The insurance sector is experiencing a silent but brutal revolution. On one hand, policyholders now demand immediate responsiveness, modeled after e-commerce standards. On the other, managers are facing an explosion in request volumes and constant pressure on operational costs.
Between the two, generative AI has established itself as the indispensable bridge in policyholder relations, but you still need to know where to lay the first foundations. Too many insurers get lost in endless testing, failing to have defined a precise and effective business use case. The challenge is no longer just testing the technology, but integrating it at the heart of your growth strategy to transform every interaction into an opportunity.
This article offers a simple and expert method to successfully define the use case for your AI chatbot in a demanding sector like insurance.
Identifying the Issues: Diagnosis Before Action
Before deploying a conversational AI agent, you need to identify the frictions slowing down your organization. In the insurance sector, these obstacles fall into two major categories.
Organizational Issues
Reducing the Human Workload
Reducing the Human Workload
The first challenge insurance companies face is the chronic overload of the support service. Your advisors are experts, yet they spend their time answering low-value questions like “Where is my third-party payment card?”, “How do I update my bank details?”, “How do I change my password?”.
This is where our Genii solution comes in. Acting as an intelligent first point of contact, our AI agent resolves these requests completely autonomously via the chatbot, providing 24/7 availability. Whether outside of business hours or during peak activity periods, immediacy becomes your new standard. By automating these processes, you drastically reduce the volume of incoming tickets, allowing your teams to refocus on complex cases where empathy, detailed analysis and human expertise are truly irreplaceable.
Improving Policyholder Satisfaction
Improving Policyholder Satisfaction
Customer experience is the new battleground for companies. Policyholders constantly need to get quick answers, otherwise retention becomes difficult. As a result, an AI agent eliminates this frustration by drawing directly from your knowledge base. Unlike traditional chatbots, our solution guarantees zero hallucination: chatbot responses are ultra-precise, sourced directly from your knowledge base and personalized. You thus offer fluidity at every stage of your customer journey.
Structural Issues
Golden rule: technology must never compromise data security.
Sovereignty: A Strategic Imperative
Sovereignty: A Strategic Imperative
The question of sovereignty becomes essential when an organization plans to integrate a SaaS solution. As a result, questions arise: where is our data hosted? Who can access it? Are we still the owners of our knowledge?
Confier vos flux clients à des serveurs basés hors d’Europe représente une problématique en croissance. Tolk.ai a fait de ce point une priorité afin de garantir à tous ses clients un hébergement 100% français via Outscale France. En conséquence, lors du déploiement de votre projet IA, vous restez l’unique propriétaire de vos données. Aussi, vous gardez la main sur votre patrimoine intellectuel.
Entrusting your customer flows to servers based outside Europe is a growing issue. Tolk.ai has made this a priority to guarantee all its clients 100% French hosting via Outscale France. As a result, when deploying your AI project, you remain the sole owner of your data. You also retain control over your intellectual assets.
Security, Inclusion and Compliance
AI deployment must also align with strict legal frameworks. Most solutions are natively compliant with GDPR and already anticipate the obligations of the European AI Act (Artificial Intelligence Act).
In a spirit of inclusion and accessibility, you can go further. Our platform is 98% compliant with RGAA standards, ensuring that your AI tools are inclusive and usable by all your policyholders, including those with disabilities.
Use Case: How to Define It to Successfully Deploy Your AI Chatbot?
The success of your AI project depends on the precision of your use case. First, it is essential to identify at which stage(s) of the journey the chatbot will intervene: in pre-sales, after-sales, or both? This decision depends on your internal resources, your organization, and is not always easy to make. We offer you a quick and simple methodology to structure your thinking.
The Pre-Sales Use Case for Your AI Chatbot
In defining a pre-sales use case, the final objectives are generally conversion and engagement of the policyholder at the start of their journey through your digital channels (mobile applications, website, etc.). Ultimately, it is useful to think in terms of a decision funnel by following these 5 axes:
1
Target Intent from the Landing
Utilisez l’agent conversationnel IA pour qualifier instantanément le besoin du visiteur dès son arrivée sur votre site via des accroches promotionnelles ponctuelles ou stratégiques.
2
Simplifiez la recommandation d'offres
Simplify Offer Recommendations
3
Defuse Drop-offs in the Funnel
L’IA peut intervenir afin de lever les doutes sur des questions accessibles auprès des prospects afin d’anticiper les abandons plus tard.
4
AI can intervene to address doubts about accessible questions with prospects in order to anticipate drop-offs later.
AI can intervene to address doubts about accessible questions with prospects in order to anticipate drop-offs later.
5
Prepare the Ground for Your Advisors
The AI qualifies the file and transmits it to the right contact at the end of the chain with the necessary context to handle the request more effectively. In conclusion, managers save time in their actions and can focus on the relationship with the policyholder.
These axes can therefore help you more easily identify whether the pre-sales use case is the right one for your AI project. If one of your needs fits this model, we recommend starting the construction of your chatbot around the pre-sales journey to achieve your objectives more effectively.
Dans le cas où vous auriez plusieurs objectifs à atteindre (ce qui est souvent le cas); et que ceux-ci se croisent avec la partie après-vente, il est important de raisonner par étapes.
- If you have multiple objectives to achieve (which is often the case) and these overlap with the after-sales part, it is important to think in stages.
- Then, in what order of priority do my objectives rank?
C’est finalement cet ordre de priorité qui déterminera s’il est préférable d’intégrer l’IA en avant-vente, en après-vente ou tout au long de votre parcours client.
It is ultimately this order of priority that will determine whether it is preferable to integrate AI in pre-sales, after-sales, or throughout your customer journey.
Deuxièmement, dans l’imagination d’un cas d’usage après-vente, les objectifs finaux sont quant à eux la rétention et l’efficacité. Ce cas d’usage se concentre sur le parcours de l’assuré après la contractualisation. Globalement, les axes pour identifier si ce cas d’usage est le plus pertinent pour votre structure sont dissociables :
1
Automate Simple Management Actions
Promote self-care for your customers by allowing them to independently manage simple requests such as document updates or downloads from their policyholder portal.
2
Facilitate Claims Reporting
Your AI assistant becomes a guide for your policyholders to help with administrative procedures or answer frequently asked questions during online processes.
3
Promote transparency
The insured, via the AI assistant, can have real-time access to the tracking of their requests and obtain additional information.
4
Explain the guarantees simply
Translate the legal jargon of contracts or your guarantee tables into plain language to avoid misunderstandings.
5
Anticipate the future needs of your policyholders
Use AI to offer complementary products at the precise moment the insured needs them throughout their insurance relationship.
Ultimately, there is no single best use case. The most important thing is that your choice aligns with your objectives, your vision, and your resources.
To further your thinking, we have several client cases available in the Case Study section that may interest you.
Towards a global and responsible assistant
In conclusion, note that true best practice isn’t about choosing between pre-sales and post-sales, but about designing a unified experience. A successful project often begins with a specific need, but it must be able to naturally extend throughout your entire customer journey. By doing so, you’ll prove that AI in the insurance sector isn’t just a gimmick.
It truly represents a growth lever capable of meeting your requirements for business expertise, security and innovation.
At tolk.ai, we don’t just provide technology. We support insurance professionals in creating interactions that matter.
To go further
We are currently supporting the VYV Group on a project within the Joint Research Unit (UMR) for the benefit of managers. If you are interested, discover the feedback on the first phases of the project.
