Short Term: What We’re Seeing Inside Independent Agencies Today

By Jacob Smith, Founder, Incepta

April 2025

Abstract

This paper explores the early points of AI integration within independent insurance agencies, with a focus on operational workflows. Drawing from detailed implementation research and direct observation within small to mid-sized firms ($3M–$15M ARR), it argues that artificial intelligence will not enter the sector through underwriting or high-level strategy, but rather through the quiet replacement of repetitive, low-discretion administrative work. This process is already underway. The paper outlines the current operational structure of independent agencies, identifies the friction points most susceptible to AI agent deployment, and explores the implications of targeting tasks versus roles in automation strategy. It concludes with an assessment of which areas remain resistant to automation and how those boundaries are shifting.

1. Introduction

Public discourse around artificial intelligence in the insurance industry frequently centers on underwriting models, predictive analytics, and customer-facing chatbots. However, this paper argues that these conversations overlook where AI is already having the most material impact: deep within operational workflows. Based on fieldwork and applied implementation research, the claim is simple: AI is entering independent insurance through back-office process replacement—not strategic decision-making.

In this report, we explore how AI agents are beginning to restructure the operations of independent agencies, not through abstract disruption, but through measurable removal of routine human labor.

2. The Operational Structure of Independent Agencies

Independent insurance agencies operating in the $3M–$15M revenue range tend to follow a relatively consistent structural pattern. Headcounts range from 5 to 30 individuals. Most employees fulfill cross-functional roles, with common combinations being account management and quoting, or sales and service. Despite variation in product lines and carrier access, the operational model is typically characterized by reactive workflows, high volumes of administrative input, and fragmented tooling.

The digital infrastructure of these agencies is often composed of several unintegrated systems: an Agency Management System (AMS), email (typically Outlook or Gmail), quoting platforms, spreadsheets, and sometimes a Customer Relationship Management (CRM) system. Workflow logic typically lives not within these systems, but within staff habits. Even in relatively high-performing agencies, work is driven by memory, email threads, or Excel task trackers.

In this context, automation is not blocked by technological capability—it is blocked by lack of visibility. The first challenge is not engineering, but seeing the structure of the work clearly enough to intervene in it.

3. Identifying the Friction Layer

While agencies rarely describe their operations as inefficient, direct observation reveals a layer of invisible friction that compounds significantly over time. This includes:

• Certificate of Insurance (COI) requests arriving sporadically, often late in the day

• Manual re-entry of client data across multiple platforms

• Delays in quoting due to redundant intake processes

• Renewal outreach performed inconsistently or manually

• Loss run collection requiring individualized follow-ups via email

• Producers interrupting operational workflows to resolve “simple” issues themselves

In some agencies, virtual assistants (VAs) have been employed to alleviate this load. However, these assistants are often limited by language barriers, lack of context, and inconsistent execution. The result is that core staff still carry the burden of review and correction.

These friction points do not generate immediate crises, which is why they persist. But at scale, they introduce operational drag that slows responsiveness, increases error rates, and limits growth.

4. Why AI Targets Operations First

AI does not enter a business at the most complex point. It enters at the point of highest return for lowest complexity. In the independent insurance context, this means tasks that are repetitive, data-driven, and low in discretionary reasoning.

Tasks currently being automated through agent deployment include:

• Certificate intake and delivery workflows

• Quoting preparation (data formatting, cross-platform entry)

• Renewal email outreach and intake forms

• Open proposal follow-up

• Loss run collection and reminder logic

• Routine email responses (e.g., payment instructions, ID card requests)

The common structure of these tasks includes high volume, minimal decision-making, and frequent cross-platform handoffs. These characteristics make them suitable for AI agents orchestrated through Large Language Models (LLMs) and custom toolchains.

5. Workflow Mapping as a Prerequisite

The quality and viability of automation is entirely dependent on the specificity of workflow mapping. Most agencies lack accurate models of how tasks are performed. Job descriptions are insufficient proxies for real behavior, and even seasoned operators often misestimate the time, complexity, or branching logic of their processes.

Our internal implementation process begins with highly detailed workflow mapping. This includes:

• Identifying every task performed by each employee

• Documenting average time per task

• Mapping workflow branches with conditional probabilities

• Noting system touchpoints (e.g., AMS, quoting platforms)

• Recording context loss and friction zones

• Capturing pay structures, margin structures, and revenue context

This process generates a granular model of agency operations. Only with this visibility can tasks be filtered for automation according to viability, ROI, and available tooling.

6. Task vs. Role: Strategic Approaches to Automation

One important question in applied AI implementation is whether to target individual tasks or whole roles. Many early-stage automations focus on tasks scattered across employees. This provides time savings, but unless that time is actively restructured or reassigned, it does not create measurable efficiency at the organizational level. In fact, it may create under-utilized labor capacity that degrades into idleness.

An alternative strategy is to cluster automatable tasks by employee, with the goal of removing—or fundamentally altering—a specific role. This creates clearer cost savings and more direct labor reallocation. While this approach is more sensitive politically, it is often more impactful from a return-on-investment standpoint. Agency leadership must confront the reality that making someone “more efficient” only adds value if that time is systematically repurposed—either into higher-value tasks, new revenue channels, or headcount reductions.

7. Current Boundaries of AI in Insurance

Despite the rapid progress of LLM-based agents, certain categories of work remain outside the current scope of automation. These include:

• High-context client communications

• Escalation handling requiring case-specific nuance

• Underwriting decisions with regulatory or actuarial complexity

• Strategic sales negotiation

• Tasks involving non-standard data sources or undocumented logic chains

However, these boundaries are dynamic. Advances in orchestration frameworks, model fine-tuning, retrieval-augmented generation (RAG), and agentic planning will likely shift this line rapidly. It is already possible to chain multiple models and tools together to mimic more complex workflows than previously assumed. The assumption that AI is useful only for “basic tasks” is increasingly outdated.

8. Conclusion: What AI Will Really Do to Insurance

AI will not disrupt independent insurance by making underwriters obsolete. It will disrupt the sector by removing invisible drag from the operational core—the daily churn of administrative tasks that no one enjoys, but everyone performs.

This is not a hypothetical future. It is already happening, quietly, inside agencies that have taken the time to model their workflows and test where AI agents can replace low-leverage labor.

The future of independent insurance will not be defined by bold headlines or industry-wide platform shifts. It will be defined by the operational quiet of agencies that scale faster, hire later, and maintain margin in markets that increasingly punish inefficiency.

In the long arc of automation, the firms that win will not be those who adopt AI for the sake of innovation, but those who target friction directly, measure its cost, and deploy agents with intent.

The shift has already begun.