为何旅游业需要分层的 AI 采用，而不是追求自主的竞赛

在旅游业的 AI 讨论中，往往聚焦于技术如何快速改变行业并带来高影响力的成果。然而，现实更为微妙。虽然 AI 已在个性化、客服和运营效率等领域展示出实在价值，但并非所有人都以相同的速度采用它。其主要原因在于复杂的技术生态系统。

航空公司、在线旅游平台（OTA）和旅游管理公司（TMC）在数十年间构建的互联网络中运营。许多企业仍依赖碎片化的数据环境和传统基础设施，这限制了 AI 的部署和扩展速度。对透明度、问责制和可靠性的担忧进一步加剧了这一问题。

While most travel companies talk about AI as a single transformation story, its adoption is unfolding across three distinct yet interconnected layers: (i) progress in customer-facing and operational automation, (ii) friction created by legacy infrastructure, and (iii) the lack of institutional trust. Each is advancing at a different pace, creating unique dynamics that demand tailored responses. Understanding these differences provides a more practical framework for identifying where the greatest opportunities (and risks) are likely to emerge.

1. 评估进展

Whether searching for flights, managing itineraries, or resolving disruptions, travelers increasingly expect every interaction to be seamless, personalized, and responsive. Gen AI assistants are helping by streamlining trip planning and customer support. In 2025, almost 40% of US travelers used Gen AI to plan trips. At the same time, machine learning models are enabling hyper-personalized offers based on traveler behavior, loyalty preferences, and purchasing history.

AI 还在幕后创造了巨大的价值。旅游公司利用高级分析更好地预测需求、管理容量、强化人力规划并处理中断。例如，一家 大型旅游服务提供商 通过生成式 AI 将每笔预订成本降低了 10%（同比），而一家 加拿大航空公司 报告称，通过 AI 驱动的定价实现了单位收入提升 2% 和网络驱动收入增长 10%。

然而，尽管 AI 可提供的实在价值证据日益增多，传统系统仍是广泛采纳的重大障碍。

2. 解决摩擦

The industry’s legacy infrastructure was not designed to support the real-time, unified data pipelines that AI requires. At the center of the problem is the Global Distribution System (GDS). GDS platforms were architected several decades ago on EDIFACT messaging protocols and still account for the dominant share of indirect airline sales globally. Integrating New Distribution Capability (NDC) with a legacy Passenger Service System (PSS) can take months of testing and development, particularly for airlines offering multiple fare brands or ancillary products. The challenge spans contractual restrictions on content distribution, organizational readiness, and the absence of standardized data across regional markets.

端到端数据可视性的缺失进一步限制了组织有效扩展 AI 的能力。 一项 GBTA 调查 显示，只有 12% 的企业旅行采购者拥有其项目数据的统一视图，这一基础性限制无论模型多么复杂都限制了 AI 系统的交付能力。

航空公司、OTA 和 TMC 正通过采用混合策略，在现有系统上叠加智能，使用可代理的 API 来应对这一挑战。与此同时，原生 NDC 的参与者则采取不同路径，将 AI 驱动的服务和政策合规直接构建进其架构，减少对传统 GDS 渠道的依赖。

但仅靠技术集成并不能保证成功。随着 AI 越来越深入并开始影响更高风险的决策，下一个挑战随之出现：信任。

3. 推进信任

Research from GBTA shows that while 92% of travel buyers are interested in AI-driven spend forecasting and 89% in automated disruption management, only 57% of the same buyers are comfortable with AI autonomously changing or canceling bookings. This contrast highlights a fundamental trust gap.

旅游业领袖正在寻求可解释、可审计的解决方案。同时，客户日益要求透明度和问责制。在多元利益相关方之间建立信任，需要承诺“负责任的演进”，在创新与透明治理之间取得平衡。

这意味着进展最大的组织不一定是部署最先进模型或发展最快的公司。相反，它们遵循结构化方法：在扩展 AI 之前夯实数据基础，在受控且高影响力的环境中验证使用案例，并在转型旅程中嵌入 比例治理。与统一治理不同，比例治理将 AI 代理组织为不同自主水平，每个水平都有明确的信任边界和治理要求。

将 AI 抱负转化为持续影响

For travel organizations moving toward the next phase, embracing this sequential approach is no longer optional; it is what separates leaders from laggards. In this emerging context, business process management partners play a critical enabling role by helping organizations operationalize AI across complex ecosystems of data, processes, and human decision‑making. Ultimately, organizations that translate disciplined execution into a durable, hard‑to‑replicable advantage will emerge as winners.