Optimizing Travel Through AI: The Intersection of Data Quality and Customer Experience

Artificial Intelligence is ever-evolving and so are its many applications in travel, covering anything from tailor-made trip suggestions to most innovative pricing tactics to conversational agents and predictive maintenance. This is because to build AI-based effective solutions, they are highly dependent on the quality of the data being used. Bad or insufficient data can result in regrettable consequences, wasted effort, and unhappy customers. This paper explores this essential link between the role of AI and data quality in enhancing travel experiences. It highlights that data management practices play a significant role in improving AI performance in travel. The paper covers targeted dimensions, including customer-facing, supply chain booking, and operational enhancement segments, while emphasizing the need for accurate and precise data for AI computational models. Through real-world case studies, this paper proves that companies emphasizing data quality often have an advantage in AI performance. The paper also elaborates on the key challenges pertaining to massive data processing, algorithmic biases, and data privacy laws. The paper closes with suggestions on how to use better data governance and data quality control to improve AI outcomes, thereby detailing a clear path for travel companies seeking to leverage AI technologies successfully. (Abstract)