Conversational AI is advancing rapidly thanks to new capabilities. Strong data and strong ethics are essential to meaningful conversations.
The potential applications of AI have increased recently due to the possibilities ChatGPT launched OpenAI. For data players, many of us in the data world have watched with enthusiasm the rise of conversational AI, which first appeared in 2018. Launching Google Duplex.
But this phenomenon is not new, and for years AI has taken an increasingly important place in our lives. It’s just that the general public usually doesn’t notice these changes directly. Indeed, most of the benefits of AI and machine learning only appear behind the scenes.
Today, the rise of generative artificial intelligence captures everyone’s imagination more than ever.
Humans are made to talk, and conversational AI is opening up new horizons.
Over the past few months, many people have tried OpenAI’s ChatGPT interface. From essay writing to writing a Ryan Reynolds commercialthe platform is tested from all angles and generates many reactions. Unsurprisingly, since the first mention of artificial intelligence in Metropolis in 1927, the world has created a fictional relationship between humans and AI. Whether inspired by fantasy myths or horror tales, these relationships have always had one thing in common: conversation.
Language is part of human DNA. From the age of 7 months until the end of life, a person speaks. It is estimated that the average person speaks 16,000 words per day. What if our interlocutors were only human? It is this aspect of AI that is so exciting. And the consequences are huge.
This means that conversational AI can potentially transform or enhance the entire digital experience.
However, designing relevant digital experiences using conversational AI is a journey fraught with pitfalls. To truly integrate conversational AI into every digital experience, conversational AI needs to go beyond simple testing, and companies need to plan now how to maximize its results in the customer journey.
How can AI create relevant conversations? How can businesses use it?
Conversational AI has value only because of the knowledge it contains, as with all machine learning applications. With every news story comes its share of concerns about the poor quality of the source information, which potentially leads to poor quality of the answers provided. Indeed, any erroneous, incomplete or out-of-date data fed into the process can only produce distorted results at the end of the chain. . In many ways, initial results from experiments with ChatGPT and other large language models show that the results provided by these models clearly reflect the query itself or the data on which the model is built.
So companies should take a step back. It’s important not to see a conversational AI interface as the only solution to answer all the questions their employees or customers have.
During joint conversations or searches, two groups of questions are asked. The first group consists of requests with or without a mark, and the second group consists of objective or subjective requests. Combining these two groups results in four distinct categories: subjective questions that do not mention the brand, objective questions that do not mention the brand, subjective questions that mention the brand, and objective questions that mention the brand.
By segmenting different types of questions into these four categories, companies can quickly identify opportunities for relevant AI-driven conversations while limiting potential errors.
While the first three categories are common in broad searches or when people are looking for reviews or ratings, the last category (objective questions that mention the brand) is where conversational AI can excel. Conversational AI is limited in the context of subjective questions and conversations, especially when people ask how and why such an opinion was expressed on a given topic. In addition, most companies depend on third-party review sites for subjective questions.
Objective questions that mention the brand (upper left scale) are the best opportunity for companies to maximize the impact of using conversational AI. These questions provide the best customer experience, but also require a different approach to data. The data needed to respond accurately and reliably must be collected from different parts of the business and stored so that the conversational AI can easily access the most up-to-date information.
The influence of conversational AI is combined with your data
To collect and organize information, brands can use a knowledge graph (ie, a content manager) powered by hundreds of platforms like Google, Bing, Siri, and Alexa — platforms that are already powered by AI. This same approach could allow conversational AI systems like Chat GTP to access the same data to answer objective questions that mention the same brands.
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The battle for the best conversational AI technology has only just begun. Google, Bing, Amazon and even Netflix are likely to integrate a conversational interface into their platform tomorrow. However, the issue of AI ethics and data integrity will become a central issue in the AI conversation. Businesses looking to use conversational AI to improve their customer experience must now ask themselves how to manage and organize their data in a way that meets privacy requirements.
Using conversational AI, brands will be able to interact directly with their consumers or associates instead of using third-party platforms such as search engines. Interactions will then become more direct and powered by their own data linked to a conversational AI interface.
Looking first at the objective questions that mention the brand, companies must develop a strategy to centralize and organize the relevant data in a platform designed to feed AI systems, while maintaining control and monitoring. person as to the exact origin of each answer. With this approach, companies can prepare for this new user experience by fueling every customer conversation with the right answers.
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