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Agentic AI: What It Is, How It’s Different, and Why It Matters in Hospitality

When we say “AI agents,” we're not talking about the suit-wearing, memory-wiping types from Men in Black, but these new agents might be just as transformative. In the world of hotel tech, AI agents are emerging as intelligent, task-driven assistants that work behind the scenes to simplify operations, boost efficiency, and create better guest experiences. Instead of battling aliens, these agents are here to tackle fragmented systems, reduce manual workloads, and unlock a smarter, more connected future for hospitality. And unlike the old “app-for-everything” approach, agentic AI offers a more agile, scalable way to run your hotel.

By Henri Roelings, Founder, Hospitality Net
Explainer

Three Real-World Examples of How AI Can Streamline Hospitality Workflows

Over the last few years, the two things the hospitality industry has learned to count on are uncertainty and change. As a result, organizations need adaptability and resilience to navigate this highly dynamic world, and their people are the foundation for both. As environments change, so do business priorities, and effective organizations will position their people to adapt on the fly.

Jason Bryant
By Jason Bryant, Vice President Strategic Growth Initiative, Oracle Hospitality
Explainer

Generative AI and Neural Net Fundamentals

There are two answers to the question of how generative AI models work. Empirically, we know how they work in detail because humans designed their various neural network implementations to do exactly what they do, iterating those designs over decades to make them better and better. AI developers know exactly how the neurons are connected; they engineered each model’s training process. Yet, in practice, no one knows exactly how generative AI models do what they do—that’s the embarrassing truth.

Jason Bryant
By Jason Bryant, Vice President Strategic Growth Initiative, Oracle Hospitality
Explainer

Machine Learning Basics for Hotel and Correlation versus Causation Modeling

There are many definitions of machine learning (ML). For purposes of this explainer, ML is the scientific study of algorithms and statistical models that computer systems automatically use in real-time to effectively perform a specific objective function (such as optimizing revenue) without using explicit instructions. Instead, ML relies on patterns and inference. All this is performed using a feedback loop so that each successive iteration further increases precision of the models, which drives and improves the objective function.

Jason Bryant
By Jason Bryant, Vice President Strategic Growth Initiative, Oracle Hospitality
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