Source: Geek Park
Author: Su Zihua
In the past year, the term AI has been almost ubiquitous in the business world.
Some companies have allocated AI budgets ranging from hundreds of thousands to millions at the beginning of the year; some executives are busy holding AI strategy meetings; and others have formed special AI task forces…
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 knock on the client's door; this year, after the Spring Festival, clients are coming to us, which is a tremendous change."
Behind this is a truly historic opportunity for B-end AI implementation.
However, in the end, we often hear feedback like: "The technology is there, but why isn't it being used well?" "The actual effects are not landing in the industry." — Many AI projects have not truly taken off.
The investment is real money, and the anxiety is also palpable.
The contradiction lies in the disconnection between technology and scenarios. Many business managers have reported 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 companies going it alone — either lacking strong foundational model support or struggling to convert general technology into industry-specific solutions.
So, what have those companies that have truly implemented AI and achieved commercialization done right? After chatting with industry leaders like Tuya Smart, Fanruan, Lanling, and Gaode, we found that the key to success lies in carving out a path together with cloud platforms — a new route from technology to scenarios and then to commercialization.
Their "AI implementation" results indicate: The industrial implementation of large models requires teams that deeply cultivate vertical scenarios to collaboratively build an AI product ecosystem with cloud platforms, allowing technology to truly integrate into enterprise processes and products, rather than achieving isolated breakthroughs.
01 Co-building AI expands the business boundaries of enterprises
The key to a technology transitioning from hype to value lies in "who can use it."
In the past year, those companies that have truly realized AI application implementation share a commonality: they do not fight alone but "co-build" with cloud platforms. Everyone has realized that in the rapidly changing AI industry environment, collaboration is the most efficient survival strategy.
In the past, cloud vendors provided model APIs for companies to integrate; now, the logic has changed. For example, in the AI ecosystem co-built with industry partners on Alibaba Cloud, Alibaba Cloud actively enters the product co-creation process: from defining scenarios, packaging components, connecting data, to supporting the commercial pathway. The role of cloud vendors is evolving from infrastructure providers to value co-creation partners.
This co-building is not just "you use my model," but "we define the product together." Ke Dumin, Vice President of Technology at Tuya Smart, stated that when creating the "Tuya IoT Platform Alibaba Cloud Edition," "we co-created this product with the Alibaba Cloud market, visiting clients together, understanding needs, and defining the product."
The "Tuya IoT Platform Alibaba Cloud Edition" can help industrial clients' devices go to the cloud and implement AI capabilities. Ke Dumin revealed that they initially approached it with a trial-and-error attitude, but unexpectedly gained numerous commercial clients.
Therefore, the essence of co-creation is to define the incremental market together, making cross-border innovation possible. The effect of one plus one being greater than two becomes evident at this point, with Tuya Smart expanding its business from focusing on spatial intelligence scenarios to new fields such as agriculture, retail, and manufacturing, successfully landing the world's top-ranked smart management project in livestock in Singapore; meanwhile, Alibaba Cloud, which provides 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 like emotional companion toys and consumer-grade headphones previously had little correlation with IoT; but 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 spaces for these industries but also further strengthens Tuya Smart's existing business advantages.
Starting from smart home solutions and gradually expanding from indoor to outdoor AIoT platform enterprises, Tuya Smart is pushing every IoT product to load AI functions and attributes, matching corresponding application scenarios — upgrading from single device intelligence to "spatial intelligence." Ke Dumin mentioned that the AI-driven "home brain" will more effectively enhance user experience and the level of scenario intelligence.
Similarly, after launching the Tongyi Qianwen plugin on its Jiandaoyun platform, Fanruan found that clients automatically began to use it without complex packaging. Shen Tao admitted: "We didn't design any specific scenarios; we just launched the plugin, and clients started using it themselves."
It is evident that low-threshold, highly adaptable tools can best stimulate users' real needs. In the daily business processed by Jiandaoyun, the AI plugin has played a key role in scenarios such as contract review, resume screening, and customer follow-up analysis. Clients no longer need contract reviewers with monthly salaries of five to six thousand, nor do they need to manually sift through customer records to extract needs — AI can automatically identify key information such as signing intentions and price fluctuations.
In large enterprise cases, the power and effect of co-building are even more pronounced. Lanling, which excels in serving central state-owned enterprises and large companies, has transformed its "Lan Doctor" from an intelligent Q&A product within enterprises to an "AI middle platform" through large models and toolchains.
Built on the combination framework of "Tongyi Qianwen + exclusive small models + intelligent agents," the new "Lan Doctor" can not only provide intelligent Q&A but also conduct cross-system searches, extract experiences, and complete documents and processes with AI capabilities.
