Why Industries Turn to Data & AI

In our country, all industries are facing a common soul-searching question—where is the next growth?

Milk, as a frequent visitor on the breakfast tables of many families, not only carries health and nutrition but is also one of the categories with a wider distribution and more sales outlets in the domestic beverage market. Faced with such a huge market, especially the 5 million sales outlets and 1.3 billion consumers covered by Yili Group, how to accurately match and meet the diverse consumer needs has become a key issue that they are constantly thinking about.

At the same time, in the express delivery industry, STO Express is also facing challenges that come with the rapid development of the industry: the fragmentation of consultations, delayed responses, and uneven service quality are becoming increasingly prominent issues. Although the express delivery customer service robot has been in operation for many years, the "intelligent" customer service of express delivery has angered consumers many times due to improper handling, and how to reduce complaints and improve customer satisfaction has become a common problem in the express delivery industry.

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The road to digital transformation in the financial industry is also full of challenges and opportunities. As the scale of banks gradually expands, Taizhou Bank is facing pain points including the lack of data standards, non-unified indicator口径, unclear inventory of data assets, unclear data management processes and division of responsibilities, and the lack of effective data governance tool support. These issues not only may lead to serious challenges from regulatory authorities in data production but may also face accountability, and will greatly reduce the credibility of business reports due to the ambiguity in the understanding and use of indicators.

From selling milk, delivering express delivery to opening banks, all are facing challenges of varying degrees from internal and external factors. But behind these challenges, they actually hope to answer these questions: where does the next growth momentum of enterprises come from? If there is no way to find the engine of growth immediately, is it possible to reduce costs and increase efficiency? Improve existing operational efficiency?

Data-driven enterprise intelligent transformation

"We have served nearly more than 50,000 enterprises in the past, and have traveled through more than 30 provinces and municipalities, and have deeply participated in the digitalization process of 24 major industries." Peng Xinyu, vice president of Alibaba and CEO of Lingyang, said at the recently concluded Cloud栖 Conference.

As a wholly-owned subsidiary of Alibaba, although Lingyang has only been established for four years, this seemingly young company actually has a deep background, gathering a group of people from Alibaba who understand data. In the full link system of data elements, it has formed an enterprise digital service ecosystem that integrates data × AI products, solutions, and services.

Taking the dairy industry as an example, although Yili started the digital transformation earlier, it still faces challenges such as the proliferation of data islands, the lack of high-value consumer data assets, and poor connection with external ecological platforms. Its transformation road is still full of challenges. Yili's strategy focuses on the dual upgrade of the supply chain and consumers, optimizing the efficiency and experience of both the production and consumption ends through digital means. However, the complex industry chain of the dairy industry, spanning multiple industry levels, makes this transformation task particularly arduous.

Faced with the huge market of 5 million sales outlets and 1.3 billion consumers nationwide, meeting the diverse needs of different consumer groups is key. In this vast market, consumer purchasing power, preferences, and choices vary greatly, and coupled with the important role of dealers in the sales chain, it makes it difficult for enterprises to truly understand consumers.Yili and Lingyang's approach is to construct a unified data service system that promotes the deep integration of data with the actual operations of the enterprise. With the help of products such as Lingyang Dataphin and Quick Audience, along with a series of data services, Yili has not only optimized the entire process of data collection, cleaning, processing, and structuring but also integrated digital experiences from various fields including marketing, supply chain, finance, and human resources to build a solid data infrastructure. This system revolves around the "production, storage, modeling, management, and application" of data throughout its entire lifecycle, ensuring the efficient operation of the supply chain.

On this basis, Yili further achieved unified management of consumer data through omni-domain digital intelligence operations, which enabled more refined user insights. This transformation significantly improved the accuracy of consumer profiling, expanding from the original 6-8 segments to 60-80, allowing Yili to better understand and respond to consumer needs. At the same time, by integrating real-time data from various links in the supply chain, Yili achieved optimization decisions at the minute level, ensuring the efficiency and flexibility of the supply chain.

"Digital capabilities have become the core driving force for the continuous growth of Yili's business," said a person in charge from Yili. The deep integration and application of data and technology constitute the core competitiveness of the enterprise. Only by organically integrating data and technology into the business can continuous growth and innovative development be achieved.

Scenarios Become Key to AI Technology Application

With the rapid development of AI large model technology, enterprises are pondering a question: how to accurately integrate AI large model technology into various business scenarios to drive digital transformation and business innovation. However, in this process, ensuring the accuracy of data, effectively integrating AI capabilities, and realizing the true value of AI through scenario decomposition and business reconstruction have become the main challenges faced by the industry.

Lingyang's strategy应运而生, which is "(Algorithm + Computing Power + Data) x Scenario". The core of this strategy is that although algorithms and computing power play an important role in digital transformation, not all enterprises need or can afford the high costs of independent research and development. In contrast, data, as a core asset of the enterprise, has uniqueness and controllability and is the key to driving intelligent transformation.

In the strategic layout, algorithms are regarded as a powerful engine for intelligent transformation, and computing power is an essential support to ensure the efficient operation of algorithms. At the same time, data, as the core element of algorithm optimization and training, is self-evident in its importance. More importantly, the strategy emphasizes the value of "scenarios", regarding them as the key bridge to transform cutting-edge technology into actual business results. By accurately matching technology with business scenarios, it helps enterprises to enhance the value of technology and accelerate the pace of intelligent transformation.

