💎 AIB - How AI Is Already Creating Real Leverage for Insurance Legal Teams - with Matthew Smith

Today I am joined by Matthew Smith, Senior Legal Engineer at Legora.

Across insurance, legal teams are under pressure to handle growing volumes of claims, coverage questions, contracts, compliance work, and regulatory change without adding unnecessary friction to the business. 

That raises a bigger question for the market:

Where can AI create real leverage inside insurance legal workflows without losing the judgment, consistency, and control these teams need?

Matthew explained why the clearest opportunities start with repetitive, document-heavy work; how AI can support claims triage, policy review, legal research, contract redlining, and horizon scanning; and why adoption depends as much on workflow design and training as on the technology itself.

Where do you see the greatest opportunity for AI within insurance legal teams?
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Let’s dive in.

Legora started as a collaborative AI platform for legal teams, which is why in-house and insurance legal functions are a natural fit. 

Insurance teams often deal with very high volumes of legal work. A lot of that work can be repetitive and process driven, especially when you look at insurance claims. 

That is where Legora can help. We have built tools in our agentic operating system, where teams can run insurance triage requests from the outset, compare witness statements, expert reports, and many other documents, produce chronology lists and more.

Because we started with legal teams, in-house legal teams at insurance companies are also a strong fit for the platform. We are now seeing use cases across compliance, regulatory teams, and claims!

The most obvious leverage is time saved.

Take something repeatable: an insurance policy wording and a new claim coming in. The team needs to review whether the policy responds to the incoming claim.

Traditionally, that is a very manual and repetitive task.

With Legora, the team can upload the relevant documents, and the platform can review them and generate a file note explaining whether the insurance covers the incoming claim or not.

When you multiply that across thousands of claims in a month, the time saved becomes significant. It means legal teams can focus more on higher-value, more strategic insurance work, rather than being bogged down in monotonous claims review.

There is another side to this as well. Some insurance companies have historically outsourced smaller claims or smaller legal tasks because they could not justify the internal time cost. With AI supporting a lot of that manual review, we are seeing some teams bring that work back in-house because it becomes more viable again.

It is not only about high-volume, low-value claims, although that is a strong use case.

In higher-value claims, Legora can still support the team as an assistant. For example, it can help with legal research, reviewing case law, looking for judgments, or supporting contract review and redlining inside Microsoft Word.

Another area is horizon scanning. With Legora’s Monitors tool, teams can set up a topic and jurisdiction, and the platform scans curated sources for new articles related to that topic. For in-house legal, compliance, or regulatory teams in insurance, this can reduce another very manual and time-consuming task.

A lot of insurance legal work involves reading, reviewing, comparing, and checking documents. AI can support that first-pass review much more quickly.

For example, in claims, teams often spend a lot of time working out whether there is a claim at all. Sometimes a fact in the papers only comes out much later, after two or three years, and that can mean a lot of time and cost has already been spent.

With Legora, that first-pass review can happen faster. It does not remove the need for human judgment, especially on complex or high-value claims, but it helps teams get to the key issues earlier.

The second change is consistency. 

If a team has a task it does hundreds of times a week, they can save that as a prompt or workflow inside the platform. That workflow can then be shared across the team, so people are working in a more consistent way.

So the change is not just speed. It is also how work is reviewed, how repeatable tasks are handled, and how teams start to think about applying the same process across similar matters.

The no-brainer starting point is to identify the tasks the team is already doing repeatedly. 

If a team is reviewing the same type of document, running the same coverage check, or completing the same legal process hundreds of times, that is where AI should start. 

Once the team has Legora, that repeatable task can be turned into a prompt or workflow, saved, and shared with the wider team. 

That means the team sets up the process once and continues to get value from it every time the same type of work comes back. 

The bigger challenge is not the AI tool itself. It is onboarding, training, and adoption. It is not enough to give people access and say, “Off you go.” 

Teams need to understand how to use the platform properly, how to apply it to real work, and how to use more than one feature. That is why we usually work with teams over a period of time. 

The process starts with onboarding and getting people comfortable with the platform, then moves into deeper sessions around specific use cases and features. After that, teams have a dedicated engagement manager.. 

The mindset shift is important. Before starting a task, people need to ask whether that workflow can be supported by AI before doing it manually. 

Some processes can be moved into Legora almost as they are. Others should be stripped back and redesigned. Just because something has been done a certain way for years does not mean it has to stay that way. 

With AI, legal teams have an opportunity to rethink the workflow, not simply copy the old process into a new tool.

The strongest take from Matthew is that AI adoption in legal teams is not about giving lawyers another tool. It is about changing the unit of work. 

The first wins sit in high-volume, document-heavy workflows where insurance legal teams already spend too much time: claims triage, coverage checks, contract review, legal research, and regulatory monitoring. 

However, the bigger opportunity is not just speed.  It is earlier issue-spotting, more consistent first-pass review, and better use of legal judgment on the matters that actually need it. 

That matters in insurance because legal work is tied directly to claims cost, coverage decisions, compliance risk, and business responsiveness. 

The human lawyer does not disappear. The role moves up the value chain: from manual review to judgment, escalation, consistency, and risk control. 

The adoption challenge is not access to AI. It is knowing which workflows to rebuild, how to train teams, and when to stop copying old processes into new tools.

See you on Friday!

Fabio Caravita
Founder, AI Insurer Brief
[email protected]

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