Transformed hospitality experiences with AI-driven personalization and intelligent automation on AWS

The hospitality industry is seeing a shift towards intelligent, AI-powered automation that optimizes revenue, enhances guest experiences, and streamlines operations. Traditional models rely on static forecasting and manual adjustments, limiting agility in a fast-changing market.

With AWS-powered Generative AI, hotels can dynamically adjust pricing, predict demand with accuracy, and personalize guest interactions at scale—boosting profitability, improving operational efficiency, and redefining customer engagement in real-time.

Customer overview

With a legacy rooted in hospitality, our customer has grown from a single motel into a global leader in the industry. Built on a foundation of exceptional service, they have expanded their portfolio to over 1,300 hotels and all-inclusive properties across 76 countries and six continents. Committed to elevating guest experiences through innovation, they continue to redefine modern hospitality by integrating cutting-edge technology and AI-driven personalization into their operations.

Challenges | From data complexity to actionable insights

The hospitality sector generates vast amounts of customer, booking, and operational data, yet without AI-driven intelligence, much of this potential remains untapped. To stay competitive, hotels needed an AI-powered solution capable of real-time demand forecasting, dynamic pricing, and hyper-personalized guest experiences at scale.

• Inaccurate demand forecasting – Rigid models led to inconsistent room allocation and revenue loss.
• Inefficient pricing strategies – Static strategies failed to capture real-time market fluctuations.
• Limited guest personalization – Lack of AI-driven insights restricted tailored guest experiences.
• High customer service costs – Manual guest service processes increased overhead and slowed response times.

Solution | AI-powered demand forecasting, pricing, and guest engagement

We developed a cloud-native AI solution leveraging AWS to enable real-time demand forecasting, dynamic pricing, and personalized guest interactions. The system integrates machine learning, predictive analytics, and conversational AI, allowing hotels to automate revenue optimization and enhance guest engagement at scale.

AI-driven hospitality infrastructure powered by AWS

To enhance real-time demand forecasting, dynamic pricing, and AI-powered guest interactions, we implemented a Generative AI-driven Retrieval Augmented Generation (RAG) framework on AWS, enabling hotels to make data-driven decisions at scale.

• Optimized demand forecasting: Integrated Amazon SageMaker DeepAR to predict occupancy trends, improving forecasting accuracy by 30%.
• AI-driven dynamic pricing: Implemented SageMaker XGBoost to adjust room pricing based on demand fluctuations, increasing revenue by 25%.
• AI-powered guest interactions: Deployed Amazon Lex & Amazon Bedrock to automate multilingual guest support, reducing customer service costs by 30%.
• Personalized guest experiences: Leveraged Amazon Personalize for AI-driven recommendations, improving guest engagement and satisfaction.
• Sentiment analysis for guest feedback: Used Amazon Comprehend to analyze reviews, enabling proactive service improvements.
• Retrieval-augmented generation (RAG) for AI-driven responses: Integrated AWS OpenSearch to enhance chatbot interactions with real-time, context-aware recommendations.

Success metrics

We assisted our customer in transforming hospitality operations with AWS-powered Generative AI, automating demand forecasting, dynamic pricing, and guest personalization, delivering significant improvements across core business metrics.

• Reduced operational costs by 30% – Automated guest interactions and service workflows led to higher efficiency and lower overhead.
• Increased revenue by 25% – AI-driven dynamic pricing maximized occupancy rates and optimized revenue streams.
• Improved guest retention by 40% – Personalized recommendations and proactive engagement enhanced customer loyalty and repeat bookings.
• Optimized resource allocation by 30% – More accurate demand forecasting reduced over/underbooking risks, ensuring better financial planning.