Where Should an Insurance Business Start With AI?

Do not start by asking “Where can we use AI?”

That question is too broad.

Almost any business process could potentially involve AI somewhere.

A better question is:

“Which processes are currently repetitive, time-consuming or inefficient?”

That changes the discussion.

Instead of searching for places to insert a new technology, the business begins by identifying real operational friction.

For example:

• calls regularly reaching voicemail;
• staff repeatedly collecting the same customer information;
• claims teams spending time gathering basic FNOL details;
• enquiries being manually sorted and forwarded;
• employees answering the same factual questions;
• information arriving in unstructured formats;
• routine data being re-keyed into another system;
• appointments being arranged manually;
• or staff spending time qualifying enquiries before the right person becomes involved.

These are workflow problems.

AI may be one way of improving them.

Start with the workflow that wastes time — not the technology that happens to be fashionable.

Look for repetitive work

A good first automation candidate often involves something people perform many times in broadly the same way.

Consider an inbound call.

The employee may repeatedly ask:

• Who are you?
• Which company are you calling from?
• Are you an existing customer?
• What is your policy number?
• What are you calling about?
• Who do you need to speak to?
• What is the best number to call you back on?

The conversation itself may vary, but the required information is predictable.

That makes it easier to define what an automated system should do.

The same principle applies to many other insurance workflows.

Repetition creates a useful starting point because the existing process can usually be described, measured and tested.

Look for information-heavy processes

Another strong candidate is a process where much of the work involves collecting, organising or moving information.

Examples might include:

• First Notice of Loss;
• new business enquiry capture;
• customer-service triage;
• broker enquiry qualification;
• policy servicing requests;
• document intake;
• appointment booking;
• call summaries;
• internal routing;
• or straightforward website enquiries.

AI can be particularly useful where information arrives conversationally or in an unstructured form and needs to become something the business can act upon.

That is why the model:

Capture → Structure → Route → Escalate → Human Decision

can be useful across multiple insurance workflows.

Separate administration from judgement

One of the most important exercises is identifying where the process changes from administration into professional judgement.

For example, in a claim:

Collecting the date of loss is information capture.

Collecting what happened is information capture.

Collecting photographs is information capture.

Determining whether the policy responds is claims judgement.

Assessing liability is claims judgement.

Negotiating settlement is claims judgement.

Those are not the same activities.

Similarly, in new business:

Collecting company details may be administrative.

Identifying the class of business may be administrative.

Arranging a meeting may be administrative.

Recommending cover may require a broker.

Pricing a risk may require an underwriter or an appropriately governed underwriting process.

The first AI project is often easier when it sits on the administrative side of that boundary.

Choose a workflow with clear boundaries

A good first project should be easy to describe.

For example:

“Answer inbound calls, identify the purpose of the enquiry, capture the caller's details and route the call appropriately.”

That is much clearer than:

“Use AI to improve customer service.”

Or:

“Capture the information required for First Notice of Loss and send a structured summary to the claims team.”

That is much clearer than:

“Automate claims.”

Clear scope makes it easier to:

• configure the system;
• define permissions;
• create guardrails;
• test performance;
• train staff;
• measure results;
• and decide where human intervention is required.

If the scope cannot be explained simply, it may be too ambitious for a first project.

Pick something measurable

The business should know what improvement would look like.

Useful measures might include:

• fewer missed calls;
• fewer voicemails;
• faster enquiry response;
• more complete information capture;
• fewer manual handoffs;
• fewer repetitive administrative tasks;
• shorter call handling time for staff;
• increased out-of-hours availability;
• fewer incomplete FNOL submissions;
• or more qualified enquiries reaching the appropriate person.

The measurement does not need to be complicated.

The important point is that the organisation should be able to compare the new process with the old one.

Without that, it becomes difficult to know whether the AI is actually helping.

Start with a process people dislike doing

A surprisingly effective way of identifying automation opportunities is to ask staff:

“What repetitive task do you wish you didn't have to do?”

The answers can be revealing.

Perhaps a broker spends part of every morning listening to voicemail.

Perhaps a claims handler repeatedly asks customers for the same basic loss information.

Perhaps an administrator spends time copying information from one system to another.

Perhaps people constantly redirect telephone calls.

Perhaps staff repeatedly respond to questions that could be answered using approved information.

These tasks may not look strategically important.

But removing them can have an immediate operational benefit.

AI does not always need to solve the company's biggest problem first.

Sometimes it should remove the most persistent nuisance.

Avoid starting with the highest-risk decision

A business may eventually want to explore more advanced AI applications.

But the most consequential decision in the organisation is not always the best first project.

For example, starting immediately with fully automated decisions around:

• coverage;
• underwriting;
• claim settlement;
• customer advice;
• fraud;
• or complex eligibility;

can introduce significantly more governance, validation and regulatory complexity.

That does not mean AI can never support those areas.

It means the organisation may learn more safely by first applying automation to a lower-risk process where the outcome is easier to review.

Experience gained from that project can then inform more sophisticated applications later.

Do not automate a broken process

AI is not a substitute for understanding how the existing process should work.

If a workflow is already confused, adding automation can simply make the confusion happen faster.

Before building anything, ask:

• What is the current process?
• Who owns it?
• What information is required?
• Which steps are unnecessary?
• Where are the delays?
• What happens when something unusual occurs?
• Which person or team should receive the output?
• What decisions need to remain human?

