यात्रा के लिए परतदार AI अपनाने की आवश्यकता क्यों है, न कि स्वायत्तता की दौड़

यात्रा में AI पर चर्चा अक्सर इस बात पर केंद्रित रहती है कि तकनीक कैसे तेजी से इस क्षेत्र को बदल सकती है और उच्च-प्रभावी परिणाम दे सकती है। वास्तविकता, हालांकि, अधिक सूक्ष्म है। जबकि AI ने व्यक्तिगतकरण, ग्राहक सेवा और परिचालन दक्षता जैसे क्षेत्रों में ठोस मूल्य प्रदर्शित किया है, सभी लोग इसे समान गति से अपना नहीं रहे हैं। इसका एक प्रमुख कारण जटिल तकनीकी इकोसिस्टम है।

एयरलाइन, ऑनलाइन ट्रैवल एजेंसियां (OTAs) और ट्रैवल मैनेजमेंट कंपनियां (TMCs) दशकों में निर्मित परस्पर जुड़ी नेटवर्क पर काम करती हैं। कई अभी भी टुकड़े‑टुकड़े डेटा वातावरण और लेगेसी इन्फ्रास्ट्रक्चर पर निर्भर हैं, जो AI को तैनात करने और स्केल करने की गति को सीमित करता है। पारदर्शिता, उत्तरदायित्व और विश्वसनीयता के बारे में बढ़ती चिंताएँ इस समस्या को और बढ़ा देती हैं।

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. Assessing Progress

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 पर्दे के पीछे भी अत्यधिक मूल्य बना रहा है। यात्रा कंपनियां उन्नत विश्लेषण का उपयोग करके मांग का बेहतर पूर्वानुमान, क्षमता प्रबंधन, कार्यबल योजना को सुदृढ़ और व्यवधानों को संभाल रही हैं। उदाहरण के तौर पर, एक एक प्रमुख यात्रा सेवा प्रदाता ने Gen AI के साथ अपनी प्रति‑बुकिंग लागत को वर्ष‑दर‑वर्ष 10 % कम किया, जबकि एक कनाडाई एयरलाइन ने AI‑सक्षम मूल्य निर्धारण के माध्यम से इकाई राजस्व में 2 % की वृद्धि और नेटवर्क‑आधारित राजस्व में 10 % की बढ़ोतरी दर्ज की।

फिर भी, AI द्वारा प्रदान किए जाने वाले ठोस मूल्य के बढ़ते प्रमाण के बावजूद, लेगेसी सिस्टम व्यापक अपनाने में एक महत्वपूर्ण बाधा बने हुए हैं।

2. Addressing Friction

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.

The lack of end-to-end data visibility further constrains an organization’s ability to scale AI effectively.  A GBTA सर्वेक्षण revealed that only 12% of corporate travel buyers have a consolidated view of their program data, a foundational constraint that limits what any AI system can deliver, regardless of model sophistication.

Airlines, OTAs, and TMCs are navigating this by adopting hybrid strategies that layer intelligence onto existing systems using agentic-ready APIs. Meanwhile, NDC-native players are taking a different approach, building AI-driven servicing and policy compliance directly into their architecture and reducing reliance on traditional GDS channels.

But technological integration alone does not guarantee success. As AI becomes more deeply embedded and begins to influence higher-stakes decisions, the next challenge emerges: trust.

3. Advancing Trust

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.

Travel leaders are looking for explainable, auditable solutions. Meanwhile, customers increasingly demand transparency and accountability. Building trust across diverse stakeholder groups requires a commitment to ‘responsible evolution,’ balancing innovation with transparency and governance.

This means the organizations making the greatest progress are not necessarily those deploying the most advanced models or moving the fastest. Rather, they are the ones following a structured approach: strengthening data foundations before scaling AI, validating use cases in controlled, high-impact environments, and embedding अनुपातिक शासन into the transformation journey. Unlike uniform governance, proportional governance organizes AI agents into levels of autonomy, each with defined trust boundaries and governance requirements.

Turning AI Ambition into Sustained Impact

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.