Jan 28, 2026
Why Microsoft Copilot (and other generic AI) is a Liability in Commercial Insurance

Ulme Wennberg
CTO


We’re building the intelligence layer
for insurance brokerage.
See how Vantel can fit your workflows, security requirements, and book of business in a live session with our team.
Why Copilot fails
Most brokers will eventually try Microsoft Copilot or ChatGPT to help with policy review. They’ll upload a document, ask a few questions, and receive a beautifully written summary.
Upon trying to double-check the results, they’ll conclude that AI doesn't work for insurance.
They aren't wrong about the outcome, but they’re wrong about the reason. Generic LLMs (Large Language Models) fail in the middle market because they treat a 100-page policy like a giant string of text. To a generic AI, a policy is just a story.
Most brokers will eventually try Microsoft Copilot or ChatGPT to help with policy review. They’ll upload a document, ask a few questions, and receive a beautifully written summary.
Upon trying to double-check the results, they’ll conclude that AI doesn't work for insurance.
They aren't wrong about the outcome, but they’re wrong about the reason. Generic LLMs (Large Language Models) fail in the middle market because they treat a 100-page policy like a giant string of text. To a generic AI, a policy is just a story.
Most brokers will eventually try Microsoft Copilot or ChatGPT to help with policy review. They’ll upload a document, ask a few questions, and receive a beautifully written summary.
Upon trying to double-check the results, they’ll conclude that AI doesn't work for insurance.
They aren't wrong about the outcome, but they’re wrong about the reason. Generic LLMs (Large Language Models) fail in the middle market because they treat a 100-page policy like a giant string of text. To a generic AI, a policy is just a story.
The "Fluency" Trap
Generic tools are built for fluency, not insurance reasoning. They struggle with the core architecture of a policy:
The Hierarchy Problem. They don't inherently know the difference between a Definition, an Exclusion, and a Condition.
The Endorsement Gap. If an endorsement on page 90 overrides a limit on page 5, generic AI misses it. It lacks the "cross-reference" logic required to track how different parts of a contract interact.
Hallucination Risk. This is the real issue. A generic AI is designed to keep the narrative coherent. If it can’t find a limit, it might invent one or assume a "standard" clause exists just to finish the sentence.
This is why unstandardized segments, like middle market and large commercial insurance, demand purpose-built AI.
Generic tools are built for fluency, not insurance reasoning. They struggle with the core architecture of a policy:
The Hierarchy Problem. They don't inherently know the difference between a Definition, an Exclusion, and a Condition.
The Endorsement Gap. If an endorsement on page 90 overrides a limit on page 5, generic AI misses it. It lacks the "cross-reference" logic required to track how different parts of a contract interact.
Hallucination Risk. This is the real issue. A generic AI is designed to keep the narrative coherent. If it can’t find a limit, it might invent one or assume a "standard" clause exists just to finish the sentence.
This is why unstandardized segments, like middle market and large commercial insurance, demand purpose-built AI.
Generic tools are built for fluency, not insurance reasoning. They struggle with the core architecture of a policy:
The Hierarchy Problem. They don't inherently know the difference between a Definition, an Exclusion, and a Condition.
The Endorsement Gap. If an endorsement on page 90 overrides a limit on page 5, generic AI misses it. It lacks the "cross-reference" logic required to track how different parts of a contract interact.
Hallucination Risk. This is the real issue. A generic AI is designed to keep the narrative coherent. If it can’t find a limit, it might invent one or assume a "standard" clause exists just to finish the sentence.
This is why unstandardized segments, like middle market and large commercial insurance, demand purpose-built AI.
The Vantel Difference
When we built Vantel, we didn’t just put a "wrapper" around a chatbot. We built an engine that reasons about insurance.
Data Mapping. We map contracts to insurance-specific data structures first.
Silent Risk Detection. We know when a policy is silent on a risk. In insurance, silence isn't an omission; it’s a potential gap.
Source Verification. We operate in a zero-hallucination environment. Every single data point extracted by Vantel is clickable, taking you directly to the exact line in the PDF source.
In commercial insurance, an AI that is "mostly right" is a liability. You need a tool built for the nuance of the craft.
When we built Vantel, we didn’t just put a "wrapper" around a chatbot. We built an engine that reasons about insurance.
Data Mapping. We map contracts to insurance-specific data structures first.
Silent Risk Detection. We know when a policy is silent on a risk. In insurance, silence isn't an omission; it’s a potential gap.
Source Verification. We operate in a zero-hallucination environment. Every single data point extracted by Vantel is clickable, taking you directly to the exact line in the PDF source.
In commercial insurance, an AI that is "mostly right" is a liability. You need a tool built for the nuance of the craft.
When we built Vantel, we didn’t just put a "wrapper" around a chatbot. We built an engine that reasons about insurance.
Data Mapping. We map contracts to insurance-specific data structures first.
Silent Risk Detection. We know when a policy is silent on a risk. In insurance, silence isn't an omission; it’s a potential gap.
Source Verification. We operate in a zero-hallucination environment. Every single data point extracted by Vantel is clickable, taking you directly to the exact line in the PDF source.
In commercial insurance, an AI that is "mostly right" is a liability. You need a tool built for the nuance of the craft.
Try Vantel for free
See how Vantel can fit your workflows, security requirements, and book of business in a live session with our team.
Blog
Insights & Customer Stories
Company News
Jan 21, 2026
Vantel Joins Brokerslink: Empowering the Global Broking Community with AI
Insights
Dec 24, 2025
Outlook for 2026: What tech leaders in insurance brokerages need to get right
A forward looking perspective on how AI will reshape commercial insurance brokerages by 2026, and what technology leaders must prioritize now to turn experimentation into lasting competitive advantage.
Customers
Dec 13, 2025
Catching what matters before it becomes a claim: How Renewable Guard uses Vantel in real client work
How Renewable Guard uses Vantel to uncover critical coverage gaps early and protect both clients and brokers when decisions carry real risk.
Product Announcements
Nov 26, 2025
Feature Spotlight: Interactive Highlighting & Source Proof
Protect your clients with more insight.
Grow your brokerage with more confidence.
See how Vantel can fit your workflows, security requirements, and book of business in a live session with our team.
Protect your clients with more insight.
Grow your brokerage with more confidence.
See how Vantel can fit your workflows, security requirements, and book of business in a live session with our team.
Protect your clients with more insight.
Grow your brokerage with more confidence.
See how Vantel can fit your workflows, security requirements, and book of business in a live session with our team.
Know the difference.
Choose the right cover.
Vantel helps brokers compare coverage faster and make clearer, more confident decisions.
© 2026 Vantel Inc. All rights reserved.
Know the difference.
Choose the right cover.
Vantel helps brokers compare coverage faster and make clearer, more confident decisions.
© 2026 Vantel Inc. All rights reserved.
Know the difference.
Choose the right cover.
Vantel helps brokers compare coverage faster and make clearer, more confident decisions.
© 2026 Vantel Inc. All rights reserved.