Why did Lingyang choose this path instead of directly engaging in the development of large models? Peng Xinyu said that AI should be a product that is accessible and applicable to actual business, rather than a high-tech that is out of reach, in order to truly popularize. Lingyang's positioning is very clear, that is, a product company, so it chooses to focus on data and application scenarios, using mature AI large model technology to provide customized solutions for enterprises and promote digital intelligence transformation.

To achieve this goal, Lingyang has launched a series of intelligent product matrices, including the data analysis platform Quick BI, the intelligent marketing growth platform Quick Audience, and the intelligent customer service Quick Service 2.0. These products not only integrate the capabilities of large models but also help enterprises create commercial value in multiple dimensions by accurately matching technology with business scenarios.

Due to the dispersion of service channels and the lack of a centralized data management system, STO had difficulty collecting and analyzing key data in the service process. The absence of this information made the assessment of service quality subjective and difficult to manage, lacking data to optimize service processes and improve service efficiency. Lingyang Quick Service has brought revolutionary changes to STO. By monitoring and recording key indicators in the service process in real-time, STO can quantify the management of service quality and provide a solid data foundation for service optimization.In traditional promotional activities, companies often face three major challenges: difficulty in understanding target users, difficulty in preparing promotional ideas, and difficulty in finding the right communication timing. At the Yunqi Conference, we saw BYD Denza Motors use the Lingyang Quick Audience platform to build a digital marketing platform, achieving the integration of data, systems, and business processes. In addition, by utilizing AI technology for real-time data monitoring and multidimensional analysis, Denza Motors can quickly capture market dynamics and accurately target customers, achieving an 80% increase in potential customer invitations and a 21.9% increase in appointment test drive rates.

In the financial sector, issues such as uneven data quality and weak data application capabilities directly affect the efficiency and accuracy of risk control and business decision-making, becoming a major bottleneck in the development of small and micro banks. Taizhou Bank, through the unified data platform built with Lingyang Dataphin and Quick BI, not only improved the level of data governance but also provided strong support for business decision-making, promoting the comprehensive digital transformation of bank business. Currently, Taizhou Bank has established more than 1600 basic data standards at the bank level, covering 10 business areas, 14 thematic domains, and more than 100 business processes.

These practices prove that the combination of "AI×Scenario" is not a simple addition, but a profound transformation from quantitative to qualitative change. Peng Xinyu pointed out that this qualitative change is irreversible, similar to the process of promoting physical growth by consuming food. When companies truly master and use AI technology well, it brings not only increased efficiency and reduced costs but also a fundamental change in business models and core competitiveness.

Data and AI become the next new increment. In recent years, each annual strategy release by Lingyang has strived to indicate the direction of change in the software industry.

Since its establishment in 2021, Lingyang has integrated multiple data technology teams within Alibaba Group and commercialized the "middle platform" strategy within Alibaba externally, officially entering the blue ocean of enterprise-level data services. At this time, Lingyang's goal is to help companies better manage and utilize their own data resources by providing professional data services.

With the deepening of corporate digital transformation, the traditional SaaS (Software as a Service) model gradually exposes its limitations, and the disconnection between data flow, workflow, and business flow becomes an obstacle for companies to mine data value. In 2022, Lingyang laid out a new track of DaaS (Data as a Service), starting to pursue a higher level of service form, that is, by technologically empowering, providing complex data processing and analysis capabilities to enterprise users in the form of services, reducing the threshold and cost of enterprises using data intelligence.

In 2023, Lingyang took another important step, launching the AI landing path of "Intelligence = Large Model + Good Data" and creating the data service hub "Lingyang Port". This move marks a significant breakthrough in Lingyang's construction of the data ecosystem. Lingyang hopes to transform data from a closed "chimney" state to an open and shared "drilling platform" in this way, thereby releasing the important value of data and promoting business growth and innovation of enterprises.

This year, Lingyang proposed the product intelligence strategy of "(Algorithm + Computing Power + Data) x Scenario", which is a comprehensive upgrade in the field of data intelligence. The strategy emphasizes the importance of algorithms, computing power, and data as the cornerstone of intelligence and clearly points out that these elements must be closely combined with specific scenarios to generate greater benefits. This reflects Lingyang's determination to build an enterprise service ecosystem with data as the core by deeply integrating AI technology and business scenarios, and further expand the to B market.

Lingyang's series of strategic changes reflect that in the digital age, companies are shifting from data-driven to AI-driven to achieve deeper business innovation and growth. In fact, since last September, Alibaba Group has made strategic adjustments and established "AI-driven" as the new development direction, looking for a new growth curve in the AI competitive landscape.Looking ahead, Peng Xinyu suggests that AI may evolve into a state as intangible and omnipresent as water. Perhaps one day in the future, when people no longer specifically mention AI, it will have truly matured. This is quite similar to the trajectory of the internet's development over the past two decades. Twenty years ago, when people asked about each other's professions, responding with "I am engaged in internet entrepreneurship" would seem very novel. Nowadays, if someone still discusses "internet entrepreneurship" as a fresh concept, people would think they have time-traveled from the past. Similarly, in the future, AI will become like this; it will no longer be a concept that is specifically mentioned, but will silently permeate and integrate into every corner of life. This is the mark of AI technology achieving true maturity and widespread adoption.

However, while enjoying the benefits brought by AI technology, it is necessary to face the related ethical and compliance issues, ensuring the transparency and fairness of technology applications. Peng Xinyu emphasizes that only in an environment where ethics and technology develop in tandem can AI truly realize its value. This is not only about the social responsibility of enterprises but also the cornerstone of their sustainable development.