[jdross]

I work in insurance - regulated, human capital heavy, etc.

Three examples for you: - our policy agent extracts all coverage limits and policy details into a data ontology. This saves 10-20 mins per policy. It is more accurate and consistent than our humans - our email drafting agent will pull all relevant context on an account whenever an email comes in. It will draft a reply or an email to someone else based on context and workflow. Over half of our emails are now sent without meaningfully modifying the draft, up from 20% two months ago. Hundreds of hours saved per week, now spent on more valuable work for clients. - our certificates agent will note when a certificate of insurance is requested over email and automatically handle the necessary checks and follow up options or resolution. Will likely save us around $500k this year.

We also now increasingly share prototypes as a way to discuss ideas. Because the cost to vibe code something illustrative is very low, an it’s often much higher fidelity to have the conversation with something visual than a written document

[hattmall]

Thanks for that. It's a really interesting data point. My takeaway, which I've already felt and I feel like anyone dealing with insurance would anyway, is that the industry is wildly outdated. Which I guess offers a lot of low hanging fruit where AI could be useful. Other than the email drafting, it really seems like all of that should have been handled by just normal software decades ago.

[mjevans]

A big win for 'normal software' here is to have authentication as a multi-party/agent approval process. Have the client of the insurance company request the automated delivery of certified documents to some other company's email.

[ThrowawayTestr]

>our policy agent extracts all coverage limits and policy details into a data ontology

Aren't you worried about the agent missing or hallucinating policy details?

[tonyedgecombe]

Management has decreed that won't happen so it won't.

[jryb]

Years ago I worked at an insurance company where the whole job was doing this - essentially reading through long PDFs with mostly unrelated information and extracting 3-4 numbers of interest. It paid terrible and few people who worked there cared about doing a good job. I’m sure mistakes were constantly being made.

[potamic]

> our policy agent extracts all coverage limits and policy details into a data ontology.

Are they using some software for this or was this built in-house?

[throaway45425]

I think we are the stage of the "AI Bubble" that is equivalent to saying it is 1997, 18% of U.S. households have internet access. Obviously, the internet is not working out or 90%+ of households would have internet access if it was going to be as big of deal as some claim.

I work at a place that is doing nothing like this and it seems obvious to me we are going to get put out of business in the long run. This is just adding a power law on top of a power law. Winner winner take all. What I currently do will be done by software engineers and agents in 10 years or less. Gemini is already much smarter than I am. I am going to end up at a factory or Walmart if I can get in.

The "AI bubble" is a mass delusion of people in denial of this reality. There is no bubble. The market has just priced all this forward as it should. There is a domino effect of automation that hasn't happened yet because your company still has to interface with stupid companies like mine that are betting on the hand loom. Just have to wait for us to bleed out and then most people will never get hired for white collar work again.

It amuses me when someone says who is going to want the factory jobs in the US if we reshore production? Me and all the other very average people who get displaced out of white collar work and don't want to be homeless is who.

"More valuable" work is just 2026 managerial class speak for "place holder until the agent can take over the task".

[stefan_]

That sounds a lot like "LLMs are finally powerful enough technology to overcome our paper/PDF-based business". Solving problems that frankly had no business existing in 2020.

[heyitsguay]

Thanks for this answer! I appreciate the clarity, I can see the economic impact for your company. Very cool.