Source: Geek Park
Author: Su Zihua
In the past year, the term AI has been almost ubiquitous in the business world.
Some enterprises have allocated hundreds of thousands to millions in AI budget at the start of the year; some executives are busy holding AI strategy meetings; others have formed AI special teams…
From last year's hesitation to this year's proactive layout, Shen Tao, Vice President of Strategy at Fanruan Software, stated: 'Last year, it might take three months to open the door to customers; this year, customers proactively come to us after the Spring Festival, which is a tremendous change.'
Behind this is the historical opportunity for B-end AI implementation.
However, in the end, we often hear feedback: 'We have the technology, but why can't we use it well?' 'The actual effect does not land in the industry.' — Many AI projects have not really taken off.
Investment is real money, and anxiety is also real.
The contradiction lies in the disconnection between technology and scenarios. Many business managers report that AI products perform excellently in demonstration environments but frequently 'fail' in real business scenarios. This contrast between the 'demo myth' and the 'implementation dilemma' exposes the limitations of enterprises going solo—either lacking strong foundational model support or struggling to transform generic technology into industry-specific solutions.
So, what exactly did those enterprises that successfully implemented AI and achieved commercialization do right? After talking with industry leaders like Tuya Smart, Fanruan, Lanling, and Gaode, we found that the key to success was carving out a path with cloud platforms—a new path from technology to scenarios and then to commercialization.
Their 'AI implementation' results indicate that the industrial landing of large models requires teams that deeply cultivate vertical scenarios to build an AI product co-creation ecosystem with cloud platforms, allowing technology to truly enter enterprise processes and be integrated into products, rather than achieving breakthroughs in isolation.
01 Co-building AI Expands Business Boundaries for Enterprises
The transition of a technology from hype to value hinges on 'who can use it.'
In the past year, those enterprises that have truly achieved AI application landing share a commonality: they do not fight alone but 'co-build' with cloud platforms. Everyone realizes that in the rapidly changing AI industry environment, collaboration is the most efficient survival strategy.
In the past, cloud vendors provided model APIs, and enterprises simply integrated them; now, the logic has changed. For example, in the AI ecosystem co-built with industry partners on Alibaba Cloud, Alibaba Cloud actively participates in the product co-creation process: from defining scenarios, packaging components, integrating data, to supporting commercial pathway connections. The role of cloud vendors is transitioning from basic infrastructure providers to value co-creation partners.
This co-creation is not just 'you use my model,' but 'we define the product together.' Tuya Smart's Vice President of Technology, Ke Dumin, stated that while creating the 'Tuya IoT Platform Alibaba Cloud Version,' 'we co-created this product with the Alibaba Cloud marketplace, visiting customers together, understanding needs, and defining the product.'
'Tuya IoT Platform Alibaba Cloud Version' can help industrial customers' devices go to the cloud and implement AI capabilities. Tuya Smart's Vice President of Technology, Ke Dumin, revealed that they initially took a trial-and-error approach, but unexpectedly gained numerous commercial clients.
Therefore, the essence of co-creation is to jointly define the incremental market, making cross-border innovation possible. The effect of one plus one being greater than two becomes apparent at this time, with Tuya Smart expanding its business from focusing on spatial intelligent scenarios to multiple new fields such as agriculture, retail, and manufacturing, successfully implementing the world's top smart livestock management project in Singapore; while Alibaba Cloud, providing AI technology and cloud services, has also expanded into new markets.
Ke Dumin told Geek Park: 'With the arrival of AI, many industries are worth redoing. Industries such as emotional companionship toys and consumer-grade headphones previously had little relevance to IoT; however, now, large models need to move from the digital world to the physical world, relying on the collaborative support of IoT technology.' He further pointed out that the emergence of large models not only opens up new growth opportunities for these industries but also further strengthens Tuya Smart's existing business advantages.
As a company that started with smart home and gradually expanded to outdoor AIoT platforms, Tuya Smart, with the support of large model technology, is pushing every IoT product to load AI functions and attributes, matching corresponding application scenarios—from single-device intelligence to 'spatial intelligence.' Ke Dumin mentioned that the AI-driven 'smart home brain' will more effectively enhance user experience and scenario intelligence levels.
Similarly, after Fanruan launched the Tongyi Qianwen plugin on its Jiandaoyun platform, they did not do complicated packaging, only to find that customers began to call it automatically. Shen Tao admitted: 'We didn’t specifically design for any scenario; we just launched the plugin, and the customers started using it themselves.'
It is evident that low-threshold, highly adaptable tools can best stimulate users' real needs. In the daily operations handled by Jiandaoyun, AI plugins have played a key role in scenarios such as contract review, resume screening, and customer follow-up analysis. Customers no longer need contract reviewers with monthly salaries of five to six thousand; they don't need to manually sift through customer records for requirements—AI can automatically identify key information such as signing intentions and price fluctuations.
In large enterprise cases, the power and effects of co-creation are even more pronounced. Lanling, which excels at serving central state-owned enterprises and large companies, has upgraded their 'Blue Doctor' from an intelligent Q&A product within enterprises to an 'AI Middle Platform' through large models and toolchains.
Built on the framework of 'Tongyi Qianwen + exclusive small models + intelligent agents,' the new 'Blue Doctor' can not only provide intelligent Q&A but also conduct cross-system searches, extract experiences, complete official documents, and process workflows with AI integration.
