AI Shouldn’t Give Hotels More Answers. It Should Help Us Make Better Decisions
Artificial intelligence is expanding hotels’ ability to analyse information, but more answers do not automatically lead to better decisions. This article argues for moving beyond self-service analytics towards decision-focused analytics: distinguishing description, prediction and causality; governing metrics; testing hypotheses; and adding human review when the cost of error is high. The real value of AI in hospitality is not producing more reports, but improving how we make decisions on pricing, distribution, guest experience, operations, talent and profitability.
For years, in Hospitality, we have pursued an apparently unquestionable aspiration: having more information to manage our hotels better. We have multiplied reports, introduced dashboards, connected distribution channels, analysed reviews, compared rates, segmented guests and turned virtually every guest interaction into potentially measurable data. Yet, after all that effort, I continue to observe an uncomfortable paradox: having more information does not necessarily guarantee that we make better decisions.
The arrival of artificial intelligence puts us once again before the same promise, although this time with far greater responsiveness. We can now ask about ADR trends, the causes of a drop in conversion, recurring comments about breakfast, a department’s productivity or the likelihood of a booking being cancelled. We can receive answers in seconds, accompanied by impeccable charts and convincing explanations. But the speed of an answer does not determine its usefulness, just as a visually attractive report does not automatically turn a correlation into a business truth.
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