---
title: "Post-Conference Season Musings: AI Continues to Evolve and Shape the Conversation"
description: Explore the evolving role of AI in Insurtech as it moves from experimentation to integration, enhancing workflows and driving meaningful business value.
image: https://blog.appulate.com/hubfs/Canva%20images/From%20Johnnys%20Desk.png
---

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 October 28, 2026

# Post-Conference Season Musings: AI Continues to Evolve and Shape the Conversation

 By  [Trae Jones, President, Appulate, Inc.](https://blog.appulate.com/author/trae-jones-president-appulate-inc)  ·   3 minute read

**Introduction**

For those of us in the Insurtech world, the fall tradeshow season brings a blend of excitement, exhaustion, and education. After participating in multiple fall insurance industry events, the Appulate team experienced a healthy dose of all three. As I reflect on the common themes that emerged this year, I wanted to share a few observations about where our industry is heading and why I’m encouraged by the opportunities ahead.

**The Ubiquity of AI**

AI continued to feature prominently in nearly every presentation, demonstration, and conversation I encountered. That level of attention reflects how much potential the technology offers our industry. At the same time, it is worth remembering that AI is not a solution to every business challenge, and its impact will vary depending on the problem being addressed. The most promising opportunities will come from identifying practical use cases where AI can make work better, faster, more consistent, or more informed, not from adopting the technology simply because it is attracting attention.

The prominence of AI was unmistakable: many of the most compelling innovations showcased across the 2026 insurance technology conference circuit had AI at their core. Nearly 100 sessions at this year’s ITC conference in Las Vegas referenced AI in their titles or descriptions. That breadth of interest is a strong signal that the industry is actively exploring how to turn AI’s promise into meaningful progress.

**How Did We Get Here? From Experimentation to Enterprise Integration**

In my humble opinion, the evolution of AI adoption in insurance can be understood as three broad, overlapping stages:

1. Many insurers first explored predictive analytics, machine learning, and robotic process automation to improve specific tasks and support more informed decisions.
2. Generative AI then created new opportunities to summarize documents, interpret unstructured information, answer questions, and assist employees with knowledge-intensive work.
3. Today, the opportunity is to bring these capabilities together within broader insurance workflows, enabling AI systems to support multiple connected tasks rather than isolated activities.

**The Rise of Agentic AI**

One of the most consequential developments discussed at ITC Vegas 2026 was the emergence of agentic AI. Unlike conventional AI assistants, which primarily respond to prompts or generate recommendations, AI agents can be designed to plan and execute sequences of tasks, interact with software applications, retrieve information, and coordinate activities within defined boundaries.

In insurance, these capabilities create an opportunity to streamline workflows that currently require employees to navigate multiple systems and complete numerous repetitive steps. Done well, agentic AI can help employees spend less time gathering and transferring information and more time applying their expertise.

Consider commercial underwriting. A well-designed AI agent can gather information from submissions, extract relevant details from loss runs, review supporting documents, identify missing information, summarize a prospective insured’s risk characteristics, and prepare an initial underwriting assessment. Additional agents could assist with pricing analysis, appetite checks, regulatory requirements, and policy documentation. Importantly, human underwriters should continue to provide judgment, expertise, and exception handling while remaining accountable for decisions. This is a compelling example of how AI can augment specialized insurance knowledge while helping teams work more efficiently.

**The Challenge of Proving Impact**

As these capabilities mature, insurers will need to determine which applications can deliver the greatest business value and how that value should be measured. Exciting demonstrations are an important starting point, but business cases become stronger when they connect a technology’s capabilities to clear outcomes, such as faster turnaround times, improved consistency, better service, lower operating costs, or more effective risk selection. For some newer tools, production data may not yet be available to establish a definitive return on investment. That is understandable at this stage of development, and it makes thoughtful pilots, well-defined success measures, and ongoing evaluation especially valuable. Insurers and technology providers have an opportunity to work together to build this evidence and identify where the benefits are most meaningful.

Encouragingly, AI is moving beyond isolated experiments in parts of the industry and becoming more closely integrated with core business processes. We are seeing a progression beyond chatbots and single-use automation toward solutions that can interpret complex documentation, coordinate workflows, support challenging decisions, and, in carefully defined settings, execute tasks with increasing autonomy. The conference discussions suggested that AI could become a foundational capability for insurers rather than a standalone technology initiative. Realizing that potential will depend on combining insurance expertise, reliable data, effective governance, and practical implementation strategies that make results measurable.

**What’s Next?**

The next phase of Insurtech competition will likely be shaped by the quality of implementation. Insurers can make meaningful progress by starting with well-defined business problems, integrating AI with existing technology and processes, and measuring improvements in performance, cost, service, or profitability. The organizations that learn systematically from early deployments will be best positioned to scale successful approaches.

A likely path forward is incremental autonomy. Insurers can initially authorize AI agents to complete well-defined tasks, establish clear controls and escalation paths, and expand responsibilities as reliability and performance become better understood.

**Conclusion**

AI in Insurtech is entering an exciting next phase. The industry is moving from experimentation with individual applications toward integrating intelligence into underwriting, claims, distribution, servicing, and other core operations. Agentic AI adds a promising new dimension, with the potential to connect tasks, reduce friction, and give insurance professionals more capacity to focus on judgment and relationships. The path forward will require thoughtful implementation, strong governance, and evidence of business value, but those are not barriers to progress. They are the foundations for deploying AI responsibly and making it an integral part of how insurance is designed, distributed, priced, and delivered.

 

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