AI has the potential to remove a significant amount of repetitive administrative work from insurance.
But the most successful automation projects rarely begin with the question:
“How can we use AI?”
A better question is:
“Which parts of this insurance process are repetitive, structured and time-consuming — and which parts still require professional judgement?”
That distinction matters.
Insurance processes often combine straightforward administrative work with decisions that require experience, regulatory awareness, customer understanding and professional judgement. Treating the whole process as something that should simply be “automated” can therefore create unnecessary risk.
The practical approach is to identify individual workflows, define exactly what technology is allowed to do, and build clear boundaries around what must remain with people.
At Riskbotix, we think about this as:
Capture → Structure → Route → Escalate → Human Decision
That model can be applied to many everyday insurance processes.
Start with the workflow, not the technology
Businesses sometimes approach AI as a large transformation project.
For most insurance organisations, that is not necessary.
A better starting point is usually a clearly defined operational problem.
For example:
- too many inbound calls being answered by voicemail;
- staff repeatedly collecting the same information from customers;
- claims teams manually gathering First Notice of Loss information;
- enquiries arriving in different formats and needing to be re-keyed;
- brokers spending time qualifying routine enquiries;
- customer requests being manually forwarded to the correct team;
- employees repeatedly answering questions that could be handled from approved information.
These are not fundamentally AI problems.
They are workflow problems.
AI and automation simply provide new ways of handling parts of those workflows more efficiently.
The objective should therefore not be to automate as much as possible.
It should be to automate the parts that can be handled consistently and safely.
What makes a good insurance workflow for automation?
The strongest candidates usually share several characteristics.
They are repetitive.
They involve relatively predictable information.
They consume staff time without necessarily requiring professional judgement.
They follow an identifiable sequence.
And the outcome can be checked or measured.
Consider an inbound insurance enquiry.
A caller may need to provide:
- their name;
- contact details;
- organisation;
- reason for calling;
- whether they are an existing customer;
- relevant policy or claim information;
- the person or department they are trying to reach.
Capturing that information does not generally require an insurance professional.
Deciding whether cover applies, interpreting policy wording or advising the customer may do.
That creates a natural boundary for automation.
The system can collect and structure the information.
The appropriate insurance professional can then make the decision.
Capture
The first stage is collecting information consistently.
This could happen through:
- a telephone conversation;
- a website assistant;
- an online form;
- an email;
- a claims notification;
- or another digital channel.
A well-designed automated system should not simply record an unstructured conversation.
It should gather the information required for the particular insurance workflow.
For example, a First Notice of Loss process may ask for details such as:
Structure
Once information has been collected, it becomes much more useful if it is structured.
Instead of a member of staff receiving a long voicemail or transcript, the information can be presented in clearly defined fields.
- review;
- search;
- route;
- transfer into another system;
- report on;
- and act upon.
Route
Once the information has been captured and structured, the next task is getting it to the right place.
That might mean:
- transferring a telephone call;
- emailing a structured summary;
- notifying a claims handler;
- sending an enquiry to the correct department;
- creating a task;
- arranging a callback;
- booking a meeting;
- or passing information into an internal system.
Routing rules can be defined by the insurance business.
For example, a new business enquiry might go to one team, an existing policyholder to another and an urgent claims notification somewhere else.
The system is following a defined business process rather than independently deciding what should happen.
Escalate
Good automation also needs to know when to stop.
This is one of the most important safeguards.
There will always be situations where a human should become involved.
Examples might include:
- a distressed or vulnerable caller;
- an emergency;
- a complaint;
- a question about whether something is covered;
- an unusual claim circumstance;
- a request for insurance advice;
- uncertainty about the information being provided;
- or any situation outside the workflow the system has been designed to handle.
The correct response is not to improvise.
It is to escalate.
That might mean transferring the caller, arranging a callback or clearly informing them that an appropriate member of the team will need to assist.
A well-designed AI system should therefore have both capabilities and boundaries.
Human decision
The final stage is deliberately human.
Automation can help gather the information required to make a decision.
It does not follow that the automation should make the decision itself.
This distinction is particularly important in insurance.
A claims handler may need to determine:
- whether a policy responds;
- whether an exclusion applies;
- what further investigation is required;
- whether liability should be accepted;
- or how a claim should be settled.
An underwriter may need to assess risk.
A broker may need to advise a client.
Those are fundamentally different activities from collecting, organising and routing information.
The technology should support the person making the decision rather than obscure where responsibility sits.
What about AI making mistakes?
This is one of the most reasonable concerns businesses have about adopting AI.
Generative AI systems can misunderstand questions, misinterpret information or produce incorrect responses.
The answer is not to assume that these risks disappear.
It is to design the workflow around them.
Controls can include:
- limiting the system to approved information;
- defining subjects it is not permitted to answer;
- requiring escalation for particular enquiries;
- confirming important information with the caller;
- providing structured rather than unrestricted outputs;
- testing the system before deployment;
- monitoring real interactions;
- reviewing exceptions;
- and updating the workflow as new situations are identified.
The narrower and clearer the task, the easier these controls become.
An AI system asked to “deal with all customer enquiries” has an enormous scope.
A system asked to “capture these eight pieces of information and send them to this team” has a much more controlled one.
Security and data handling still matter
Insurance workflows often involve personal and commercially sensitive information.
Any automation project therefore needs to consider issues such as:
- what information is collected;
- where it is processed;
- who can access it;
- what third-party suppliers are involved;
- how information is transferred;
- how long it is retained;
- how recordings and transcripts are handled;
- and what happens when information is no longer required.
These questions should be considered as part of the workflow design, not after the technology has been deployed.
A useful automation project should reduce operational workload without creating an uncontrolled information flow elsewhere in the business.
Keep the scope clear
One of the most useful disciplines in insurance automation is defining exactly what the system does — and what it does not do.
Clear boundaries make automation easier to understand, easier to test and easier to govern.
Start small and measure the result
An insurance business does not need to automate an entire department to see a benefit.
A sensible first project might be one process that currently consumes several hours of staff time every week.
Measure:
- how many interactions are handled;
- how much information is captured successfully;
- how often the process requires escalation;
- how much staff time is saved;
- how customers respond;
- and where the workflow needs improvement.
Once the process is working reliably, the same principles can be applied elsewhere.
This incremental approach is generally more practical than trying to introduce AI across the organisation in a single programme.
Automate the process. Keep people in control.
The most useful insurance automation is often not the most ambitious.
It is the automation that quietly removes repetitive work from a clearly defined process.
Capture the information.
Structure it.
Route it.
Escalate when necessary.
Then let the appropriate insurance professional make the decision.
That is how AI can be introduced into insurance workflows in a way that is practical, controlled and useful.
And in many cases, it is a much better starting point than trying to automate insurance itself.