Introduction: The turning point of the cultural and tourism economy
During the May Day holiday that just passed in 2026, China's cultural tourism market showed remarkable surging momentum and strong consumption resilience. The latest official authoritative data shows that during this key holiday consumption node, the number of domestic travels across the country reached 325 million, a steady growth of 3.6% compared with the same period last year; at the same time, the total domestic travel expenditure reached 185.492 billion yuan, a year-on-year increase of 2.9%. This series of core economic indicators not only confirms the continued surge of the cultural tourism economy and the full release of holiday consumption vitality, but also profoundly reveals the solid status of the cultural tourism industry as a strategic pillar industry of the national economy. When we expand the macro dimension of observation from a single "Golden Week" to the economic fundamentals throughout the year, the annual accounting data previously released by the National Bureau of Statistics further established this structural trend: the added value of my country's cultural and related industries has accounted for 4.61% of the gross domestic product (GDP), while the added value of tourism and related industries has accounted for 4.35% of the GDP. The combined direct and indirect contributions of these two major industries to the national economy have constituted an indispensable engine of macroeconomic growth.
However, under the surface of prosperity as the scale of the industry continues to expand and experience scenarios become increasingly diversified (such as concerts, music festivals, and sports events driving cross-border consumption of cultural tourism, and new hot spots such as the transformation of traditional intangible cultural heritage skills into immersive experiences), the global cultural tourism industry is facing extremely severe structural challenges. Rising labor costs brought about by labor-intensive attributes, low collaboration efficiency caused by extremely fragmented supply chains, and the huge tension between consumers’ ever-expanding extreme personalized needs and traditional standardized service supply are constantly compressing the profit margins of enterprises. Faced with these systemic bottlenecks, the traditional digital transformation characterized by "softwareization" and "mobile Internetization" over the past decade has hit its ceiling.
It is at this historical node where macroeconomic needs and micro-enterprise pain points converge that the evolution path of artificial intelligence technology has undergone a fundamental turning point - the industrial application of artificial intelligence is experiencing an era turning point in evolving from the "open domain question answering" of General AI (General AI) to the "complex business closed loop" of Vertical AI (Vertical AI). Today's capital markets and real enterprises are no longer satisfied with the divergent texts and vague reference suggestions provided by generative AI (Generative AI). Instead, they urgently need "vertical agents" that can deeply internalize the industry's underlying know-how, directly call system interfaces across system boundaries, and autonomously complete the delivery of complex transaction tasks. As relevant research points out, 2026 is a key turning point for enterprise-level AI to achieve substantial return on investment (ROI), marking the official entry of the business world into the "AI execution era". In this era, artificial intelligence is transforming from a mere generative tool to an autonomous execution system deeply embedded in industry-specific workflows. This underlying shift in technological paradigm is irreversibly reshaping the underlying business operation flow and commercial value distribution logic of the cultural travel industry.
Chapter 1: The business boundaries of general AI in the field of cultural tourism
In the past few years, general artificial intelligence technology represented by large language models (LLMs) has swept the world at an unprecedented speed, successfully completing the efficient analysis, reasoning and reconstruction of human natural language. However, as the technology advances in depth in the enterprise market, the inherent architectural limitations of general AI begin to be exposed in serious business scenarios.
1.1 Generalization effectiveness: General knowledge hegemony relying on massive public data
The core technical cornerstone of the general large model lies in its extremely large parameter scale and unsupervised pre-training based on massive public data on the Internet (covering general text, open source code, images and videos, etc.). In open-field knowledge question and answer, general text generation, basic language translation, and cross-modal creative generation, the general large model has demonstrated amazing generalization performance and creative capabilities. The core commercial value of this generalization capability is that it greatly lowers the cognitive threshold for human-computer interaction, allowing computing devices to efficiently "answer broad questions." For office scenarios that require high fault tolerance, such as basic content drafting, email writing, and general code-assisted generation, general AI has indeed verified its initial value as a general productivity tool (Copilot).
1.2 Scenario barriers: strict boundaries and data gaps of enterprise-level core business flows
However, when the general large model tried to sink into enterprise-level core business flows, especially into real economic links such as the cultural travel industry with extremely complex transaction logic, its proud generalization capabilities encountered hard business boundaries. In the face of complex real business scenarios, general models lack industry private data and deep context, making it difficult to meet the strict execution standards of "strong logic and low fault tolerance" for core business flows.
This scene barrier is mainly reflected in three deep dimensions. The first is the lack of private data and dynamic context. The core assets of the cultural travel industry are not static encyclopedia knowledge, but highly dynamic proprietary data, such as airlines' real-time fare rules and cancellation and change policies, hotels' instant room inventory and dynamic pricing algorithms, and even the instantaneous crowding of destinations. General large models cannot grasp these real-time internal data that require high-frequency API calls by crawling public web pages in advance. The second is model hallucination and extremely low commercial error tolerance. In the business closed loop of travel itinerary planning and large-amount bookings, factual accuracy is the cornerstone of business trust. If the general model is based on the "next token prediction" mechanism in probability, it fabricates a flight time that does not actually exist, or misinterprets a country's transit visa-free policy, it will directly lead to catastrophic business failures such as stranded passengers and extremely serious customer complaints. Finally, there is a lack of end-to-end operation execution authority. General AI is still essentially a passive "dialog system" that can give a natural language response of "I suggest you book a hotel." However, it lacks the native ability to directly cross IT system boundaries to call the underlying interfaces of Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) or Global Distribution System (GDS) for actual deductions and order generation.
