Open AI and ChatGPT in the hotel industry

Conversational AI powering Healthcare chatbots can serve the patient’s needs, respond to their questions in an empathetic manner, and provide appropriate support. During the peak period of the Covid-19 pandemic, it was a daunting task to get a doctor’s appointment. The healthcare providers at all levels were overburdened with the huge inflow of Covid patients.

By routing to the relevant team members and/or departments the guests’ requests based on their specificity, hotels can significantly improve both staff productivity and guest satisfaction. The conversational AI-powered chatbot lists relevant recommendations, and allows users to book their favorite hotel besides facilitating payments. As conversational contact between bot and customer can be casual and natural, and the data can often contain sensitive information, so careful technical and policy treatment is necessary. At the same time, you’ll want to make sure you can use the data you’re gathering in the future to improve the user experience. Since both conversational agents and conversational improvements allow people to communicate with you, you’ll need to figure out how to generate the material they provide.

Speak Their Language

If the bot is unable to help you complete the transaction or if it takes you to the wrong product page, it does not signal the end of the world. Common queries around location and operating hours aside, users could ask about medical procedures, health screening, symptoms, and matching doctors and could even share their personal info. To help train the bot effectively, it is important to collect real user data or as close to how real users would ask in every day virtual assistant queries. Customer preferences can also be saved and sent to a hotel CRM to boost future hotel marketing campaigns. In a chat, a user shares personal data that is sometimes sensitive and falls under the GDPR and other privacy laws around the world. Open AI, the publisher of ChatGPT does not properly inform users that it is collecting their personal data, nor does it even provide a legal basis on which to do so.

Due to societal, cultural and economic differences, the attitudes towards healthcare may differ between countries and regions. And this often directly translates into the clinical protocols adopted in the region and hospital. Even if such data is recorded and documented, they may not be labeled. Labeling is necessary for any NLP system to extract meaning and establish relations between words and entities.

Why is Conversational AI becoming a trend in Hospitality?

With the end approaching, hoteliers have a lot of questions about the transition to GA4. Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals.

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