AI Efficiency Is the New Hospitality Currency

Artificial intelligence has become an inevitability in the modern business world to the point that virtually any business that hasn’t embraced it is quickly being left behind. AI is integral to the operations of all industries, especially hospitality. From streamlining customer complaints to ensuring proper inventory management, efficient AI has become the cornerstone of customer satisfaction. 

With increasing demands for efficiency, cost control, and, of course, improved customer experiences, businesses in this industry are continuing to leverage AI for real-world solutions. Over the past few years, advancements in AI efficiency have transformed the hospitality industry, providing companies with new opportunities to enhance operational efficiency, refine pricing strategies, and ultimately offer better guest experiences. 

The shift to smaller, more efficient AI models

As is the case with most technological advancements, especially in their early stages, AI has grown in efficiency and usability, getting larger before it gets smaller. AI models have grown in size and complexity, with larger models outperforming the smaller ones. 

In 2024, however, there was a major shift where smaller models significantly outperformed larger systems. Suddenly, compact AI models, which are one-hundredth the size of earlier models, began demonstrating similar capabilities in tasks such as natural language processing and decision-making.

The rise of these more efficient models is a game-changer for hospitality companies. Smaller models require less computing power, making them more cost-effective to deploy. They can also be run on local devices, reducing the need for expensive cloud infrastructure. 

For example, a hotel could run an AI model to manage its guest services locally, rather than relying on external data centers, allowing for faster response times, reduced latency, and lower operational costs. All of these are essential factors for industries like hospitality, where efficiency and customer satisfaction are paramount. 

Industry-led AI development: Meeting real-world needs

Historically, academia led AI research, but in recent years, the focus has shifted. Today, nearly 90% of AI models are developed by companies driven by the need for practical, industry-specific solutions. For hospitality businesses, this shift means that AI tools are becoming more tailored to the sector’s unique challenges, such as dynamic pricing, customer service automation, and operational efficiency.

As AI development moves from the lab to the corporate world, businesses in hospitality now have access to more immediately applicable AI solutions designed to address real-world operational needs, from optimizing revenue management systems to personalizing guest experiences in real time. Industry-driven AI models offer faster deployment times and more relevant features, allowing companies to quickly and efficiently improve their services. This capability is crucial in the hospitality industry, where customer expectations are continually evolving, and businesses must adapt rapidly to remain competitive.

The rise of open-weight AI models

Closed AI models, which are proprietary and often locked behind paywalls, have long dominated the market. However, open-weight AI models are gaining traction, offering a viable alternative because they are publicly accessible and customizable, allowing businesses to modify them to suit their specific needs. By early 2025, the performance gap between open-weight and closed AI systems had narrowed significantly, making open-weight models a practical choice for many businesses, including those in the hospitality industry.

The key advantage of open-weight models lies in their flexibility. Hospitality brands can host these models on their own servers, maintaining control over their data and ensuring compliance with relevant data protection and privacy regulations. 

Additionally, these models can be retrained regularly, allowing businesses to adjust to new trends or customer preferences without relying on expensive third-party vendors. For hospitality companies, this flexibility reduces costs, enhances security, and provides greater control over AI applications, from customer service chatbots to predictive analytics for guest behavior.

The economic benefits of AI efficiency in hospitality

As AI systems become more efficient, hospitality businesses are seeing direct financial benefits. Smaller, more efficient AI models reduce the need for costly infrastructure and cloud services, while open-weight models allow for more cost-effective customization, eliminating the need for expensive, proprietary solutions. These cost savings can be reinvested in other areas of the business, such as amplifying the customer experience, bolstering employee training, or adopting new technologies.

For example, AI-driven systems can help optimize pricing in real-time, leading to more dynamic and competitive rates that maximize revenue without requiring manual adjustments. AI can also streamline operations, from automating customer service inquiries to optimizing kitchen schedules, reducing overhead costs while improving service quality. The cumulative effect of these efficiency gains can lead to healthier margins and better overall business performance.

The advancements in AI efficiency are reshaping the hospitality industry. With the rise of smaller models, industry-driven AI solutions, and the growing adoption of open-weight systems, hospitality businesses now have more tools at their disposal to boost efficiency, cut costs, and enhance customer experiences. As AI continues to evolve, embracing these advancements will be critical for staying competitive in a rapidly changing market.

By leveraging AI’s growing efficiency, hospitality brands can not only improve operational performance but also create more personalized experiences for guests, ensuring they stay ahead of the curve in a highly competitive industry.

About the Author

Dev Nag is the CEO/Founder of QueryPal. He was previously on the founding team at GLMX, one of the largest electronic securities trading platforms in the money markets, with over $3 trillion in daily balances. He was also CTO/Founder at Wavefront (acquired by VMware) and a Senior Engineer at Google, where he helped develop the back-end for all financial processing of Google ad revenue. He previously served as the Manager of Business Operations Strategy at PayPal. He also launched eBay's private-label credit line in association with GE Financial.