Productivity vs Risk in the Age of AI

AI is powerful. Everyone has been playing with it, and most of us, by now, have realised that not all AI tools are created equal. General-purpose platforms like ChatGPT are incredibly useful, but in a professional setting, they weren't designed to handle the privacy, compliance, or content accuracy needs like those essential to the insurance industry.

The question for most brokers today is not whether to use AI. It's choosing which AI to trust.

The Productivity Trap

Why brokers are turning to AI. And why it's risky

It's hard to ignore the promise of AI. It speeds up research, summarises documents in seconds, and helps professionals get through their workload faster.

However, in professions like insurance, where accuracy, confidentiality, and compliance are crucial, relying on the wrong AI can introduce serious risks.

In a rush for efficiency, there is an increasing danger of uploading sensitive client data into public tools or relying on summaries that appear convincing but are ultimately incorrect.

While we have yet to see brokers end up in court through the misuse of AI, we have already seen examples in adjacent industries. In the UK, lawyers have faced judicial warnings after submitting court filings that relied on fictitious cases generated by AI. In addition to professional embarrassment for the negligent parties, these cases have shaken trust in the legal system.

The stakes are similar for brokers. A bad call based on inaccurate AI output can mean denied claims, lost clients, and professional liability.

Not All AI Is Built the Same

Custom-trained tools matter in insurance

General-purpose AI tools, like ChatGPT, are trained on a broad range of internet content. They're excellent for general knowledge tasks. But they weren't built to navigate the nuance of insurance policy wording. Or to recognise where a clause might carry serious implications at claim time.

Imagine a broker asks an AI to review whether a policy covers flood damage. A general tool might locate the word "flood" in the document, but it may not understand the difference between "included," "excluded unless," and "excluded under all circumstances."

Worse, it might give a confident-sounding summary that misses a critical sub-limit or policy condition, and the broker wouldn't necessarily know it's wrong.

Industry-specific tools have an edge. AI trained on insurance content is coded to recognise key — uniquely insurance — patterns, flag ambiguous wording, and highlight differences between similar-looking policies. It interprets language in context and surfaces details that might otherwise go unnoticed.

Accuracy Starts with the Dataset

Training AI on the right content — and keeping your data where it belongs

When AI produces an output, it's drawing on everything it has "seen" before. In the case of generic models, this can include a mix of government sites, textbooks, blog posts, forums, and even Reddit threads.

That diversity can be useful for general writing tasks. But insurance requires absolute precision. A model trained on structured insurance policies and compliance documents is far more likely to accurately handle exclusions, endorsements, and technical definitions.

There's also the matter of input. In a public AI tool, everything you enter may be used to train future models. And it may be stored indefinitely. For brokers handling confidential client data, that alone could be a deal-breaker.

By contrast, at Raindrop, our purpose-built platform is structured to ensure data remains internal, secure, and excluded from any broader training set.

Clarity In Every Drop

AI can be incredibly helpful. But only when you know what it's built on, what it's doing with your data, and how much you can trust the answer it gives you.

For brokers, that trust has real consequences. It influences the advice you give, the cover you recommend, and the risks your clients are (or aren't) protected from.

Raindrop is a tool that helps brokers read and compare insurance policies more easily. It utilises AI to process the documents, pull out key details, and make it quicker to spot what's covered, what's not, and how one policy differs from another.

And because it draws only from actual policy documents (not public data or general web content), you know exactly where the answers are coming from.