Generative artificial intelligence is supporting the automation of processes in many industries. In this article, we will delve into one Generative AI use case that Pragma has developed in the insurance sector.
At the beginning of 2024, we told you how a business group providing services in the fields of policies, social security, and trusts in Central America built an Azure AI and Cognitive Search-based chatbot with Pragma. Six months later, we want to share some of the progress we have made with eight new features and some projects we have for the middle and long term.
Initially, this project sought to create a solution that enabled all the company's collaborators to access data from the company intuitively, efficiently, and accurately. For this reason, we trained an Artificial Intelligence that supported the sales staff by answering queries in natural language. This way, we made the company's portfolio consultation easier while ensuring that collaborators, especially those in the sales teams, consumed accurate and updated information.
Additionally, this first iteration of the project avoided hallucinations, i.e., answers that are not based on actual data, which are a susceptible issue for companies that want to start using AI in their processes.
Our first step was to analyze the success stories of companies that have improved their processes through tools similar to the one we had built.
By doing this benchmark, we realized that we could create sections of our AI that were specialized in specific functions. These are some of the ones we have developed:
So far, the artificial intelligence we developed for this insurer has supported the work of the company's employees. The new challenge is to implement it to serve end users.
We have had successful pilots in which the AI has responded accurately and with the right tone of communication to Whatsapp queries. This new functionality requires orchestrating several details, especially everything related to training and data protection, but we expect to have a launch in the short term.
Another area in which we have made very positive progress is creating a meta persona, a character, or an avatar through which users can interact with the AI.
In this field, we have managed to train the voice of artificial intelligence, and thanks to the functionalities we have already built, we have obtained information from audio and generated responses with that information, resulting in a functional version capable of holding conversations.
The next step is to develop the graphic avatar, something we can advance quickly, as there are open-source resources to achieve it. Despite this, the costs related to this new dimension of Artificial Intelligence could make it take a little longer to have an implementation.
As has been said many times throughout this article, one of the points that we have taken the most care in implementing this Artificial Intelligence tool has been everything related to data protection.
This is a particularly sensitive issue in insurance sector organizations, and that is why we have managed to build a digital solution that, unlike those of public access, allows us to protect and govern the data with which it is fed.
On this basis of trust, we will continue to contribute to building an organizational culture in which there is clarity and transparency regarding the limits that tools of this type may have while promoting the adoption and use of the functionalities in each area of the organization and for the end user.
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