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Is Your Data Ready for the Way Insurers Will Use AI?

Insurers are adopting AI first to cut admin, but the next step is AI that reads submissions, summarises risk and helps decide which quotes merit an underwriter’s time. 

Image Source: Salefocus

For UK brokers, that raises one question: is your data clear and complete enough for a machine to read, trust and act on? AI can only work with what it is given. The broking firms and insurers that get their data in order now will be best placed as these tools become standard.

What do insurers expect from AI today?

A 2026 Insurance Post survey of 111 insurance professionals asked what they expect AI to deliver. The answers were mostly about efficiency:

  • 47% expect less manual work
  • 36% expect faster processes
  • 9% expect better pricing quality
  • 5% expect competitive advantage

Put another way, 83% of the expected benefit is administrative support, and only 14% is related to writing better business. That isn’t a lack of ambition. It reflects what AI has mostly been used for so far..

The survey also asked where AI would be most useful in pricing. Automating workflows (22%), building models (17%), optimising rates (15%) and documentation (15%) led the list. Lower down were AI-driven tasks that act independently (8%), quote ingestion (7%) and quote triage (5%). Roughly one in five answers pointed to AI doing something on its own initiative. We expect that share to grow as more people see these capabilities working in practice.

What changes when AI reads a submission first?

As these tools mature, a submission may pass through three stages before an underwriter sees it. Of course, adoption varies from insurer to insurer, so treat this as a general direction of travel.

  1. Reading: AI extracts information from proposal forms, spreadsheets, slips and PDFs, so nobody has the arduous task of rekeying it. The quality of the result depends on the quality of the source, so if key details are missing, inconsistent or buried, confidence drops and queries follow.
  2. Summarising: Instead of dozens of individual fields, the underwriter gets a concise view of the risk, with unusual features highlighted. As people make sense of context first and detail second, AI summaries are designed to work the same way.
  3. Triage: The current reality is that insurers receive more submissions than underwriters can review, especially at renewal. Triage weighs the likely value of a risk against appetite, portfolio and existing accounts, and helps decide where human time goes.

For this process to run smoothly, each stage relies on accurate, consistent, connected data. That is why the conversation about AI is quickly becoming a conversation about data.

Why does data readiness decide the outcome?

An underwriter cannot triage, price or explain a risk whose data they don’t trust. The same is true inside a broking firm, where reporting, commission reconciliation and client money all depend on data that agrees with itself.

In Elait ’s experience, readiness comes down to ‘QCOT’:

  • Quality: Is the information accurate and complete when it is first captured?
  • Consistency: Do the same terms, formats and definitions mean the same thing across systems and teams?
  • Ownership: Does someone have clear responsibility for each key dataset?
  • Traceability: Can any figure be followed back to where it came from, so people can trust it and correct it quickly?

Of these four, traceability is most important for trust. People rely on a tool that shows its workings. When every value can be traced to its source, corrections become quick and confidence grows.

What can broking firms and insurers do now?

Here is the good news. You don’t need a large programme to begin. These steps are practical and can start small.

  • Identify the data that drives decisions. List the information your team needs to place, price or report on the business, and where it currently lives.
  • Standardise how it is captured. Fixing data at the point it enters the business is far cheaper than cleaning it later.
  • Agree definitions and ownership. Settle what key terms mean and who is accountable for each dataset.
  • Make data traceable. Ensure people can see where a figure came from and how it has changed.
  • Start with one workflow. Prove the value in a single area, then extend. This keeps cost and disruption low.

How can Elait help?

Elait is a consulting and technology company that helps enterprises master their data and prepare it for AI, drawing on experience in data governance and solution architecture. Our approach is steady and practical. We start with the systems and workflows you already have, aim to keep costs and disruption to a minimum, and build towards AI once the foundations are trusted. We can walk you through the five steps above to help you get a feel for how we support you. Drop us an email at contact@elait.com.

Frequently asked questions

What is AI data readiness?

It means being confident that your data is accurate, consistent, governed and traceable enough for AI to use safely and for people to trust the results.

Will AI replace underwriters or brokers?

Nothing in the survey points that way. Insurers mainly expect AI to reduce manual work. We believe judgement and client relationships remain central, and AI supports them.

Where should we start?

Pick one workflow that causes regular friction, such as submission handling or month-end reporting. Assess the data behind it, fix the biggest gaps, and build from there.

Source: Insurance Post survey of 111 insurance professionals, 2026.

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