Insights

How Can Generative AI Improve the Guest Experience?

From schedule and room inventory optimization to revenue management and look-alike audience models, artificial intelligence (AI) models have already been driving revenue for travel brands for years. But generative AI enables dynamic content through faster content generation and dynamic presentation.

New generative AI tools built on large language models (LLMs) like ChatGPT and Google’s Bard have prompted industry-specific investment and applications. At the same time, rapid adoption has introduced new risks for businesses around copyright, customer trust and factual errors.
 

Default outputs require prompt engineering, customization and fine-tuning.

As futuristic possibilities for chat-based AI tools in travel and hospitality take shape, ambitious brands should begin testing and developing a go-to-market strategy, factoring in their unique risk tolerance and business goals.

"It’s clear that LLMs have the potential to transform digital experiences for guests and employees much faster than we previously thought,” says Head of Customer Experience for Travel and Hospitality at Publicis Sapient, J F Grossen. “However, this isn’t a sprint to the finish line to apply the latest trending technology. It should be considered in the context of a larger toolkit of products and services designed for customer needs.”

This article will explore the most realistic use cases for generative AI tools and large language models in the travel and hospitality space, how these use cases can improve the guest experience and several strategic approaches to integration for both short-term applications and long-term revenue growth through AI.
 

What does the travel and hospitality industry need to know about generative AI and large language models?

Large language artificial intelligence models, like GPT-4, can predict what text should come next based on unique text inputs and prompts, drawing from a large text-based data set. LLMs are just one branch of artificial intelligence, a broad term used to describe computers’ ability to mimic human intelligence through processing, synthesizing and generating information. Generative AI refers to artificial intelligence models, including LLMs like GPT-4, that can generate new content, like audio, video and text.

“What’s particularly significant about GPT-4 is that it can handle an astounding range of language processing tasks—like creating high quality and coherent summaries, formulating answers based on questions asked, and even generating code based on natural language descriptions of what a computer program should do,” says Head of Engineering for Travel and Hospitality at Publicis Sapient, Ravi Evani.

This includes sentiment analysis (“Was the traveler’s flight experience good or bad?”), text summarization (“What are the hotel guests' top requests and complaints?”) and speech recognition (“I want an egg sandwich and a small coffee, no cream or sugar”).

Why are large language models significant for travel and hospitality leaders?

GPT-4 and other advanced large language models like it have three unique characteristics that have garnered attention from the travel and hospitality industry.

These new large language models are:

What high-value use cases exist for ChatGPT across the travel life cycle of a guest?

How to improve the travel guest experience with large language models

For brands looking to invest in large language models or generative AI, there are three major use cases to enhance the guest experience: content generation, travel merchandising and customer service.

Using large language models for travel content generation

Hospitality brands can generate human-like authored content by blending hotel information (amenities, surroundings, location, reviews, etc.) with a prospective guest’s information (interests, preferences, household info, etc.) to create dynamic, personalized narratives; and, as a result, increase the propensity of a prospect to book a particular property.

The outcomes that enterprises can aim to achieve with these capabilities are increased content relevance to the audience, increased speed to market, increased efficiency and reduced cost of content production and distribution.

Using large language models for travel merchandising

LLMs can also allow brands to drastically expand website merchandising to focus on not just the core product but the entire guest experience by quickly generating probable text outputs and images by synthesizing trends and preferences from large data sets.

The outcomes that enterprises can aim to achieve with these capabilities are increased product relevance to target the audience, increased speed to market, increased efficiency and reduced cost of merchandising.

Using large language models for hospitality customer service

Finally, large language models can power knowledge sharing and customer service to support employees and guests at the same time.

“Historically, natural language processing artificial intelligence models that relied heavily on intent recognition had to be trained with canned fallback answers, producing results such as ‘I’m sorry I don’t have that information’ that increased user frustration,” says Evani. “The ability of generative AI to produce engaging, open-ended and conversational alternatives quickly can help make a difficult customer interaction more human.”

Large language models can be leveraged by travel and hospitality brands across the customer service lifecycle:

With these capabilities, enterprises can aim to achieve reduced handling time in customer service interactions and increased responsiveness to customer problems.

How travel and hospitality brands can get started with pre-trained large language models

While generative AI’s text and image completion features have gone viral, enterprise-grade AI tools need to be more focused, predictable and repeatable.

"Enterprises must plan to bolster relevance, accuracy and consistency of large language model outputs with internal systems, models and pipelines to create reliable solutions,” says Evani.

 While AI tools are getting easier to use, enterprises must not underestimate the capabilities, including people and processes, needed to implement and leverage pre-trained models like GPT-4. The good news for digitally mature travel and hospitality enterprises is that much of the core supporting capabilities such as an AI platform or ML Ops are usually in place to some extent.

Enterprise capabilities needed to support LLMs

Brands will especially need to fine-tune models to enable fact-checking, reduce bias and provide transparency before production deployment.

“Travel companies currently rely on their associates to fact-check information, and new technology hasn't changed that,” says Grossen. “It just provides them with more conversational support, which in turn will create more seamless, efficient and personalized interactions.”
 

Three go-to-market LLM artificial intelligence strategies for the travel and hospitality industry

Large language models present an exciting yet complex opportunity to transform travel experiences, but they remain less effective without the right supporting capabilities and training.

“Brands need to narrow their focus to specific areas of expertise and train new models deeply to provide value to their employees and guests,” says Evani, artificial intelligence expert.

Innovative travel companies can get started with large language models in three different ways, starting from smaller test-and-learn investments all the way to building market-level B2B AI tools.

While ChatGPT opened up new opportunities to drive value, generative AI is still just one piece of the guest experience puzzle.

“AI tools should not be designed and developed in a vacuum,” says Grossen. “Travel brands that integrate LLMs successfully will begin with a deep, empathetic understanding of their employees’ and guests’ needs and use them as a guiding compass to test-and-learn with this new technology.”