Sometimes the best first step is to simplify the existing workflow.

Automation can then support the improved version.

Consider customer impact

Efficiency matters, but so does the experience of the customer.

A process may be technically easy to automate but inappropriate if customers strongly value human interaction at that particular point.

The question should therefore not simply be:

“Can this be automated?”

It should also be:

“Should it be?”

For example, an AI receptionist may be entirely appropriate for:

• identifying a caller;
• collecting contact details;
• answering routine questions;
• and arranging a callback.

But a sensitive complaint or complex coverage dispute may be better handled by a person.

Different stages of the customer journey deserve different levels of automation.

Think about exceptions before the happy path

It is easy to design a system around the perfect interaction.

The real test is what happens when the conversation does not go according to plan.

Before choosing a workflow, consider:

• What happens if information is missing?
• What happens if the customer is upset?
• What happens if the AI is uncertain?
• What happens if someone asks for advice?
• What happens if the request is outside scope?
• What happens if an integration fails?
• What happens if the service is unavailable?

If those questions have straightforward answers, the workflow may be a good candidate.

If every exception requires complicated judgement, it may not be the right place to start.

Look at integration realistically

A process may appear simple until it needs to connect with ten legacy systems.

Integration complexity can determine whether an otherwise attractive automation is sensible as a first project.

A useful first workflow may only need to:

• receive a telephone call;
• collect information;
• send a structured summary;
• transfer a caller;
• or book an appointment.

That can be much easier to implement than a process requiring deep integration into:

policy administration; claims platforms; finance systems; underwriting tools; document management; and multiple legacy databases.

Integration can come later.

The first project should ideally demonstrate value without requiring the entire technology estate to change.

Keep security proportionate to the workflow

The information involved also matters.

A simple general-enquiry assistant may process relatively limited information.

An FNOL workflow may process substantially more personal information.

A system connected to policy or claims data may require tighter permissions again.

The organisation should therefore understand:

• what data the process needs;
• where it will go;
• which suppliers are involved;
• who can access it;
• what the system can do;
• and how long information is retained.

Security should reflect the actual workflow.

This is another reason to begin with a clearly bounded use case.

Make human escalation easy

A good first AI project should not depend on the system handling every situation autonomously.

There should be a simple route to a person when necessary.

That might mean:

• transferring a call;
• sending an alert;
• assigning a task;
• arranging a callback;
• or directing the matter to the appropriate team.

The automation should handle what it is good at.

The organisation should remain available for everything else.

This reduces both operational risk and customer frustration.

A useful first-project checklist

A potential workflow is particularly attractive if most of the following are true:

• The task is repetitive — people perform it frequently in a similar way.
• The input is relatively predictable — the information required can be clearly defined.
• The process is time-consuming — it consumes meaningful staff time.
• The task is primarily administrative — it does not depend heavily on professional insurance judgement.
• The output can be structured — the result can be represented clearly and consistently.
• The workflow has clear boundaries — it is obvious what the system should and should not do.
• Escalation is straightforward — there is a clear human route for exceptions.
• Success can be measured — the business can determine whether the process has improved.
• Integration is manageable — the first version does not require rebuilding the entire technology infrastructure.
• The risk is proportionate — a mistake can be detected and corrected before it creates a serious outcome.

The more boxes the workflow ticks, the stronger the candidate.

What might good first projects look like?

For an insurance broker:

AI voice receptionist

Answer calls, identify the enquiry, capture information and route appropriately.

For an MGA:

New enquiry qualification

Collect preliminary information before passing the opportunity to an underwriter.

For an insurer:

First Notice of Loss

Capture structured claim information before a claims professional begins assessment.

For a coverholder:

Broker enquiry triage

Identify what the broker needs and route the enquiry to the appropriate team.

For a claims business:

Out-of-hours intake

Provide a consistent method of capturing new loss notifications outside normal working hours.

For almost any insurance organisation:

Approved-information website assistant

Answer straightforward factual questions and escalate anything requiring judgement.

None of these requires the business to automate everything.

They solve one defined problem.

Prove the workflow before expanding it

Once a first project is live, observe it.

Measure what happens.

Review:

• successful interactions;
• failed interactions;
• customer behaviour;
• staff feedback;
• escalations;
• missing information;
• unexpected requests;
• and the actual time saved.

Then improve it.

Once the workflow is performing reliably, the organisation has learned something valuable about:

AI capability; governance; supplier management; data; integrations; staff adoption; customer response; and operational monitoring.

That knowledge makes the second project easier.

AI adoption can therefore become incremental rather than transformational.

Build capability one workflow at a time

There is sometimes pressure to create a large “AI strategy” before doing anything practical.

A strategy has value.

But experience has value too.

One successful workflow can teach an organisation more than months of abstract discussion.

The business learns:

what AI is good at; where it struggles; where guardrails are required; how customers respond; what staff value; and where the next opportunity might be.

That creates a more grounded AI strategy because it is based on real operational experience.

Start small. Learn. Then expand.

The insurance organisations that benefit from AI will not necessarily be those that automate the most.

They may be the ones that choose the right processes.

Start with a workflow that is:

repetitive; information-heavy; clearly defined; measurable; relatively low-risk; and easy to escalate when professional judgement is required.

Solve that problem.

Measure the result.

Improve the process.

Then consider the next one.

AI does not need to transform the insurance business overnight.

It can begin by simply making one part of the working day better.

And that is often the most practical place to start.