After landing on the platform, Lanling's first new energy client, Seres, achieved the 'three ones': find work knowledge in one minute, preliminarily solve problems in one day, and accumulate project experience in one month.
The exponential improvement in efficiency is AI's most direct contribution to enterprises.
The results co-created by Lanling and the cloud platform indicate that to transform AI capabilities into usable products for customers, both platform and industry Know-How are indispensable. 'Alibaba Cloud has technology and customer resources, but many concrete scenarios need us to implement,' said Xia Jinghua, director of Lanling Research Institute. 'We have to work together.'
A more typical example is the Gaode Open Platform's MCP service. By overlaying Tongyi Qianwen's semantic understanding with its own mapping capabilities, developers can generate a complete cycling route and automatically create map code with just a natural language sentence.
This 'model + MCP + toolchain' approach has greatly expanded Gaode's business boundaries and created new commercial opportunities for developers. A relevant person in charge from Gaode told Geek Park: 'The introduction of large models can better help our services upgrade from single mapping to full-scenario travel solutions. We hope to reach more customers through the ecosystem.'
Through the various cases mentioned above, we can see that the boundaries of enterprises are being redefined, not only determined by industry and scale tags but also by 'what problems can be solved.' In the process of co-building AI, industry partners can break through their limitations and enter fields that were once difficult to reach.
For cloud platforms, the process of co-building the AI ecosystem is also pushing them to transition from 'selling capabilities' to 'ecosystem organizers.' It can be said that the breadth of the platform's technology and the depth of the industry partner's scenarios form a golden combination for AI implementation.
02 AI Commercialization: Entering the Ecosystem Phase
If two years ago, when large models were just emerging, enterprises were still competing on parameters and fighting their own battles. By 2025, the industry is increasingly focused on the practical question of 'how AI can generate revenue.'
In the past, the frequently mentioned term was 'model performance'; now, more common terms include 'scenario-based Agent,' 'deliverable solutions,' and 'channel monetization.'
Cases from Fanruan, Lanling, Tuya, and Gaode indicate that in the 'AI ecosystem' co-built with partners like cloud platforms, what is being built is not only the technology stack and product capabilities but also the commercial pathways. The core value of the ecosystem lies in bridging the 'last mile' from technology to business.
For example, Lanling uses Alibaba Cloud's customer resources and market subsidies to acquire new customers and expand overseas; the Gaode Open Platform will soon launch the Gaode MCP Server on the Alibaba Cloud Marketplace, directly connecting the developer ecosystem; Fanruan revealed that they are trying to co-create an Agent solution with Alibaba Cloud to list it on the Alibaba Cloud Marketplace, leveraging platform traffic to convert into commercial results.
As leading enterprises accelerate monetization through ecosystems, industry analysts predict that by 2030, 50% of corporate AI models will be private field models, while in 2024, this ratio is only 5%. This means that future AI implementation will rely more on close collaboration between 'general large models + industry small models + scenario-based tools.'
These business actions reflect a change and trend: AI implementation is a systematic project, and platforms need to provide end-to-end support. Enterprises' expectations of cloud platforms are no longer solely focused on model performance but are beginning to hope that platforms can provide product delivery capabilities, market reach capabilities, and even joint operational capabilities.
As the saying goes, technology determines the lower limit, and the prosperity of the ecosystem will determine the upper limit. In April this year, Alibaba Cloud's 'Bloom Project' is precisely a footnote to this transformation.
According to the official definition, the 'Bloom Project' aims to focus on six key areas: infrastructure, models, data, tools, applications, and delivery over the next three years, with the goal of serving millions of customers and generating billions in business with partners.
The cases of Fanruan, Gaode, Tuya Smart, Lanling, and others mentioned earlier, which have made significant progress in AI implementation, are precisely the co-creation partners of the 'Bloom Project.'
From an external perspective, behind the 'Bloom Project' is the quiet transformation of Alibaba Cloud's role, which can be likened to building a shopping mall. Previously, it was only responsible for constructing the building and providing electricity; now it needs to attract different merchants, help restaurants design menus, assist clothing stores in setting up display racks, and even coordinate supply between merchants.
The value of the 'Bloom Project' lies in the current expectation across industries for AI applications to land; it has initiated an ecosystem that reduces friction in cooperation and increases innovation density. Lowering the costs of ecosystem collaboration and enhancing innovation efficiency will become the core competitiveness of the platform.
In this ecosystem co-built by Alibaba Cloud and its partners:
Openness is the cornerstone of ecosystem prosperity. The cloud platform provides a truly open ecosystem by opening models, data, toolchains, and cloud markets;
Ecosystem partners transform industry Know-How into replicable product solutions;
Market channels and commercial mechanisms support the commercial closed-loop conversion from 'solution to order.'
The ultimate goal is for participants to jointly promote 'demo demonstrations' to become 'real applications.'
Alibaba Cloud's actions in the product ecosystem dimension also provide us with an insight: whether now or in the future, the winners in the AI era will be those who find the right partners, step on the right scenarios, and turn technology into usable products. Ultimately, by 2025, AI implementation will not just depend on 'whose technology is cooler,' but 'whose ecosystem can deliver.'
Perhaps this is also an extension of Alibaba's philosophy of 'making it easy to do business' in the AI era—'making it easy to do AI business.'
