mitigation, and pricing. AI can help develop nuanced, data-informed approaches that protect balance sheets while keeping coverage accessible.

Putting people at the center

AI that helps people starts with reliable, unbiased data. It continues with continuous learning, domain literacy, and ethical governance. Without both, even advanced models will miss the mark.

A practical playbook for insurance leaders

• Define outcomes first: loss ratio improvement, expense ratio reduction, cycle-time cuts, or retention gains.
• Fix data quality at the source: lineage, timeliness, bias detection, and clear ownership.
• Modernize the core: APIs around legacy systems, event streaming for real-time signals, and a secure feature store.
• Adopt an AI operating model: roles, RACI, validation standards, and model risk management with audit trails.
• Pilot agentic workflows in underwriting and claims FNOL where feedback loops are tight and impact is measurable.
• Keep humans in the loop: confidence thresholds, intervention points, and transparent explanations.
• Measure what matters: combined ratio drivers, claim cycle time, leakage, first-contact resolution, and NPS.
• Upskill by job family: underwriters, actuaries, claims, and distribution each need targeted training. Explore curated options here.
• Protect data: encryption, access controls by role, and clear handling of PII/PHI.
• Plan vendor strategy: open standards, portability, and an exit path to avoid lock-in.

The bottom line

Insurance is built for disciplined risk decisions, which is why the sector is ahead on AI. Pair broad adoption with focused upskilling, modernized platforms, and human-centered governance, and you get safer decisions, better service, and a stronger promise to policyholders.