"Scenic-area AI is not to build a smart guide device, but to become a cultural translator and service backfill."
While most scenic areas still chase surface-level intelligence like "face-recognition entry" and "AR tours," Huangshan Tourism's fully deployed AI travel agent in 2025 answered the deep question of cultural-tourism digitalization with business results—99.9% Q&A accuracy, growth in secondary-consumption revenue, and a drop in complaint rate. This "trinity" agent—led by Huangshan Tourism, developed by Tuma Technology, with the technology base provided by Ant Group—not only solved the scale problem of "high-volume service pressure" but also rebuilt the interaction logic of "people – scenery – culture": it proves the essence of cultural-tourism AI is to use technology to fill the industry gaps of "cultural-transmission efficiency" and "experience certainty," not a simple tool iteration.
01 The First Principles of the Cultural-Tourism Industry: Selling Cultural Experience
The first principle of cultural tourism has never been "selling tickets" but "selling cultural experience"—that is, cultural transmission and scenario experience. But under the traditional model, this experience is always constrained by "three irreconcilable" contradictions, and that is exactly where Huangshan's AI agent breaks through.
1. The Contradiction Between the "Subjectivity" and "Standardization" of Cultural Transmission
When tourists visit Huangshan, they see not only the strange pines and rocks but the cultural depth behind the "Four Wonders of Huangshan"—yet traditional guides' commentary quality highly depends on individual experience: some can explain "the age and protection history of the Guest-Greeting Pine," others only say "photo spot"; Chinese commentary is passable, but foreign-language service is a gap. This "experience-dependent" transmission causes significant cultural-information decay, and the shallow experience of "seeing only a mountain" is widespread.
2. The Contradiction Between the "Spatio-Temporal Limits" and "Demand Immediacy" of Service Coverage
The biggest feature of cultural-tourism demand is its "timelessness": a tourist feeling sudden illness at 3 a.m. needs medical guidance, a cable-car stoppage in a storm needs instant notice, a weekend-peak visitor unable to find a restroom needs navigation—yet traditional manual service always has "blind spots." In 2024, the Huangshan scenic area received 5.568 million visits in total; the huge flow put enormous pressure on service, and many complaints centered on "slow response" and "can't find staff."
3. The Contradiction Between "Scale" and "Personalization" in Experience Design
Huangshan receives over 5 million visits a year, yet traditional service can never escape "one size fits all": uniform guide scripts, fixed routes, indiscriminate product recommendations—while tourist needs are already tiered: families need "fun interaction," photography lovers need "best vantage points," cultural scholars need "deep research." This mismatch between "scaled supply" and "personalized demand" leads to short stays and low secondary-consumption rates, and the cultural-tourism industry widely shows the "crowds but no revenue" phenomenon.
02 Huangshan's AI Agent: From All-Purpose Tool to Precise Backfill
The smartness of Huangshan's AI agent is that it did not fall into "big-and-complete" technology showmanship, but made each function correspond to a concrete business pain point, while leaving flexible room for "human-machine collaboration."
1. Knowledge Closed Loop: From "Fuzzy Experience" to "Traceable Cultural Library"
Unlike general large models that are "broad but not precise," Huangshan's AI agent adopts a "vertical deep-cultivation + dynamic iteration" strategy. The R&D team of Anhui Tuma Technology, a subsidiary of Huangshan Tourism Development Co., Ltd., adapted to local conditions—selecting its own large-model dev tools, vector database, and deployment plan—to build a vertical technical system suited to Huangshan's tourism scenarios, significantly raising the model's precision in calling the Huangshan cultural-tourism knowledge base.
2. Human-Machine Collaboration: The Golden Division of "Machines Ensure Efficiency, Humans Deliver Warmth"
Huangshan deliberately avoids the misconception of "AI replacing humans," instead drawing the line between "what machines excel at" and "what is uniquely human," building a "collaborative enhancement loop." AI's value is not "replacing humans" but being a "24/7 backfill that never clocks out." Through "LBS positioning + real-time data sync," Huangshan's AI agent delivers "scenario-triggered service."
Q1 2025 data shows Huangshan's AI travel assistant has served over 120,000 users and answered over 900,000 consultations, markedly optimizing service capacity and sharply cutting manual-response pressure. AI takes over large amounts of repetitive work, equivalent to freeing substantial full-time staff—who were not laid off but reskilled into higher-value roles such as "AI trainer" and "cultural-experience specialist."
3. Creating Value: Intelligent Scenario Recognition That "Drives Surrounding Consumption"
Huangshan scenic area's "tickets valid within 3 days" policy aimed to drive surrounding consumption and raise revenue via longer stays, but the effect was poor. For tourists, redundant information is hard to verify and itinerary uncertainty blocks quick decisions. Huangshan's AI agent breaks through with "tag-based precise matching": using visitor profiles authorized via the WeChat mini-program, combined with real-time location, it generates customized plans.
Since Huangshan's agent launched, the system has driven over RMB 5 million in additional transaction volume by funneling visitors to on-mountain and surrounding lodging and dining. After the "tickets valid within 3 days" policy, non-ticket revenue's share rose from 35% to 58%, bringing growth to mountain B&Bs, specialty dining, and other formats.
03 Lessons from the Case: How to Achieve Breakthrough Growth in Cultural Tourism with AI?
The core of cultural tourism is "cultural transmission and scenario experience." Huangshan AI's approach is to "focus on its own strengths": not chasing parameter scale, but cultivating "Huangshan-specific data" and using a vertical technical system to raise the model's precision in calling the Huangshan cultural-tourism knowledge base. This shows: cultural-tourism AI's competitiveness lies not in "model size" but in "data relevance and authority."
"AI will take guides' jobs" is a common industry anxiety, but Huangshan's practice proves technology can actually make "human value" stand out more. After AI takes over repetitive work, guides can transform from "information deliverers" into "cultural-experience designers." This "human-machine collaboration" logic suits "high-emotion + high-professional" industries like cultural tourism especially well: machines solve "efficiency and standardization," humans solve "emotion and personalization," and their synergy far exceeds either alone. Good cultural-tourism AI should make visitors rely more on people, not more on machines.
The future competition in cultural tourism is no longer a contest of "ticket price" but of "experience depth": only when AI becomes the "translator of culture," the "backfill of service," and the "designer of experience" can a scenic area find "certainty in keeping visitors" amid "traffic anxiety"—perhaps the most precious gift Huangshan gives the whole industry.
