The marketing sector is undergoing a profound transformation, shifting from "localized tool application" to "comprehensive logic restructuring," driven by the continuous penetration of artificial intelligence. Currently, AI applications in marketing are flourishing, achieving end-to-end coverage and individual process empowerment—from insight generation and creative development to campaign deployment and operational management. This adoption is expanding from leading brands to small and medium-sized enterprises. Overall, AI undeniably enhances marketing efficiency; however, brands still face multiple practical challenges in implementation, including data fragmentation, a lack of vertical domain knowledge in models, insufficient generative and reasoning capabilities, and limitations in cross-process integration.
Looking ahead, the deeper changes brought by AI lie in the shift of traffic entry points and the restructuring of decision-making mechanisms. With significantly improved capabilities in information integration, filtering, and decision-making, AI is evolving into a new core traffic gateway, gaining control over critical information distribution rights. This implies that brands' existing communication channels may become partially ineffective, leading to a passive situation due to a lack of "machine-readable assets." Furthermore, as the AI Agent paradigm develops into a closed-loop system, user experience and interaction habits may be fundamentally reshaped: the underlying logic of purchasing will shift from "emotional connection with the brand" to "algorithmic trust based on Agent evaluation." This restructuring of the trust mechanism will not only weaken direct emotional engagement between brands and consumers but also trigger a profound divergence in brand value orientations.
Thus, whether in current single-process empowerment or future marketing mechanism restructuring, AI is driving marketing toward a structural transformation. In this context, how should brands re-examine their marketing strategies? As the core audience extends from end consumers to AI assistants, how should data systems and model architectures be restructured accordingly? How can brands compete for "information influence" in the AI era through new strategies such as GEO/AEO? This article will provide a systematic analysis based on application scenarios, offering actionable AI deployment insights for brands and stakeholders to gain a competitive edge in the new marketing paradigm shift.
**Current State: AI Empowers Brands' Single-Process Productivity** New AI technologies, represented by large language models, are deeply reshaping the entire marketing value chain, effectively enhancing brands' productivity. This transformation spans four core marketing stages: opportunity insight, content creation, precise deployment, and operational conversion. While driving efficiency improvements and model innovation, it also exposes challenges related to data and models. The following sections detail how brands can systematically adjust their strategies to more effectively address AI limitations, leverage its capabilities, and build differentiated advantages.
(1) Opportunity Insight: The core value of AI lies in its multimodal understanding capabilities, enabling real-time, multi-dimensional judgment of market signals and introducing a new paradigm for market research, significantly enhancing the comprehensiveness and granularity of insights. Specifically, leveraging vast multi-source data—such as social media interactions, news trends, competitor activities, and user behaviors—large models can understand unstructured multimodal content, use contextual reasoning to capture changes in user interests, sentiment tendencies, and consumption intent in real time, and even proactively identify emerging needs and potential public opinion risks from faint market signals.
For example, a domestic brand used an AI monitoring system to predict the domestic spread of overseas complaints three hours in advance, enabling a rapid response that contained negative sentiment to one-third of the industry average. This shifts brand decision-making from relying on long-term historical experience to dynamic data-driven approaches, establishing an insight foundation for real-time strategy adjustments through continuous monitoring of market demands and risks. Additionally, emerging AI-native tools, such as digital twin Agents, enable low-cost information collection by simulating user behaviors, preferences, and decision-making processes, offering new technical pathways for agile insights.
However, the implementation of this capability faces two major bottlenecks: in breadth, consumer data is fragmented across super-app ecosystems, making it difficult for AI to model complete user profiles across work, social, entertainment, and shopping scenarios; in depth, general-purpose models lack structured knowledge in vertical domains, struggling to meet industry-specific deep insight needs. To address these issues, brands must prioritize data accumulation, not only integrating existing data but also building unstructured insight systems covering diverse content formats like short videos, live streams, notes, and reviews to maximize AI value. Brands with relevant technical capabilities can train lightweight vertical models using unique user data, product knowledge, and industry methodologies to reduce reliance on general models and improve precision in user insights and opportunity identification within specific contexts.
(2) Content Creation: The core value of AIGC lies in supporting brands to consistently output high-level creative concepts and relatively controllable marketing materials, achieving low-cost, scalable productivity breakthroughs. Specifically, at the creative origin, AI transforms upstream insights into numerous preliminary creative concepts based on multidimensional tags such as marketing subjects, campaign scenarios, and target users, alleviating human cognitive limitations. A more significant empowerment lies in multimodal content production; compared to traditional labor-intensive creation reliant on professionals, generative AI achieves substantial cost reduction and efficiency gains.
According to ByteDance's data, the entire AIGC workflow—from creative scripting to final review—takes only 3.5 hours, saving material shooting costs of 1,200–2,500 per day. Simultaneously, mass content production will drive content strategies from "limited universalization" to "infinite personalization" through real-time iteration, enhancing brands' marketing capabilities at the content source. However, AI currently plays a primarily辅助 role. First, while it can replace some content production, the integration of front-end insights, overall content planning, and subsequent testing analysis and judgment still heavily rely on human input. Second, generative effects have shortcomings, including reliability and consistency "hallucination" issues. Third, models tend to reuse high-frequency creatives and common styles, leading to visual and copy similarities across different brands' outputs.
