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#数据治理

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Aug 4Tue
  1. Hospitality Technology

    Your Hotel Is Invisible on LLMs, But Not for the Reason You Think

    A benchmark study by Americas Great Resorts (AGR) reveals serious issues with AI engines recommending hotels. The study tracked 824 recommendations across ChatGPT, Gemini, and Google AI Mode in six major U.S. markets, finding that AI recommendations are highly concentrated, with an average of just five hotels taking half of all recommendations. More critically, AI recommendations include closed hotels, such as the demolished Mandarin Oriental in Miami. The study indicates that AI search relies on document authority rather than entity facts, leading to stale information. It suggests hotel IT and marketing co-own data governance to optimize public entity records.

Mar 30Mon
  1. PhocusWire All News

    Ensuring AI Benefits Everyone in Travel

    A session at ITB Berlin addressed AI bias in travel. Professor Fevzi Okumus of the University of South Carolina noted that AI can disadvantage certain groups in recruitment, service recommendations, and business visibility, making it harder for smaller operators and independent providers to compete. He urged treating AI as an enabler rather than a replacement and proposed a framework to audit AI systems for transparency, fairness, and trustworthiness.

Mar 18Wed
  1. PhocusWire All News

    What 'AI-native' really means in travel

    AI-native has become a buzzword in travel, with companies like Sabre, Airbnb, and Riyadh Air using it to describe their initiatives. However, experts argue that true AI-native requires ground-up architecture and robust data governance. Phocuswright research shows 28% of travel executives rank generative AI as their top tech investment priority, but only 13% allocate over 20% of tech budget to AI implementation.

Mar 17Tue
  1. PhocusWire All News

    Two operational priorities that define AI success

    Phocuswright research highlights two operational priorities for translating AI strategy into reality: establishing custom evals and strengthening data governance. Custom evals are structured processes to test AI system quality, performance, and reliability; generic benchmarks are insufficient. Data governance has shifted from back-office to strategic imperative, as AI reliability depends on data quality.