If you have ever used live chat for customer support, you know the frustration. Phrase your question slightly off-script, and the bot fires back: “I didn’t quite catch that; please select from one of the following options.” For years, customer service automation relied on rigid decision trees. They worked well—until customers asked something unexpected. The arrival of AI in customer support—specifically Generative AI (GenAI)—promised to fix this, and in many ways, it has. Modern AI agents understand natural language, handle vague queries, and sound surprisingly authentic. They can even detect emotion and deliver personalised interactions that feel genuinely conversational.

The next-gen front desk: From scripted bots to intelligent agents

Traditional chatbots fell apart the moment a user drifted off-script. GenAI changes that dynamic completely.

When powered by semantic search and Retrieval-Augmented Generation (RAG), AI tools access unstructured enterprise knowledge—such as policy manuals, FAQs, and product databases. They build detailed answers in real time instead of pulling rigid responses from a fixed list.

The business impact is visible in two fields:

• Productivity: A study by researchers at Stanford and MIT found that generative AI tools boosted overall agent productivity by 14%. Less-experienced workers benefited most, improving performance by 34%.
• Cost efficiency: Gartner estimates that using conversational AI in contact centres could save billions in agent labour costs worldwide and reduce average handling time (AHT).

Yet, answering routine queries like password resets or tracking updates is just the start. GenAI delivers its greatest value in stressful, highly regulated sectors with complex workflows—like financial services and insurance.

More AI in customer support

When you think of artificial intelligence answering customers’ questions, you probably picture a simple chat window on a website. But that’s just the simplest realisation. There are actually many more capabilities behind the scenes.

AI bots can handle end-to-end conversations. In insurance, they can guide a customer through submitting a simple claim, collecting necessary data and documents before a human ever needs to step in.

Moreover, systems analyse query intent and route it straight to the right department—claims, sales, or dispute resolution—sparing the customer from being transferred multiple times.

AI used in customer support can make it more proactive. By picking up early warning signs—such as telematics data indicating a higher risk or account history suggesting a likely renewal question—AI can help companies reach out before the customer even needs to ask for help.

Thanks to instant analysis of text or voice that detects rising frustration, high-stress cases (e.g., reporting a house fire) can be automatically prioritised and routed straight to a senior claims adjuster.

Another powerful yet simple application of GenAI is automated updates. Both agents and clients will surely appreciate systems sending them timely notifications about missing documents, changes in claim status, or upcoming due dates.

AI can also play an important role in quality assurance and coaching. By analysing completed calls, it can identify where agents spent too much time, potential compliance issues, and provide managers with useful insights for targeted coaching.

Last but not least, modern IVR systems understand natural spoken sentences rather than rigid keypresses, incorporating voice biometrics for secure caller authentication.

These tools yield the best results when built as a single unified system rather than isolated solutions.

Case study: InsuranceChat and modern claims processing

Let’s focus on insurance customer service, for example. We have worked in the insurance sector for some time now and know from experience that it’s full of difficult terms, strict rules, and long policy documents, where even a small misunderstanding can be costly.

We built InsuranceChat to solve this operational bottleneck. Support reps, agents, and brokers spend far too much time digging through policy wording, exclusions, and claims procedures mid-conversation. InsuranceChat helps them quickly find the information they need to prepare quotes and provide accurate answers. It:

• gives an overview of how products stack up against each other for specific customer needs
• finds specific policy terms, coverage details, and exclusions from insurance documents in seconds
• verifies customer coverage requirements against available products efficiently
• gives immediate answers about policy details, renewals, claims procedures, and coverage explanations.

In practice, a query feeds directly into the AI Claims Assistant, which cross-references coverage terms, policy limits, and claims databases to generate a clear, contextual response in real time. Instead of putting customers on hold or calling them back days later, staff can query the assistant in natural language.

Importantly, InsuranceChat is created as an internal copilot for staff rather than an end-customer bot. You can read more about it here. Our product can operate in the background during live calls, pulling up answers, summarising past communication histories, and drafting responses so agents don’t have to switch between multiple legacy systems.

It is also designed with a deep understanding of local regulations, terminology, and market practices. The AI accurately processes Polish insurance language and compliance requirements. Intermediaries query complex policy wording and get exact answers instantly, dramatically reducing customer drop-off rates.

