Generative AI (genAI) has emerged as the latest buzzword in the AI field, offering innovative possibilities.
Use cases of generative AI in travel include travel content creation, customer service chatbots and itinerary creation. Whereas, traditional AI is more established in consumer products, focusing on analyzing patterns and making predictions based on structured, domain-specific data solving problems including personalized, contextual travel suggestions, travel demand prediction, and optimizing hotel promotions based on travelers' preferences.
While genAI excels in content creation and reasoning by analyzing patterns from extensive data sets, traditional AI is more reliable, easier to scale, and a safer bet for applications requiring high precision.
Factors to consider while choosing AI approach include type of problem to solve, available data, delivery timeline, performance measurement, costs and scalability of the solution.
Traditional AI is preferred for relevance and search optimization, semantic matching using Machine Learning, and propensity models to predict user behavior while genAI is ideal for distilling large amounts of information into insights such as summarizing content from traveler reviews.
The combination of both genAI and traditional AI can create a personalized and efficient experience such as using traditional predictive data to gather restaurant preference information from the user and combine it with existing data to create prompts for the LLM to find authentic local cuisine options near a traveler's hotel.
In conclusion, businesses must evaluate factors and set goals when selecting an AI approach to adopt, considering the strengths of both genAI and traditional AI to create personalized solutions.