AI assistance: see how brands put it into practice

This article was originally published in the “Economic Daily” column “Digital Marketing/AI Assistance: See how brands implement it (opens in a new tab)” (October 5, 2026)

The popularity of generative artificial intelligence has caused countless brands to fall into the illusion of productivity. A large number of companies rushed to introduce AI, but what they got in return was a same tone of voice and consumers’ visible indifference. When technology reduces the marginal cost of content to close to zero, brands that purely pursue speed and savings will inevitably fall into the quagmire of homogenization.

Brand AI empowerment that can truly widen the competitive gap does not treat technology as a content copier, but transforms it into a system architecture that reshapes strategic thinking, dynamic experience, and product co-creation. This means that companies must move from vague tool theory to rigorous methodology and complete verification in real business scenarios.

Reconstruct consumer insights with dynamic intent model

The first practical entry point for this revolution is to abandon the static audience profile and reconstruct consumer insights with a dynamic intent model.

For example, the beauty giant L’Oréal uses its patented big data and algorithm platform to process millions of social video and text messages around the world every day. The system does not simply count the frequency of keywords, but identifies emerging beauty micro-trends in non-mainstream communities, allowing the R&D and marketing teams to accurately predict and deploy product matrices months before a specific formula or makeup becomes mainstream, compressing market research that often took a year into a few weeks.

When Coca-Cola promotes its “Create Real Magic” project, it combines OpenAI’s technology to allow consumers and artists to create creations using its iconic Coke bottles, curved logos, and historical advertising images. Coca-Cola did not allow AI to freely develop and dilute the brand characteristics. Instead, it set strict brand visual boundaries, allowing global users to produce thousands of artistic works of different styles in a consistent brand context. This not only solved the production bottleneck of localized materials in multi-regional markets, but also allowed marketing to evolve from one-way communication to high-viscosity large-scale co-creation.

Give AI a distinctive brand personality

At the interactive level of the customer journey, the core of the method is to give the AI system a distinctive brand personality and break the cold question-and-answer logic. For example, the Swedish oat milk brand Oatly has written its consistent anti-bone, self-deprecating and humorous tone into large-scale models in its digital interactions.

When consumers talk to the system, what they receive is not a standard “Please wait, we will transfer you to customer service”, but a humorous response with a strong brand personality. Even when answering questions about the origin of oats and carbon footprint, it can cleverly maintain its rebellious image of advocating sustainability.

In its digital ecosystem, German sports brand Adidas no longer just recommends similar shoes based on browsing history, but integrates users’ running frequency, local real-time climate data and personal training goals to dynamically generate customized exercise suggestions and exclusive visual interfaces.

When a runner opens the app on the eve of the rainy season, what the system delivers is not a cookie-cutter new product poster, but a set of features analysis of water-repellent running shoes optimized for slippery roads, paired with a progressive training schedule that meets the runner’s physical fitness goals.

Let market feedback flow back into R&D

The last link of this system is to return feedback from the end of the market as living water for product development, forming a closed loop from data to manufacturing. Starbucks, the world’s largest coffee chain, uses its artificial intelligence platform called Deep Brew to integrate the consumption habits of tens of millions of members around the world, weather changes in store locations, and real-time inventory status.

After analyzing a large amount of information about the proportion of plant milk, specific syrup preferences, and the alternation of hot and cold drinks at different times, the R&D team was able to accurately refine new product concepts with high potential.

Conclusion: AI is an amplifier of cognition and execution

Looking at these implementation practices, successful brands have never regarded artificial intelligence as a substitute for creativity, but rather positioned it as an amplifier of cognition and execution. The highest level of brand AI empowerment is by no means turning a brand into a cold and efficient computing machine, but using technology to remove complex structural baggage, allowing brands to re-enter consumers’ real lives with a more agile attitude and deeper empathy.

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*Source: Vista Cheng (鄭緯筌), “Digital marketing/AI assists to see how brands are put into practice (opens in a new tab)”, “Economic Daily”, October 5, 2026. *