How the model actually knows what’s covered

A question risk managers often ask is: How does the AI actually know what a policy covers?

It’s tempting to think the answer is simply “we fine-tuned the LLM.” In practice, it’s much more about everything built around the model. The AI needs access to the right information, clear rules for how to use it, and safeguards to prevent it from filling in the gaps with guesses.

Here’s what that looks like in practice.

Under the hood

First, there’s the knowledge base. Policy documents—general terms and conditions, addenda, limits, exclusions, claims procedures—are broken down, indexed, and made searchable. A surprisingly large part of the work happens before the AI ever sees the data. Terminology needs to be cleaned up, and different ways of describing the same thing need to be connected. For example, “flooding” and “water damage” may need to be recognised as related concepts rather than treated as completely separate terms.

Then comes the way we instruct the model. Policies change, so it doesn’t make much sense to rely on the model memorising static policy facts. Instead, we focus on teaching it how to behave: cite the relevant source, say when it lacks sufficient information, and flag ambiguous cases rather than confidently making something up.

There are also safeguards in place to keep the model from overstepping. Answers need to be backed up by a specific policy clause, and if there isn’t enough evidence, the system should say so rather than guess. Sensitive personal information is also filtered out before the data reaches the model.

And finally, humans remain part of the feedback loop. When an agent spots an incorrect or unhelpful response, that feedback can be used to improve the prompts, retrieval process, or even the underlying documentation. In other words, the system gets better through real-world use rather than being treated as something that can simply be deployed and forgotten.

Where automation stops

All of this can make insurance support much faster and more efficient. But that doesn’t mean the entire claims process should be handed over to AI.

There are some decisions where getting it wrong is simply too costly.

The first is accuracy. Language models generate the most likely response. They don’t actually understand a policy in the way a human does. If an AI tells a policyholder that water damage is covered when the policy explicitly excludes it, the consequences can go far beyond an unhappy customer. There could be regulatory, legal, and reputational consequences.

Fraud and risk assessment are another example. AI is very good at spotting unusual patterns and inconsistencies. However, fraud isn’t always obvious in the data. So a system might flag discrepancies in a claim, but a human adjuster still needs to consider the wider context, intent, and credibility before making a decision.

And then there’s something technology struggles to replicate: empathy.

Making an insurance claim often means that something has gone seriously wrong—a car accident, a fire, damage to a home. In those moments, speed and accuracy matter, but so does how the customer feels they are being treated. An instant, perfectly worded AI response can still feel cold when someone is in crisis.

That’s why the most effective approach isn’t really AI versus humans. It’s about giving each one the job they’re best suited to do.

AI can gather information, search documents, spot patterns, and structure the context. People can handle judgment, exceptions, difficult conversations, and final decisions. Top performers consistently use a Human-in-the-Loop (HITL) framework. AI gathers data and structures context, but trained staff make the ultimate decisions.

The goal is not to remove humans from the process. The goal is to remove the repetitive work that gets in their way.

Finding the sweet spot: Hybrid service architecture

The main goal of AI isn’t to replace people, but to make operations smoother—to be a tool. Top companies use a tiered approach from full automation to full human support:

• The first approach is for AI to manage low-risk, repetitive tasks—checking claim statuses, fetching policy PDFs, modifying contact details, answering routine FAQs, and guiding basic onboarding.
• The second approach is for moderately complex queries. Human agents lead the interaction while the GenAI copilot works in the background, fetching data, summarising files, and drafting responses.
• The third is that companies use AI in high-value, emotionally sensitive, or ambiguous scenarios, such as suspected fraud, complex claim adjustments, appeals, and contested denials. It bypasses automated bots entirely and goes straight to experienced specialists.

A more practical future for AI in customer service

Generative AI is changing the role of customer service. Instead of focusing mainly on handling fewer calls, companies can use it to help resolve issues more quickly and give support teams better access to the information they need. AI claims assistants are a good example: they can handle repetitive research and information gathering while leaving the more complex decisions to people.

So we believe that balance between human involvement and the technology matters. It’s simply a part of building a system people can rely on. The key point is to use AI in customer service as a tool that provides real value, with clear boundaries for situations where human judgement is crucial.