22 Hotels Can't Outperform 4 Cable Cars: Can AI Reshape Mountain Scenic Area Economics?

A Surprising Set of Numbers

A glance at Huangshan Tourism's financial statements reveals a counterintuitive fact: the annual revenue of the group's 22 hotels falls short of the income generated by just 4 cable car lines. With extremely low marginal costs and ultra-high gross margins, the cable car business has become the most profitable "money-printing machine" across the entire scenic area, while the hotel business struggles to survive between high operating costs and low per-customer spending.

This is not a predicament unique to Huangshan — it is a common portrait of mountain-type scenic areas across China. As the traditional "tickets plus cable cars" model hits its ceiling, a core question confronts all industry practitioners: Can AI and digital technology help mountain scenic areas find a second growth curve?

Why Do Cable Cars Crush Hotels? The Underlying Logic of Structural Imbalance

The cable car business is essentially built on "rigid demand + monopoly + low marginal cost." Visitors climbing the mountain almost always need to take the cable car. Pricing power rests firmly with the operator, and once a cable car system is built, the marginal cost of transporting each additional passenger approaches zero, keeping gross margins consistently above 80%.

By contrast, hotel operations in mountain scenic areas face multiple pressures:

• Extreme seasonality: Mountain scenic areas like Huangshan see vast differences in visitor volume between peak and off-peak seasons, leaving hotels largely vacant during slow periods.
• Rigid operating costs: Logistics for supplies and labor maintenance at mountaintop hotels are far more expensive than at the base.
• Homogeneous experiences: Most mountain hotels remain at the basic service stage of "just having beds," making it difficult to justify premium pricing.
• Narrow visitor demographics: The primary audience consists of sightseeing day-trippers with limited overnight demand.

In short, cable cars are an infrastructure business that "earns passively," while hotels are a service business characterized by "heavy assets, heavy operations, and low returns." This structural imbalance is nearly impossible to reverse through traditional management approaches.

AI Intervention: Smart Cultural Tourism Is Rewriting Scenic Area Economics

Over the past two years, AI technology has noticeably accelerated its penetration into the cultural tourism industry. From visitor flow forecasting to dynamic pricing, from personalized recommendations to virtual-physical hybrid experiences, AI is restructuring the value chain of mountain scenic areas across multiple dimensions.

1. Intelligent Visitor Flow Scheduling to Solve the "Tidal Effect"

One of the biggest headaches for mountain scenic areas is extreme fluctuation in visitor flow — overcrowded on holidays, deserted on weekdays. AI-based visitor flow prediction systems have already been deployed at some scenic areas. By integrating historical visitor data, weather forecasts, social media trends, OTA search indices, and other multidimensional information, AI models can predict visitor volumes up to 72 hours in advance with accuracy rates exceeding 90%.

This means scenic areas can adjust cable car capacity, hotel supplies, food service provisions, and security staffing in advance, reducing operational waste while improving the visitor experience. Huangshan has already introduced similar systems in recent years for peak-period crowd control and time-slot reservations.

2. Dynamic Pricing and Revenue Management to Unlock Dormant Hotel Value

Revenue Management, long standard in the airline and chain hotel industries, remains virtually nonexistent among mountain scenic area hotels. AI-driven dynamic pricing systems can automatically adjust room rates based on real-time supply-demand dynamics, competitor pricing, weather changes, and other factors — maximizing revenue during peak seasons and boosting occupancy through flexible discounts during off-peak periods.

Going further, AI can bundle hotels with cable cars, dining, and entertainment into personalized packages, precisely recommended based on user profiles. For example, photography enthusiasts might receive a "sunrise room + early cable car" combo, while families could be offered a "stargazing camping + nature education course" experience package. This shift from "selling rooms" to "selling scenarios" is the key to breaking through the low-margin trap in the hotel business.

3. AIGC Reshaping Destination Marketing

The explosion of large language models and AIGC (generative AI) technology is transforming how scenic areas produce content and conduct marketing. A promotional video that once cost hundreds of thousands of yuan and took weeks to produce can now be replaced with AI-generated video, AI copywriting, and AI virtual hosts, enabling scenic areas to mass-produce personalized marketing content at minimal cost.

Some scenic areas have already begun using AI to generate virtual tour videos from different seasons and perspectives for targeted distribution on social platforms. Huangshan's iconic scenes — "sunrise over the sea of clouds" and "snow-covered ancient pines" — are naturally suited for AI-powered visual content remixing and viral distribution.

4. Digital Twins and Virtual-Physical Hybrid Experiences

More cutting-edge exploration lies in the application of digital twin technology. By creating high-precision 3D models of entire mountain bodies and combining them with AR/VR technology, scenic areas can offer visitors immersive experiences that blend the real and virtual. For instance, scanning with a smartphone at a specific viewing platform could reveal AI-reconstructed scenes of ancient scholars ascending the mountain, or AR "X-ray vision" could display the full panorama of mountain ranges beneath the clouds on foggy days.

These experiences not only extend visitor dwell time and spending willingness but also create entirely new paid scenarios, breaking the single-point dependency on cable car tickets.

The Difficulty of Transformation: Real-World Challenges Beyond Technology

Although the picture AI paints is exciting, the digital transformation of mountain scenic areas still faces considerable real-world obstacles:

• Weak infrastructure: Network coverage, computing power deployment, and sensor installation all face special difficulties in high-altitude areas.
• Severe data silos: Cable car, hotel, dining, and ticketing systems often operate independently; data integration is a prerequisite for AI to deliver value.
• Talent and awareness gaps: Traditional scenic area management teams have limited understanding and application capability regarding AI; digitalization often stops at "installing systems" rather than "using data."
• Institutional constraints: Many mountain scenic areas involve state-owned asset management, nature reserve regulations, and other complex factors, leaving limited room for innovation and trial-and-error.

Outlook: AI Transformation for More Mountain Scenic Areas Has Only Just Begun

The reality that Huangshan Tourism's "22 hotels can't match 4 cable cars" is fundamentally a signal that the growth model for traditional resource-based scenic areas has peaked. As visitor flow dividends fade and ticket-based economics hit their ceiling, only by using technology to restructure the relationship between "people, products, and places" can new value be unlocked.

Nationally, leading mountain scenic areas including Mount Tai, Mount Hua, Zhangjiajie, and Mount Emei are all accelerating their smart tourism initiatives. China's Ministry of Culture and Tourism has also issued multiple directives in recent years encouraging scenic areas to adopt AI, big data, digital twins, and other technologies to enhance management efficiency and visitor experiences. It is foreseeable that AI will be not just an "efficiency tool" for mountain scenic areas but a "strategic variable" that redefines their business models.

The transformation of more mountain scenic areas has only just begun. Whether AI can truly help "hotels outrun cable cars" — the answer may emerge within the next three to five years.