Broking and underwriting teams already know where their time is wasted. It’s spent chasing incomplete submissions and on clarifications. It’s wasted on that spreadsheet that started as a temporary fix and quietly became part of the operating model. It’s lost on submission packs arriving as a jumble of PDFs, emails and attachments, or bordereaux that need cleaning before anyone can actually use them.
And of course, manual processes are not wrong in themselves. Insurance will always involve judgement, negotiation, documents and detail. That comes with the territory. The issue is the amount of human effort spent copying, checking and rekeying information between systems that were never designed to work properly together.
When workarounds become the process
Most firms have built sensible workarounds over time. The problem is that workarounds rarely scale. As volumes increase, the model cannot handle the extra work, so firms add more hands, more controls, and more manual review. That can hold things together for a while, if not for ever, but it doesn’t move things forward. If every new account, renewal or endorsement needs more hands-on handling, growth stays tied directly to headcount.
For brokers, the drag often shows up in renewal preparation, market submissions and quote comparisons not to mention claims and IBA. Placement tracking becomes harder, governance checks take longer, and client reporting turns into another manual exercise. Underwriters face similar issues: submission triage, referrals, authority checks and portfolio monitoring all demand time and attention. The work gets done, but often through persistence and institutional know-how rather than a process designed to scale cleanly. All of that creates a problem that firms rarely budget for properly: operational fragility.
The operational risk hiding in plain sight
Manual processes create risk because every rekeyed field is another opportunity for error. Spreadsheet formulas become hidden dependencies. Informal handovers rely on someone remembering what happens next, assuming they aren’t on holiday or off sick. In insurance, small administrative mistakes can quickly turn into coverage disputes, compliance issues or client service failures.
Most of the time, experienced people catch these problems before they escalate. That’s a credit to the teams involved, but relying on good people to rescue weak processes isn’t the same as having strong operational controls. When a business depends too heavily on institutional memory and heroic follow-up, it becomes harder to manage and even harder to audit as it grows.
Why better data matters
The other issue, which is often underestimated, is data. Insurance is fundamentally an information business. Brokers need reliable data to understand clients, build stronger submissions and advise effectively. Underwriters need it to assess risk, price consistently and monitor exposure across a portfolio.
Yet much of the most valuable information still sits in emails, PDFs, meeting notes and underwriting commentary. Some of it exists only in people’s heads. If information isn’t captured consistently, it can’t be reused confidently. And without that, firms struggle to build reliable analytics, meaningful portfolio insight or genuinely useful AI capability.
AI as decision support
And this is where many AI discussions in insurance miss the point. The real opportunity isn’t simply adding a chatbot to existing documents. Useful though that may be, it’s not transformational. The bigger opportunity is decision support: turning messy, fragmented inputs into something clearer and more consistent so brokers and underwriters can make better decisions more quickly and explain those decisions properly.
In broking, that might mean extracting key information from client documents, identifying what has changed since the previous renewal, or flagging missing information before a submission goes to market. It could help brokers compare quotes more effectively or indicate how particular markets may respond based on previous placements and appetite.
In underwriting, it may involve triaging submissions, summarising risks or identifying gaps in the information provided. It could support referrals by surfacing relevant guidelines and creating a clearer audit trail around decisions. None of this removes human judgement. If anything, it reinforces its importance.
An AI-enabled broker or underwriter is not someone who hands decisions to a model. It’s someone who has better context, stronger evidence and more consistent information at the point a decision needs to be made. The judgement still belongs to the individual. AI simply helps make decisions faster, more consistent and easier to defend.
Technology alone won’t fix the process
The bigger risk is treating AI as a standalone technology initiative rather than an operational improvement programme. AI will not magically repair broken workflows. If data is inconsistent, controls are weak and processes are unclear, technology will just expose those weaknesses faster. A more practical starting point is to identify where information is repeatedly rekeyed, where spreadsheets have become critical infrastructure, where decisions slow down unnecessarily, and where knowledge remains trapped in inboxes or documents.
The firms that solve this will pull ahead
Manual processes create a hidden tax across the business. They limit scalability, increase operational risk and weaken the data foundations firms need to make better decisions. In insurance, that matters. The firms that address these issues effectively will operate faster. They will be businesses that are easier to control, quicker to learn and ultimately easier to grow.
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