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Insights

Jan 28, 2026

Why Microsoft Copilot (and other generic AI) is a Liability in Commercial Insurance

Ulme Wennberg

CTO

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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.

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.