In the field of TOB (business-to-business), the application of AI (artificial intelligence) has always attracted much attention, but it has also caused many controversies. On the one hand, AI is believed to bring huge efficiency improvement and business growth opportunities to enterprises; On the other hand, enterprises are often confused about the application of AI, and even the phenomenon of “internal cold and external heat” occurs. This article will delve into the current status and challenges of AI applications in the TOB field, and analyze how enterprises can set clear AI goals at the strategic level, how to achieve large-scale implementation of AI at the business level, and how to promote the successful implementation of AI projects in the organizational structure by dismantling 6 enterprise AI breakthrough cases.
1. How difficult is the application of AI in the field of TOB?
About three years ago, we started learning about GPT (Generative Pre – Trained Transformer), a generative AI represented by GPT that will have an impact on everyone’s life.
Soon, we really saw the huge opportunities brought by artificial intelligence to enterprise business growth.
From an external perspective, the entire market is enthusiastically pursuing AI.
Paradoxically, interest in generative AI within enterprises is gradually cooling.
1. Businesses are “duplicitous” about AI
The main manifestations are three aspects:
1) In the process of applying AI to enterprises, it encounters the situation of “internal cold and external heat”;
2) The value of AI is difficult to scale in business
3) The strategic communication gap of AI projects is inches high
2. The breadth of strategic goals and the depth of application
So, how should organizations develop a clear AI strategy to address business disconnect?
To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.
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This can be divided into the breadth of strategic goals and the depth of application.
There are two dimensions to planning an AI strategy blueprint:
1) Breadth of AI strategic goalsThe business value points are divided into the following three categories:
2) AI landing depthIt is divided into three stages: proof-of-concept, scale-up, and organizational restructuring.
3. Build an AI strategy matrix
When we combine the two dimensions, we form a 3X3AI strategy matrix.
Enterprises can choose pilot projects under three important business goals, and then gradually promote the scope of application to scale. Thereinto:
- Enterprises can focus on the core scenario or choose to start multiple proofs of concept at the same time, with multiple business units doing it simultaneously
- A pilot project is not linear in progress, but rather a timely redirection along the way: to inspire new concepts or directly change business objectives
2. Dismantling the best practices of AI strategy for 6 companies
Case 1: An international retail brand of consumer resistants
How traditional manufacturing companies can build the soil for innovation and embrace AI to successfully take the first step in a multitude of strategic goals.
“Top-down + bottom-up” organizational management is an important way to promote it.
The “international consumer goods retail brand” company first set up an AI innovation team of several people, but it is not a “technology geek camp” in the traditional sense, and their core task is not to develop complex algorithms, but to “translate” AI technology into business language.
For example, A/B testing can verify whether AI can improve warehouse picking efficiency or double the output rate of popular social media content.
Since it is not enough for AI to directly serve the business, it is certainly not enough for a few people to understand AI, so the company has established a club to attract tens of thousands of employees around the world to participate in AI learning in the form of an interest community.
First, the marketing department uses AI to judge fashion trends, explore hot topics, summarize recent positive and negative feedback, and regularly capture highly relevant likes and reviews.
Second, by summarizing popular content formats with insights, AI can generate 0-1 outlines that employees in the retail department can iterate from 1 to 1.1/1.2. In turn, AI can generate some more basic marketing content, including but not limited to product promotional copy and images, and even use AI to edit live slices.
Finally, review the performance of content data, summarize popular ideas, share them with everyone, and encourage everyone to do a new round of iteration, so that the explosive rate is getting higher and higher.
This practice of “technology democratization” not only lowers the threshold for AI application, but also a management innovation on “how people collaborate with AI”.
Case 2: A national home furnishing brand
With the first-mover advantage of starting from e-commerce, the home furnishing brand has successfully established a business territory with a scale of 10 billion.
Driven by organizational restructuring and technological innovation, one of its core strategies is to use generative AI technology to solve the key problems of large-scale content production and distributor empowerment.
For traditional industries, the online traffic of social media can be described as a total increase. The home furnishing brand has built a comprehensive contact layout including AutoNavi, Meituan, Dianping, Douyin local life services, and Xiaohongshu, giving full play to the scale effect of the headquarters in traffic acquisition, while ensuring the localized response ability of customer service and sales conversion, and finally forming a closed-loop new retail ecosystem.
The company’s AI strategy has received strong support from the company’s top management, with projects directly led by the CMO or higher.
The company recognizes that AI applications are not experimental projects in a single department, but rather strategic efforts that require cross-departmental collaboration.
Direct push from the top ensures that AI projects are fully resourced and avoid becoming department-level “self-entertainment” experiments.
Case 3: Midea
As a digital pioneer in the home appliance manufacturing industry, Midea Group has invested more than 20 billion yuan to promote digital transformation since 2012.
The company established the AIGC strategy group in 2024, establishing three major goals: improving work efficiency, stimulating employee creativity, and enhancing product competitiveness.
The most notable feature of Midea’s AI strategy is its strictly ROI-based landing methodology, ensuring that every AI investment generates measurable business value.
1) Efficiency Enhancement: AI+ factories improve efficiency and rewrite industry standards
Midea Building Chongqing Factory is the first full-process AI-powered lighthouse factory in the global central air conditioning chiller industry.
It is not only a sample of intelligent manufacturing transformation and upgrading within Midea Group, but also sets a model for Midea’s green industry to empower the intelligent development of the global manufacturing industry.
