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Hotels and Airlines Are Investing in AI, But Not Where It Counts

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  Published in Stocks and Investing on Thursday, October 23rd 2025 at 6:49 GMT by Newsweek
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Hotels and Airlines Are Investing in AI—but Not Where It Counts

The travel and hospitality sectors have been swept up in the AI boom, pouring millions into machine‑learning platforms, chatbots, and predictive analytics tools that promise a more personalized guest experience. Yet, a closer look at the industry’s spending patterns reveals a misalignment: the technology is largely being deployed for marketing and customer‑service tasks, while the areas that could deliver the biggest operational savings and safety gains are left largely untouched.

Front‑of‑House Fads vs. Back‑End Brilliance

A recent analysis of Fortune 500 hospitality and airline companies shows that up to 80 % of AI budgets are earmarked for “front‑of‑house” initiatives. For hotels, this includes AI‑powered concierge chatbots that answer booking questions, predictive pricing tools that recommend rates to sales teams, and digital twins that showcase rooms before guests arrive. Airlines, on the other hand, are investing heavily in customer‑facing AI such as virtual assistants that guide passengers through check‑in and in‑flight entertainment options.

However, these investments do little to address the core operational challenges—crew scheduling, maintenance planning, fuel optimization, and yield management—that have traditionally been the main drivers of cost and revenue in the industry. The article highlights that while the average hotel spends roughly $1 million on AI each year, less than 5 % of that is directed toward data‑driven maintenance scheduling or energy‑management systems.

The Case of Marriott and Delta

Marriott’s “Smart Room” initiative, which uses sensors and machine‑learning models to auto‑adjust temperature, lighting, and even TV content, is often cited as a flagship AI project. Yet, behind the glossy promotional videos lies a system that largely operates in isolation from the hotel’s central property‑management system, limiting its potential to reduce energy costs across an entire chain.

Delta Airlines, meanwhile, has unveiled a proprietary AI tool that predicts flight delays and suggests alternative itineraries to passengers. While this improves customer satisfaction, the same technology could be applied to crew rostering and real‑time gate allocation—a use case that Delta has not yet fully explored.

Why the Gap Exists

The article attributes the disparity to a few key factors:

1. Data Silos: Hotels and airlines often maintain fragmented data ecosystems. Customer‑behavior data lives in one system, while operational data—fuel consumption, flight logs, HVAC usage—resides in another. Integrating these disparate data sources to train meaningful AI models is a formidable technical hurdle.

2. Regulatory Constraints: Airlines operate under strict safety regulations that demand rigorous testing and certification before deploying any new technology. Even if an AI algorithm can optimize fuel burn, regulatory bodies will require extensive evidence that the system will not compromise safety.

3. Short‑Term ROI Focus: Marketing and customer‑service AI projects deliver tangible, short‑term revenue lift through upselling and cross‑selling. In contrast, operational AI improvements, such as reducing maintenance downtime or optimizing crew schedules, often require a longer investment horizon and complex change‑management initiatives.

4. Talent and Expertise: Building advanced AI solutions for yield management or predictive maintenance requires specialized talent that many airlines and hotel chains lack. Consequently, companies lean on off‑the‑shelf AI vendors that focus on customer‑experience use cases.

Emerging Opportunities

Despite these challenges, there are growing indications that the industry is beginning to shift its focus. The article notes that a handful of airlines—such as Southwest and Alaska Airlines—are piloting AI systems that feed directly into revenue‑management engines, adjusting fares in real time based on demand forecasts. Hotels are also experimenting with AI‑driven dynamic pricing that considers local events, weather patterns, and competitor rates.

Moreover, sustainability has become a new driver for AI adoption. Airlines are deploying machine‑learning models to predict the most fuel‑efficient flight paths, while hotels are using AI to balance energy consumption with guest comfort. These initiatives not only reduce carbon footprints but also deliver significant cost savings, aligning with the growing consumer expectation for “green” travel.

Looking Ahead

The article concludes that the true potential of AI in the hospitality and airline industries lies in unlocking operational efficiencies and safety improvements. While front‑of‑house AI will continue to be a high‑profile investment, companies that successfully integrate data across systems, comply with stringent regulatory standards, and cultivate internal AI talent stand to reap the most substantial rewards.

Ultimately, the question is no longer whether hotels and airlines will adopt AI, but how they will evolve from marketing‑centric pilots to data‑driven operational engines that reduce costs, enhance safety, and deliver a truly differentiated experience to travelers.