Recent advances in generative AI and Large Language Models (LLM) have opened up exciting new perspectives for chatbots. LLMs, such as ChatGPT, are now at the heart of the evolution of chatbots and artificial intelligence. These advances have transformed chatbots into augmented advisors with enhanced intelligence, making them significantly more effective. This article will also look at the experience of our 10-year-old client Stellantis’ (ex-PSA), seeking to improve its IT chatbot’s answers with generative AI.
Understanding the relationship between generative AI and chatbots
The rise of artificial intelligence has paved the way for some remarkable innovations, including generative AI, which plays a central role in the development of chatbots. Chatbots, also known as “virtual assistants,” have evolved considerably with this technology.
Chatbots are computer programs designed to automate communication and interaction with users. They are often used to answer customer questions, provide information, or perform specific tasks without human intervention. However, with the integration of generative AI, they have become much more than just computer programs.
LLMs (Large Language Models), such as GPT-3.5, are remarkably capable of understanding and generating human language and are at the heart of this transformation. They turn chatbots into “augmented advisors,” endowing them with a deeper understanding of user needs and enabling them to learn continuously and provide more relevant answers. Chatbots powered by Generative AI can adapt their response to the context of the conversation, significantly boosting their effectiveness. Generative AI enriches chatbots with an adaptive capacity to help them meet ever-changing user needs, enabling them to respond with more natural, human, and relevant answers. Combining generative AI and chatbot technology is a big step forward in user experience and opens up new perspectives in automated communication.
In response to strong demand from our clients (including Stellantis) and prospects, Dydu has decided to integrate generative AI into its conversational AI solution while complying with GDPR and data protection requirements. This initiative clearly illustrates the growing importance of generative AI in enhancing chatbots and customer engagement.
Stellantis and the integration of generative AI in its Dydu chatbot
Faced with an ever-changing business environment, Stellantis, a global automotive leader, has made a strategic decision to invest in technological innovations, such as generative AI. The main aim is to optimize interactions with employees via its Dydu chatbot.
The current test phase reflects this drive for modernization. This phase targets employees who have recently joined Stellantis and are not yet familiar with the Microsoft suite. Ensuring new employees benefit from a smooth and efficient onboarding process is essential.
But what initially prompted Stellantis to implement a chatbot? Stellantis was looking to significantly reduce local support costs. This decision came after a recent internal reorganization, which significantly reduced the number of support teams. By implementing a Dydu chatbot, Stellantis can provide 24/7 support while optimizing resources.
After identifying new needs in 2023, Stellantis has decided to test generative AI. The key objectives are to ensure easy access to SharePoint documentation, facilitate knowledge base management, and, as an international group, provide multilingual support by integrating new languages.
All the above reflects Stellantis’ proactive vision of technological innovation. The group’s aim is clear: to be at the cutting edge, to provide employees with an optimal user experience, and to ensure operational efficiency every time they interact with the digital tools at their disposal.
Generative AI in action: use cases and associated benefits
Integrating generative AI via LLMs into our solution opens up new use cases that enhance the user experience :
- Broadened scope of response: LLMs enable the Dydu chatbot to draw on existing document databases, meaning that it can answer more user questions.
- Detection of misunderstood questions: generative AI identifies any questions beyond the bot’s comprehension. This capability paves the way for targeted improvements to ensure a more satisfying user experience.
- Language and automated translation: another powerful feature enables the bot to detect the user’s language and translate content accordingly, facilitating multilingual exchanges.
- Enhanced Natural Language Understanding (NLU) of the Dydu solution: integrating LLMs with the Dydu solution also makes it possible to generate text automatically by adding phrases and matching groups to the knowledge base. The aim is to continually enhance the user experience and improve the chatbot’s answers.
Integrating generative AI is a significant step forward in improving chatbot efficiencyand the overall user experience and can help companies optimize their customer interactions.
If this article has sparked your interest, check out our 12 October webinar for more information. Click here to find out more about Stellantis (ex-PSA)’s experience 👇
