The travel industry has been no stranger to tech upheaval. Tectonic shifts over the past several decades have changed the ways that we plan, book, and experience our journeys – while also disrupting the companies that help bring those journeys to life.
NB: This is an article from McKinsey & Co.
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Given the tremendous potential of agentic AI, travel and hospitality companies are beginning to experiment with it. But to realize the technology’s full impact, organizations will need to create new AI strategies, governance, and infrastructure – altering core business processes and ways of working. Companies will have to shift from scattered pilots to enterprise-scale transformations architected by cross-functional teams and championed by deeply engaged C-suite leaders.
What does the travel industry need to know as it ponders going all in on agentic AI? How suited is this technology to the unique dynamics of the travel sector? Which consumer and non-consumer facing use cases could offer travel and hospitality players the most ROI in the near term and over the long haul? How can organizations integrate agentic AI in ways that will allow it to deliver maximum benefit to companies, workers, and consumers?
Challenges that hinder AI’s deployment in travel
Despite all this burgeoning enthusiasm and adoption, however, the travel and hospitality sectors still appear to lag behind others in terms of AI maturity. Travel companies’ AI-based efforts have largely centered on creating enterprise-wide copilots and chatbots, and such efforts have scaled quickly. But for the most part, these more horizontal initiatives have delivered diffuse, hard-to-measure gains. Vertical use cases that are function specific to this sector could be more transformative, but the vast majority of these more focused experiments have remained stuck in pilot mode.
Why has the travel industry faced challenges as it attempts to dive deeper into AI? Two possibilities emerge as central stumbling blocks:
- Siloed data and incompatible systems make using AI more difficult. The travel industry (and especially hospitality) is highly fragmented, cobbled together in part from countless small to medium-size businesses spread across nearly every country. The lack of centralized data ownership across the travel ecosystem limits the network effects and feedback loops that typically accelerate AI performance. As a result, it can be exceptionally challenging for these companies to train effective AI models or to deliver personalized, real-time, AI-powered experiences at scale.
- Travel companies tend to favor investment in human interconnection instead of tech innovation. Some of the travel sector’s wariness regarding AI might be attributed to the industry’s general view that it specializes in service, not technology. New tech capabilities are often seen as enablers but not as core business components. As a result, tech talent and tech investment can lag behind.