2) Cost reduction: AI+ content generation, saving real money
At Midea, the value of AI projects must pass the “cost reduction test” of the finance department.
The company deeply integrates AI into all aspects of enterprise operations, establishes a quantifiable value evaluation system, and achieves comprehensive efficiency improvements from content creation to customer service to R&D processes.
3) Drive growth: AI+ customer experience to create a global user value chain for smart homes
Midea uses AI technology to comprehensively improve product experience and user interaction, realizing the optimization of the complete value chain from hardware performance to marketing reach, and injecting new impetus into business growth.
Case 4: Yili
As early as a few years ago, Yili forward-looking listed generative AI as a key technology to drive future growth, and acted quickly after the release of OpenAI in late 2022, launching its self-developed “YILI-AI” within just a few months.
Yili emphasizes that it does not talk about business from the perspective of technology and systems, but from the perspective of business how to empower with data and technology, and closely links digital performance with business goals.
1) Drive growth: AI+ product innovation is digitized in the whole link, accelerating from market insights to creating explosive models
Yili Changqing Popping Bead Yogurt verifies the new formula of “intelligent explosive product = accurate demand insight × agile proof of concept × precise market breakdown”.
Through AI semantic analysis, Yili accurately captures emerging needs such as “chewing fun” and quickly locks in the market opportunities of popping bead technology applied to dairy products.
With the help of the user labeling system, the team formed a community of high-value consumers such as “urban light food people” and “cutting-edge mothers”, optimized recipes and flavor combinations based on data analysis, and finally determined unique product series such as “blueberry + purple rice popping pearls”.
After listing, the digital business dashboard monitors sales data in real time, guides marketing strategy adjustments, and achieves efficient conversion.
Remarkable results: the new product achieved a household penetration rate of 1.2% (3 times the industry average) within 40 weeks of launch, a repurchase rate of 17.27%, and ranked first in the country in terms of sales share, becoming the king of low-temperature yogurt.
2) Cost reduction and efficiency increase: AI+ supply chain optimization to ensure the stable operation of core business
Traditional equipment maintenance scenarios rely heavily on the operational experience of frontline workers (i.e., “tacit knowledge”), and this unstructured experience data has not been effectively utilized for a long time.
With the support of AI technology, Yili has achieved two key breakthroughs:
- The response time of knowledge base queries has been greatly reduced, and employee satisfaction has been effectively increased to 98%.
- Significantly reduced mean time to repair (MTTR) for production equipment downtime and increased fault repair efficiency by 52%
3) Business model innovation: AI+ health services, expanding the possibilities from “selling products” to “selling services”
- Offline activity level: Provide children with scientific analysis such as height screening and prediction, posture assessment, and sports potential analysis through AI smart devices, and generate personalized growth and improvement plans
- Online private domain operation: Precipitate user data, interpret reports for users based on AI large model capabilities, and continuously provide interactive content such as nutrition consultation and nutrition guidance, forming a complete business link of “screening-service-conversion”
Case 5: L’Oréal
L’Oréal uses AI as the starting point to reshape its business shape, reconstructing the organization from the thinking level, rather than superimposing AI capabilities on top of the original business structure. The company’s definition of innovation standards is also very extreme – “meaningful for business and scalable”.
L’Oréal China’s content middle office system
The platform’s technical architecture has three core features:
- AI Engine Integration: Integrates various AI capabilities, including image generation, text creation, hashtag recommendations, and more, to provide targeted solutions for different business scenarios.
- Modular design: The system adopts a highly modular architecture, allowing different brands and regions to customize the functional combination according to their own needs, achieving technology scalability and adaptability.
- Localized deployment: attaches great importance to data security to ensure that internal information is not leaked; Train the model using its own historical content to avoid losing brand tone
Case 6: Shutterstock
Founded in the United States in 2003, Shutterstock is the world’s leading digital content licensing platform, selling the rights to high-quality digital assets such as images, videos, music, and illustrations.
The company’s business model is simple: artists and creators can upload their work to the platform, which then offers on-demand downloads to creative professionals or subscriptions to corporate customers.
After the copyright of the work is sold, the author will naturally receive a certain percentage of dividends.
Shutterstock quickly realized that providing “legitimate and trustworthy” AI-generated content services that address the market’s pain points about AI content copyright is likely to be a completely blank market opportunity.
Based on these insights, Shutterstock developed a three-step transformation strategy:
Step 1: Establish strategic cooperation and take the initiative in technology
In October 2022, Shutterstock announced a partnership with OpenAI:
- Gain access to AI technology by authorizing training data, not being marginalized
- Establish a business model for selling AI training data, create new revenue streams for content creators, and provide “armament support” for AI companies
- Seize the “legitimate AI content” market positioning and turn potential threats into differentiated advantages
Step 2: Launch a creator compensation program to address ethical dilemmas
Shutterstock recognizes that any AI transformation must address creator rights. At the end of 2022,
The company launched a creator fund, promising to return a portion of the revenue from AI collaborations to content contributors. This measure is solved
Key ethical issues were resolved:
- Confirm that the creator has a reasonable interest in the content used to train the AI
- Establish a transparent compensation mechanism to maintain the creator ecosystem
- Form ethical differentiation from other AI platforms
Step 3: Integrate AI creative tools to reshape the value proposition
In early 2023, Shutterstock integrated AI image generators into its platform, repositioning the company’s role: from a mere content provider to a creative solutions provider.
This article is quoted from “[China Europe International Business School – Tezan Technology – Growth Black Box] Blueprint for Business Evolution in the AI Era”.
