Join Andrew Rubinacci, EVP Commercial & Revenue Strategy Aimbridge Hospitality, and Jason Bryant, VP Nor1 at Oracle Hospitality to hear a conversation about how hoteliers can use AI to drive revenue and streamline operations.

AI, and more specifically machine learning, is the bridge between data and decisioning. Hotels have plenty of data in plenty of disparate and fenced applications. Determining which data is worth the cost of extraction and aggregation is the first step to decisioning; applying machine learning to the data to find its value and identify offers that will generate material new revenue is the goal.

Machine learning can transform revenue management, applying automation to mundane tasks that hinder the ability to create meaningful revenue strategy. And that automation adds a layer of intelligence that provides coherency across the guest journey, from booking to check-out. Because revenue strategy shouldn't end at booking.

Segments of the hospitality industry are differently situated to take advantage of AI. Global brands have scale but are sometimes faced with the constraints of franchise agreements and conflicts between stakeholders. Owners are risk-takers and can move quickly, at a smaller scale but with more of the business under their control. Are independent properties driving innovation?

And what about attribute-based selling? Nor1 has been upselling attributes like high floors, views, cabanas, and parking for 15 years, but applying ABS in the booking engine is trickier. Unlike airlines, hotels actually have attributes guests want, but do guests really want to book a hotel by attribute? Will it materially increase RevPAR and guest satisfaction, and will the industry see a return on the technology spend to build ABS?

If other industries are any indication, the hospitality industry at the beginning of this curve but it's going to accelerate, and the evolution will be fast.

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