What Specialty Insurers Get Wrong About AI
Every week, another announcement: a carrier deploying AI for underwriting, a broker automating submissions, an MGA building a new platform. The investment is real. The results, often, are not.
Most AI deployments in insurance don't fail because the technology is bad. They fail because the problem definition is bad.
The "AI for AI's sake" trap
The most common mistake is picking a capability first - we want to use a large language model - and then searching for a problem to fit it. The result is demos that impress in a boardroom but systems that don't get used on the floor.
The right question isn't "what can AI do?" It's "where does my team spend time that doesn't require their expertise?"
In specialty insurance, those answers cluster around the same places every time: extracting structured data from unstructured documents, normalising data from multiple sources into a consistent format, and building first-pass summaries that a human then reviews and acts on.
Where AI genuinely delivers
The areas where AI produces consistent, measurable value in insurance today are narrow but real.
Bordereaux processing. Normalising data from dozens of carrier templates into a single schema is highly repetitive, well-defined, and currently absorbing enormous amounts of skilled time at MGAs across the market. It is exactly the kind of task AI handles well: the rules are knowable, errors are detectable, and volume is high enough to justify the investment many times over.
Submission intake. Parsing incoming emails and their attachments to extract the structured facts an underwriter needs - insured name, class of business, key exposures, prior loss history - so they start from context rather than a blank page and a PDF.
Document extraction. Pulling structured data from policy documents, endorsements, and certificates at scale. The task is consistent, the output is verifiable, and the alternative is a person doing it by hand.
Where it doesn't
AI doesn't replace underwriting judgement. It doesn't price risk. It doesn't build the carrier relationships that get a hard-to-place submission looked at.
The mistake isn't deploying AI in insurance - it's expecting it to do things that require experience, context, and accountability that only humans can provide. Those expectations set up projects to fail and make teams cynical about the next attempt.
The right frame
Think of AI as a capable analyst who is very fast, never tired, and needs clear instructions. They can prepare the brief. They can flag the anomaly. They can draft the summary. But they need a senior to make the call.
The teams getting real value from AI in specialty insurance today are the ones who've been precise about that division of labour - and built systems that enforce it, rather than obscure it behind a chat interface.
Slipstream is built around this principle. See how it works in practice.