1.3 Strategic Shift: Evidence from Gartner’s Top Ten Strategic Technology Trends in 2026
Faced with the scenario barriers encountered by general AI in enterprise-level applications, Gartner, the world's top technology research and consulting organization, has given a clear direction for industry evolution. In the "Top Ten Strategic Technology Trends for 2026" officially released by Gartner, it is clearly stated that the market is accelerating the shift to "Domain-Specific Language Models (DSLMs)" and "Multiagent Systems (MAS)" to meet more professional and precise business needs.
Gartner's in-depth analysis points out that if enterprise-level chief information officers (CIOs) want to obtain real transformative value from generative AI, they must shift from tactical general-purpose large-model experiments to strategic actions of deploying DSLMs. Compared with general-purpose large language models with hundreds of billions of parameters and extremely expensive running costs, domain-specific language models focus on fine-tuning data sets for specific industries. They not only demonstrate extremely high consistency, accuracy, and compliance in business-critical workflows, but their development and deployment costs can even be reduced by up to 50%. At the same time, the multi-agent system (MAS) trend highlighted by Gartner further reveals the direction of future software architecture: by allowing modular AI agents with knowledge in different professional fields (such as ticket agents, hotel agents, and visa agents in cultural and tourism scenarios) to collaborate with each other to jointly complete extremely complex automated business flows. The release of this strategic trend marks that the focus of enterprise-level AI has completely shifted from "general knowledge bases" to "professional domain tools" that can penetrate into the core of the business.
Chapter 2: Vertical AI’s business reconstruction of the cultural travel industry: Entering the era of AI execution
If general AI is a preliminary simulation of human natural language communication capabilities, then vertical AI is a dual imitation of the brain reasoning logic and hands-on operation capabilities of experts in specific industries. The rise of the bottom layer of vertical AI means that digital systems finally have the ability to autonomously perceive, in-depth reasoning, and directly intervene in the operation of the real business world.
2.1 Native execution engine: from divergent natural language to reliable end-to-end delivery
Vertical AI is fueled by scarce industry proprietary data with extremely high barriers, and deeply internalizes complex business know-how into the constrained boundaries of the algorithm. This evolution has completely transformed artificial intelligence from a simple "natural language generator" into an "Agentic Architecture" that can call the underlying system and handle complex tasks autonomously.
In the actual operation of the cultural tourism industry, this technological leap has brought about disruptive experience reconstruction. When consumers put forward extremely complex natural language requirements that are full of implicit restrictions, such as "Take two elderly people and a three-year-old child to Sanya for five days. The total budget is controlled within 10,000 yuan, the itinerary needs to be relaxed and leisurely, and the main demands are high-star beach hotels and barrier-free ocean parks." The traditional general model can only output a lengthy "Five-day trip to Sanya reference guide" that has no practical value. The vertical AI engine based on the agent architecture will regard it as a multi-dimensional operations research optimization computing task. It will automatically break down the requirements and trigger multiple concurrent API calls: the air ticket agent queries low-price flights on a specific date; the hotel agent selects room types in Sanya Bay or Yalong Bay that have child care services and meets the budget; the itinerary agent plans a tour route with no more than 5,000 steps per day. The core thing is that what vertical AI delivers is no longer a divergent "reference answer", but a reliable solution that includes accurate quotations, locked inventory, and can complete "end-to-end work results" with one-click payment.
2.2 Explosion of business value: Monro Ventures revealed a surge in spending and a market blowout
Breakthroughs in the underlying computing architecture directly brought about a blowout in the enterprise-level service market. The improvement in technological maturity is quickly reflected in the flow of capital and the reallocation of corporate IT budgets. According to the latest "2025 Enterprise Generative AI State Report" released by Menlo Ventures, a veteran and top venture capital institution in Silicon Valley, global enterprise spending in the field of generative AI has soared to an astonishing scale of US$37 billion, becoming the fastest-expanding subcategory in the history of software development.
