What AI Hype Looks Like in Reinsurance (& How to Not Fall for It!)
AI has been having a rather long 15 minutes of fame. The hype doesn’t seem to be slowing down much and without a doubt, it’s hard to ignore! And in many cases, we don’t want to ignore it. You’ll hear good and bad things about AI, but what’s important is that leaders are taking on board the correct information, not falling for the fear-mongering and fear of missing out (FOMO, if you will!) and looking at this potentially fantastic tool in a strategic light, rather than leaning too much into the hype.
With that in mind, let’s look closer at what sheer ‘hype’ looks like in AI and strategies leaders can put in place to reduce frustration and actually gain results.
‘Success theatre’
In the race to modernise, it’s tempting to showcase progress by spotlighting surface-level wins. A flashy demo here, an automated task there. It really gives the appearance of everything coming together. This scenario has been dubbed perfectly as ‘success theatre’ by the likes of Robert Blumofe , playing into the illusion of innovation without the deep operational change to back it up.
In an article for MIT Sloan School of Management, Robert talks of success theatre in terms of LLMs. Over here in the land of reinsurance, we might refer to it more in terms of automating first notification of loss (FNOL) while the underlying processes remain fragmented. If claims data still sits in disconnected systems, or if triage decisions still require manual data rekeying, then the ‘automation’ barely scratches the surface. It may speed up one step, but it doesn’t make the end-to-end workflow any smarter or more efficient.
So then we might say that genuine progress comes from rethinking how the pieces fit together. That means asking harder questions: What data do we need to move faster? Where are the handoffs causing friction? Which manual decisions could benefit from AI and which still require human expertise?
Without that broader view, it's easy to overstate impact, thus buying into AI hype in all the wrong ways.
Please fix your data
Leaders tend to bring in AI to ‘solve’ operational inefficiencies which, granted, it can do. However, the issue here is not with AI but in the data leaders are providing it with. I particularly enjoy this quote by Forbes which gets the message across:
“AI needs data more than data needs AI”
It’s a reminder that no algorithm, no matter how powerful, can generate meaningful insight from chaos. If you don’t give your AI models the quality, connected data needed to function effectively, they won’t function effectively. In reinsurance claims, that might look like layering AI onto legacy systems where structured and unstructured data sit in different formats, in different departments, or simply aren’t being captured reliably at all.
And today’s AI models rely on such a wide spectrum of data types. Structured data (loss histories, bordereaux spreadsheets, etc.) is essential for predictions and grouping. Unstructured and semi-structured data, such as adjuster notes, images of damage, or PDF claim files, are equally vital for training AI to understand real-world scenarios. But if all of this data lives in silos, in inconsistent formats, AI becomes just another layer of confusion. You’ve bought into the hype and as a result, inputs are patchy and inaccessible.
The smarter approach, of course, is to start with your data. Build or refine the infrastructure to capture it cleanly before applying AI. This is no glamorous feat, I’ll give you that, but it’s what gives AI its power.
The human factor
Once the automation ball starts rolling, it’s tempting to apply it to everything. Especially when leaders feel pressure to cut costs or show tech leadership, there’s a growing assumption that if a task can be automated, it should be.
But in reinsurance, that’s a risky oversimplification. This is an industry that trades in complexity. Claims often involve multiple markets, bespoke treaties, intricate loss scenarios, and the negotiation of liability across borders and time zones. It’s not just data processing! We’ve got a lot of context and commercial sensitivity to look out for here.
And while AI absolutely has a role to play, it should be a tool that supports the human in the loop, not removes them entirely. AI can easily flag an unusually worded clause in a facultative placement, but it can’t tell you how that wording might be interpreted in Bermuda court or what it could signal about counterparty risk. Don’t be led astray by the hype train. Total automation, in any field, is not the goal.
Clarity over capability
Avoiding the AI hype in reinsurance means understanding where this technology actually adds value. And be honest with yourself when it doesn’t look like it’s working out! If you take anything from my article, just remember: clean data, sound operations, skilled people. With those fundamentals in place, you can’t go too far wrong.
I’ve been guiding Senior Leadership and IT teams on their digital implementation journeys for over 27 years now, offering practical support and 'been there and done it' expertise to set them up for success.
If you’d like support in furthering your technology implementation in reinsurance claims or accounting functions, drop me a message: https://www.buondrius.com/contact