From Pilots to Production: How AI Is Rewiring Insurance

• March 16, 2026

By Vic Yeh

For the past two years, artificial intelligence (AI) has been one of the most discussed technologies in insurance and other industries. Agencies, brokerages and carriers have experimented with new tools to explore the potential of this emerging technology. Initially, much of that activity remained experimental, with many early AI deployments existing only as demos and side tools, serving as proof of concept rather than meaningfully changing how insurance organizations operated.

That phase is now ending.

Across the insurance value chain, AI is moving from pilot to production. As organizations move beyond experimentation, AI is beginning to reshape the underlying workflows that power insurance operations. The shift is less about adding another tool to the stack and more about fundamentally rewiring how work gets done across the industry.

The Operational Reality of Insurance

Insurance is one of the largest industries in the global economy, yet the day-to-day work within agencies and brokerages remains largely manual.

Agents, account managers and service teams spend a significant portion of their time completing applications and quote proposals, analyzing policy coverages, rekeying data across multiple systems, and relaying information between clients and carriers. These operational processes are essential to running a brokerage, but they rarely create direct value for clients.

At the same time, the industry faces a growing talent shortage. With only about 25% of the current insurance workforce under the age of 35 and nearly 50% of the workforce expected to retire over the next decade, according to the U.S. Chamber of Commerce, agencies and brokerages are struggling to hire and train new staff quickly enough to keep up with demand.

This creates a fundamental challenge for brokerages: how to scale revenue and service capacity without increasing headcount at the same pace. For many organizations, AI is emerging as the most promising answer.

A New Intelligence Layer for Software

Historically, insurance software functioned primarily as a system of record. Agency management systems, CRMs and carrier portals helped digitize information but still required humans to execute the operational work. Employees were responsible for reading policies, drafting emails, comparing coverages, generating certificates of insurance and moving information between systems. Even highly digital agencies still relied on significant manual coordination across tools.

AI introduces a new interface for interaction. Instead of simply storing data, modern systems can now interpret and act on information. AI can analyze policy documents, reason through operational tasks and assist with processes that previously required significant manual effort.

When embedded directly into existing workflows, AI begins to function less like a tool and more like an operational layer supporting the entire organization. AI can analyze policies, draft client communications, compare coverages, generate documentation and assist with servicing tasks, often running in the background.

Instead of navigating multiple systems to complete tasks, professionals increasingly work through AI systems that coordinate these processes behind the scenes, even across voice calls and email channels. It becomes the interface that connects the broader technology stack together. While this transition will take time, the direction is clear: software is shifting from simply storing information to actively helping complete work.

Why Insurance Requires Domain-Specific AI

Insurance is a highly specialized and regulated industry. From quote-to-bind-to-issue and ongoing servicing, brokerages operate within workflows that require accuracy, compliance and full auditability. Errors in documentation or missing information can create significant operational or regulatory risk.

Generic AI tools often struggle in this environment. While they can generate text or summarize documents, they typically lack the domain knowledge required to understand insurance-specific workflows.

Domain-specific AI systems must be able to interpret policy language, understand the structure of ACORD forms, analyze endorsements and navigate brokerage operations. They must also operate within regulatory constraints while maintaining a clear audit trail of their actions.

The Return on Investment

When AI operates with domain-specific context, the greatest value does not come from automating a single task: It comes from connecting multiple tasks into coordinated workflows.

Consider a common request inside a brokerage: generating a certificate of insurance. Traditionally, this process involves reviewing policy details, confirming coverage information, completing documentation and sending the certificate to the client. Each step requires human intervention. With production-grade AI, much of this workflow can be automated. Systems can interpret incoming requests, verify policy data, generate documentation and deliver the certificate within minutes.

When applied across dozens of operational processes, productivity gains become substantial. Account managers regain time each week and producers spend more time advising clients and less time on administrative work. Brokerages can scale service capacity without expanding the work of staff.

For most insurance agencies and brokerages, starting to adopt AI does not require a full technology overhaul. A practical path often includes a few simple steps, including:

1) Start with a clear operational problem. Identify your most common manual workflows, such as submission preparation, policy checking or renewal preparation.

2) Map the current workflow. Understand and document where manual work slows teams down.

3) Evaluate AI options. Speak with peers and explore domain-specific AI tools that are built for insurance workflows to see how they can integrate into existing processes.

4) Pilot with a small group. Identify a cohort of users within your organization to trial the new technology and workflows.

5) Expand across the organization. Refine the process and gradually expand successful use cases across the full organization.

Across brokerages adopting AI in production, early results are already visible. Many teams report reclaiming at least several hours per week per team member, allowing staff to focus more on client relationships and revenue-generating work.

Given how quickly the market is shifting, the greatest risk for an insurance organization may be falling behind the adoption curve and needing to play catch-up. Every role in insurance is beginning to transform. Professionals who learn to delegate repetitive workflows to AI will increasingly be able to focus on higher-leverage work.

For an industry built on managing risk and protecting others, the opportunity ahead may represent one of the most significant operational upgrades in decades.

Vic Yeh is CEO of Big “I” Agents Council for Technology (ACT) supporting partner, Cara, a domain-specific AI platform purpose-built for insurance. For more insights from Cara be sure to join Vic Yeh for “From Pilots to Production: Agentic AI 101”, a webinar hosted by ACT on April 22^nd, 2026.

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The Big “I” Agents Council for Technology (ACT) has many AI resources available to help independent insurance agencies clarify goals, assess their tech stack, evaluate operational readiness, explore AI adoption, and align internally before engaging with technology vendors. Check out our AI resource hub.