After the first new energy client, Seres, implemented this platform, it achieved the "three ones": finding work knowledge in one minute, preliminarily solving problems in one day, and accumulating project experience in one month.
The exponential increase in efficiency is the most direct contribution of AI to enterprises.
The results of Lanling's co-building with cloud platforms indicate that to convert AI capabilities into usable products for clients, both the 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, "and that requires us to work together."
A more typical example is the MCP service of the Gaode Open Platform. By overlaying the semantic understanding of Tongyi Qianwen with its own mapping capabilities, developers can generate complete cycling routes and automatically generate map codes with just a natural language sentence.
This "model + MCP + toolchain" approach greatly expands Gaode's business boundaries and creates new commercial opportunities for developers. A relevant person from Gaode told Geek Park: "The introduction of large models can better help our services upgrade from a single map to a full-scenario travel solution. We hope to reach more clients through the ecosystem."
Through the numerous cases above, we can see that the boundaries of enterprises are being redefined; they will not only be determined by industry and scale labels but also by "what problems can be solved." In the process of co-building AI, industry partners can break through their own limitations and enter fields that were previously difficult to reach.
For cloud platforms, in the process of co-building the AI ecosystem, they are also promoting their own transformation from "selling capabilities" to "ecosystem organizers." It can be said that the breadth of the platform's technology and the depth of the industry partners' scenarios constitute the golden combination for AI implementation.
02 AI Commercialization: Entering the Ecosystem Competition Stage
If two years ago, when large models first emerged, companies were still competing on parameters and fighting their own battles, then by 2025, the industry is increasingly focused on the practical issue of "how AI can be monetized."
In the past, the frequently mentioned term was "model effectiveness"; now, more terms like "scenario-based agents," "deliverable solutions," and "channel monetization" are emerging.
The cases of Fanruan, Lanling, Tuya, and Gaode indicate that in the "AI ecosystem" co-built with cloud platforms and other partners, what is being co-built is not just the technology stack and product capabilities, but also commercial pathways. The core value of the ecosystem lies in bridging the "last mile" from technology to business.
For example, Lanling utilizes Alibaba Cloud's customer resources and market subsidies to acquire new clients and expand overseas; the Gaode Open Platform will soon launch the Gaode MCP Server on the Alibaba Cloud market, directly connecting to the developer ecosystem; Fanruan revealed that they are trying to co-create an agent solution with Alibaba Cloud to be listed on the Alibaba Cloud market, leveraging platform traffic to convert into commercial results.
As leading companies accelerate monetization through ecosystems, industry analysts predict that by 2030, 50% of enterprise AI models will be privatized domain models, while this proportion was only 5% in 2024. This means that future AI implementation will increasingly rely on the close collaboration between "general large models + industry small models + scenario-based tools."
These commercial 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 effectiveness but are beginning to hope that platforms can provide product delivery capabilities, market reach capabilities, and even joint operation capabilities.
As the saying goes, technology determines the lower limit, while the prosperity of the ecosystem will determine the upper limit. In April of this year, Alibaba Cloud's "Blossom Plan" is precisely a footnote to this transformation.
According to the official definition, the "Blossom Plan" aims to focus on six key areas — infrastructure, models, data, tools, applications, and delivery — over the next three years, aiming to serve millions of clients and generate billions in business together with partners.
The cases mentioned earlier, such as Fanruan, Gaode, Tuya Smart, and Lanling, which have made good progress in AI implementation, are precisely the co-building partners of the "Blossom Plan."
From an external perspective, behind the "Blossom Plan," the subtle transformation of Alibaba Cloud's role is evident. It 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 displays, and even coordinate supply between merchants.
The value of the "Blossom Plan" lies in the fact that in an era where all industries are eager 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 the ecosystem that Alibaba Cloud co-builds with partners:
Openness is the cornerstone of ecosystem prosperity. The cloud platform provides a truly open ecosystem through open 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 transformation from "solution to signing."
The ultimate goal is for participants to jointly promote the transformation from "demo display" to "real application."
Alibaba Cloud's initiatives in the product ecosystem dimension also provide us with an insight: whether now or in the future, the winners of the AI era will be those who find the right partners, step into the right scenarios, and turn technology into usable products. Ultimately, by 2025, the competition will not only be about "whose technology is more dazzling," but also about "whose ecosystem can deliver."
Perhaps this is also an extension of Alibaba's philosophy of "making it easy to do business anywhere" in the AI era — "making it easy to do AI business anywhere."
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