In this in-depth research report, Monro Ventures divides enterprise-level AI application spending into three core arrays, and the data clearly outlines the trajectory of the vertical AI explosion. The first is horizontal AI (Horizontal AI), with expenditures reaching US$8.4 billion, mainly used for general productivity improvements across all functional departments; followed by departmental AI (Departmental AI), with expenditures reaching US$7.3 billion, focusing on standardized functions such as code generation (accounting for 55% of killer applications), customer success, marketing, etc.; the most eye-catching is Vertical AI (Vertical AI) The field of AI has suddenly emerged. Enterprises' single-year spending in this field has surged to 3.5 billion US dollars, compared with 1.2 billion US dollars in the previous year, achieving explosive growth that nearly tripled. The report specifically points out that the healthcare industry currently accounts for half of vertical AI spending at US$1.5 billion (aimed at solving the problems of administrative burden and shrinking profit margins). However, this AI reconstruction logic based on complex business flows is rapidly spreading to vertical fields such as finance, law, and cultural tourism that are highly dependent on proprietary processes. This drastic change in the expenditure structure proves that corporate decision-makers have deeply realized that what can truly build a long-term moat is not a general tool, but a vertical intelligent solution that deeply integrates industry attributes.
2.3 2026: The substantial turning point of ROI and the full arrival of the AI execution era
As the early technology hype bubble is gradually squeezed out, enterprises’ evaluation criteria for AI are rapidly returning from “technical stunningness” to the cold “commercial return on investment (ROI)”. A joint in-depth study by RBC Capital Markets and Omdia, a world-renowned research institution, further established a crucial macro milestone: 2026 is a key inflection point for enterprise AI to obtain substantial return on investment.
Matt Hedberg, global head of technology, Internet, media and telecommunications (TIMT) research at RBC Capital Markets, clearly pointed out in the report that the adoption of enterprise-level AI will reach a historic turning point in 2026, and the market will officially transition from board-level conceptual conversations and early pilot project experiments to a measurable and quantifiable enterprise return on investment (ROI) growth stage. Research shows that although software companies may face profit margin pressure due to AI infrastructure integration costs in the short term, in the steady-state operating stage, application layer companies that deeply embed AI will deliver substantial revenue expansion and customer spending growth across various vertical industries. This means that the business world has officially bid farewell to the auxiliary stage of only using AI for text polishing, and has fully entered the "AI execution era" in which systems can automatically execute complex business decisions and transaction instructions.
Chapter 3: Core demands of the market: Enterprise-level AI intelligence engine that spans cycles
Facing the technological paradigm shift from general basic models to vertical execution engines, the entire industry's demand for digital empowerment has undergone a qualitative change. In the cultural travel industry, simple general-purpose technical tools (such as just adding a question and answer floating window based on a large-model API interface to the official website) not only cannot substantially improve operational efficiency, but may reduce user experience due to the fragmentation of underlying business logic, and even bring about compliance risks and public relations crises caused by AI "illusion".
Therefore, what the market really and urgently needs is a vertical AI system that can penetrate into the core business flow of enterprises. This core demand requires cutting-edge technology companies and industry giants to complete a fundamental evolution of their roles: from "IT tool suppliers" that simply provide cloud storage, SaaS software or standard algorithms, to an "enterprise-level AI intelligence engine" that can lead systemic business reshaping. To achieve this leap, suppliers must build a solid moat at the bottom consisting of three core elements:
The first is the unfathomable “industry depth Know-how”. The cultural tourism industry involves extremely complex long-link services, from cabin control rules for air freight rates, dynamic pricing games for hotel revenue management, to fragmented scheduling of destination ground resources. These non-standardized business logic cannot be mastered by AI through simple text training. The second is "Proprietary Data Moats" that cannot be copied. As RBC Capital Markets points out, in an era where AI is reshaping the information landscape, proprietary data will command a very high premium. Only companies with massive historical real transaction data, multi-dimensional user behavior trajectories, and real-time supply chain inventory and settlement data can train accurate models without illusions. Finally, there is the extremely powerful “end-to-end execution capability”. This requires the agent to not only be able to "understand" and "think", but also to have "hands and feet" and the ability to perform engineering implementation of concurrent order operations on multiple systems through an extremely complex API interface group. Only an enterprise-level AI intelligence engine that combines these three can open up a way for cultural tourism companies to reduce costs and increase efficiency under the double attack of rising labor costs and intensifying competition in the era of AI execution in 2026 and beyond.
Conclusion: Reconstruct production relations and return to the origin of experience
The rapid development of technology will eventually return to the essence of business. The "execution era" dominated by vertical AI is not just an arms race in computing power, it is also a profound reconstruction of industrial production relations and business patterns.
When complex system scheduling, tedious itinerary planning and massive supply and demand matching are perfectly taken over by vertical intelligence at almost zero marginal cost, the cultural travel industry truly ushered in a historical moment of "rebirth". At this stage, the core that an enterprise delivers to the market will completely transform from mere standardized "IT products" and "resource platters" to non-replicable "emotional assets" and "lifestyles."
This means that great companies in the future must have dual engines: one has the technical landscape to control underlying intelligence and proprietary data, and the other has the platform thinking to reshape the digital business ecosystem and empower upstream and downstream industries. In this new era starting from 2026, computing power and intelligence are just tickets to the future; what truly determines how high and far a company or even the entire industry can go is the redefinition of the boundaries of the business ecosystem, the in-depth co-creation of local culture, and the unbounded imagination that spans technology cycles to connect to the deep needs of mankind. The turning point of the times has arrived, and the sea of stars belonging to Wenlv Zhiqing has just begun.