Insurtech car insurance provider Root said it has started a 24-hour agent appointment program to allow independent insurance agents to complete onboarding and begin selling policies in as little as one day.
Root Insurance said traditional insurance carrier onboarding often takes weeks, but its 24-hour agent appointment program compresses this process into a single-day, fully digital experience to give independent agents faster access to revenue opportunities.
Root said it has appointed more than 7,500 agents since launching. About 2,400 agents have been added this year. More than 15,000 independent agents are appointed with Root and 4,000 agencies are approved to sell Root policies, the insurer said.
Qualified agents can fast-track their access to Root by requesting an appointment online and completing the accelerated vetting process within 24 hours.
Root Inc. reported a fourth quarter 2025 net income of $5.3 million compared with net income of $22.1 million for the same period in 2024 but ended the year with record net income of $40.3 million.
Now operating in 36 states, Root said independent agents are its fastest growing segment.
The company announces first quarter 2026 results on May 6.
Rather than replacing the role brokers play in advising on, structuring, and placing complex risk, AI is strengthening it. The firms seeing the most impact are not using AI as a bolt-on productivity tool. They are embedding it into the operating layer of the business — across placement, servicing, and renewal.
Large language models are already delivering real value across insurance workflows. They can interpret unstructured submissions, extract relevant data, and accelerate how information moves through the placement process. In that sense, they are becoming a powerful tool in the broker’s toolkit.
Increasingly, firms are recognizing that general purpose models are only part of the picture. Insurance workflows depend on domain context — policy structures, market conventions, claims histories, and regulatory nuance — that sits outside the training of generic models.
This is driving a shift toward domain-specific AI, where models are applied within the operating context of insurance itself. For example, a general purpose model may summarise a slip, but a domain-specific model can flag a missing aggregation clause or inconsistent limit structure — issues that materially affect coverage quality
The difference is material: from generating plausible outputs to delivering results that can be relied on in live placement and servicing workflows.
But their role is distinct. Brokers operate on judgment, accountability, and outcome-driven advice. In complex commercial risks, structuring and placing coverage requires an understanding of client intent, market dynamics, and the nuances of coverage that extend beyond what any model can infer from data alone.