How to get the most out of generative AI

Did you know that for 2025 will 75% of the tasks that take place in the Contact Center be carried out by an AI? And by 2030, 80% of people will have daily contact with virtual assistants*. This increasingly accelerated growth directly corresponds to the arrival of generative AI and prompt engineering.

But what exactly is generative AI? It goes beyond being a simple tool or a technological complement. We are dealing with a real revolution in the field of artificial intelligence. While the Deep Learning has marked a before and after in data processing, generative AI takes things one step further. Technology can generate content based on specific commands, optimizing and accelerating processes that were previously unthinkable.

At inConcert we are not only looking to the future, we are building it: thanks to the implementation of the most advanced generative AI model, ChatGPT, our chatbots are redefining the rules of the game in customer interaction. It's no longer just about answering questions, but about anticipating needs and personalizing each interaction to the maximum.

Bot Training

In a context of ever accelerating changes and transformations, Minimizing the time spent training bots is essential for a company's success. Previously, the task, carried out by a team of specialized linguists, required between 4 and 6 weeks. By combining GPT and Embeddings, at InConcert we can reduce this process to minutes and improve our correct response rate, achieving a CX capable of evolving in real time.

Traditionally, the process begins with drafting a list of frequently asked questions. The bot is then trained based on these, where a team of linguists write 10-15 examples for each question. In a third instance, the bot is tested to adjust any type of detail, prior to its release into production.

Until now, the efficiency of the Bot was largely dependent on the FAQs provided. However, companies are rarely able to translate all their business knowledge into a question and answer format. Now, through generative AI, it is possible to unify all existing structured and unstructured information into a single knowledge base to activate it in a matter of minutes.

Simplified knowledge management

The linguistic AI models developed by OpenAI and other companies researching generative AI make it possible to dispense with traditional FAQs and implement much more dynamic and immediate strategies. The language analysis capacity of these engines speeds up the training phase of a bot, since they are able to “understand” the content we provide based on multiple inputs, such as PDF documents, tutorial videos, public websites, historical conversations and other knowledge bases. Generative AI not only has the power to analyze large and varied sources of information instantly, but also to detect patterns and relationships to generate new content and much more efficient interactions.

In this way, fundamental goals are achieved: reduced bot development time and costs in the initial setup. In addition, achieving a higher percentage of detected responses improves customer satisfaction and significantly reduces inquiries to advisors, offering personalized interactions at the right time and human support in key situations.

In turn, it saves the need to develop quick response templates for advisors or knowledge portals that house all the information that may be necessary to resolve a particular situation. It also avoids lengthy training and updating processes by using embeddings and a simple graphical user interface (GUI) to make changes immediately and with minimal effort.

Intent detection

The union of GPT and NLU enhance intent detection of users and customers in each of their interactions. This technological advance not only improves cognitive model training times, but it also allows us to provide a hybrid approach: it generates and interprets with GPT and uses NLU when it is necessary to execute a rigid procedure, taking into account the entire historical context of the conversation.

Conversational Bots with training and active learning capabilities achieve greater understanding and more natural conversations with each interaction. On the other hand, incorporating STT (speech-to-text) and TTS (text-to-speech) voice technologies allows them to understand the colloquial speech of customers and respond in the same way. Using GPT as a response generator enables an excellent multilingual experience where we can answer in the user's language, regardless of the language defined as predefined.

Quality Leads

We know that Generate quality leads can be one of the most difficult (and costly) tasks in your strategy. In this context, Conversational bots not only do they enhance the recruitment phase through greater engagement but, as the lead rate increases, they simultaneously improve conversion and lower costs, directly impacting the CAC.

In addition, as we have seen previously, the bot can carry out conversations in real time asking specific questions for a correct classification of the lead and, based on the answers received, can continue with a specific subset of tailor-made questions to detail the classification in a more granular way.

Sales support

Generative AI also improves agent efficiency, ensuring that they follow a specific script and assisting them in real time with strategic feedback based on the ongoing conversation. For example, if the customer mentions a competitor, the system provides them with information about their own competitive advantages in order to strengthen their sales arguments.

This also makes it possible to overcome recurring problems, such as keeping agents informed and updated about dynamic changes in the services offered. In addition, it facilitates the onboarding and training of new agents, providing quality support to increase your efficiency.

At inConcert, we are at the forefront of innovation.We offer up-to-date technological solutions with the latest interaction models to optimize the entire customer lifecycle and multiply sales in an intelligent, efficient and scalable way.

Don't miss our next article to learn how we can transform your business with generative AI, with 7 uses to optimize everyday processes in an intelligent way.

*Source: Emerging Tech Impact Radar: Hyperautomation, Gartner (2023)

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