This also triggers some consumer resistance to AI-generated content, with negative feedback such as "too much AI flavor," posing additional challenges for brands in building emotional connections and user trust. Consequently, while productivity explosions make "winning by volume" possible, brands must be vigilant about the risk of homogeneous content flooding the market. Correspondingly, the core value of designers will shift from "execution and production" to "aesthetics and optimization," i.e., excelling in prompt design, material selection, iteration, and coordinating workflow integration and team collaboration.
Faced with these empowerments and challenges, brands should focus more on content quality and differentiation, prioritizing personalized creativity for refined scenarios and segmented audiences. Firstly, brands can systematically build prompt libraries, presetting structured templates for different channels, audiences, and product selling points to enhance the usability and controllability of generated content. Secondly, to meet the need for external differentiation and internal consistency in brand tone, brands should begin constructing dedicated, high-quality multimodal databases. This can involve internal accumulation—collecting and annotating brand-specific visual materials and copy to form a unified style library—or introducing external IP resources.
For instance, OpenAI licensed over 200 Disney cartoon characters for integration into Sora and ChatGPT's asset libraries. Such IPs inherently possess high recognition and emotional added value, helping mitigate negative perceptions of purely AI-generated content and creating differentiation advantages amidst homogeneous AI creations. Furthermore, brands can use lightweight fine-tuning techniques, like LoRA, to inject granular brand visual elements, language styles, and professional knowledge into models, thereby improving reliability and consistency at the source and building marketing content barriers.
(3) Precise Deployment: The core value of AI lies in helping brands develop a refined and accurate understanding of users, enabling real-time tracking and dynamic optimization of deployment strategies to some extent, driving new breakthroughs in personalized marketing. Specifically, compared to traditional broad, static user segmentation, large models can understand composite data—including content, products, influencers, and industry trends—to build more granular, dynamic user profiles. This not only helps brands quickly identify consumer interests and conversion opportunities within critical time windows but also alleviates the "cold start" challenge arising from insufficient historical behavioral data.
Simultaneously, AI-driven fully automated deployment solutions, such as Google's PMax and ByteDance's UBMax, are becoming cutting-edge approaches. They dynamically adjust bids and budget allocations based on posterior effects, control deployment pacing, and reduce trial-and-error costs. According to ByteDance statistics, its latest automated deployment features increased average application downloads in gaming and online services by 30%, improved ROI by approximately 20% for the same game, and raised cold start pass rates by 10%. Overall, AI platforms are forming data flywheels within continuous "deployment-conversion" closed loops, gradually enhancing content distribution accuracy and moving toward the ideal model of hyper-personalization.
However, this process still faces dual constraints from data and models. On one hand, data silos between platforms make it difficult to integrate user characteristics; AI optimization is often confined to single platforms, preventing global precision across ecosystems. On the other hand, advertising deployment is inherently a complex game scenario under information asymmetry; current AI, with its limited capabilities, primarily handles execution and basic matching, while high-level strategy formulation still relies on experienced deployment experts. Notably, the value and control of these evolving AI capabilities are increasingly concentrated on the platform side, leaving brands with relatively limited strategic space to address such structural challenges.
Nevertheless, brands are not powerless. While leveraging rapidly developing channel platforms, they should actively build their own data assets. For example, brands can develop and operate direct-to-consumer (DTC) websites, brand apps, mini-programs, and member communities to directly reach consumers; by offering member benefits and personalized services, they can seek consumer data authorization. This accumulates brand-specific, high-quality first-party data, continuously refining user profiles and providing more accurate bases for cross-platform deployment, enabling brands to gradually strengthen their deployment decision-making initiative while leveraging platform capabilities.
Additionally, AI-driven performance advertising, due to conversion improvements and cost optimization, may generate certain budget surpluses. This prompts brands to rebalance budget allocations between short-term conversions and long-term brand building based on their specific needs. High-frequency consumer brands, prioritizing rapid conversion and repeat purchases, might reinvest most surpluses into performance ads to scale effects. High-ticket brands, focusing more on brand value and long-term user trust—like Geely Group, which indicated it would allocate surplus resources to brand advertising—can build enduring competitive barriers and premium capabilities.
(4) Operational Conversion: AI is expanding the breadth and depth of marketing operations, primarily reflected in enriched user experiences and refined brand management. Specifically, on one hand, AI introduces novel personalized interaction formats—such as digital human livestreams, intelligent customer service, and virtual try-ons—providing users with diverse immersive experiences. For instance, Florasis' collaboration with Perfect Corp. on an AI shade try-on feature increased lipstick try-on click-through rates on Taobao from about 20% to 40%; its virtual anchor also replaced human hosts during late-night slots, effectively reducing labor costs. Such experiential AI applications primarily accumulate brand awareness by enhancing perception and extending user dwell time, though their direct impact on conversion remains relatively limited.
On the other hand, driven by strong data capabilities, AI enables refined, automated operations. Compared to traditional fixed discount and pricing strategies, AI analyzes user profiles, behavioral data, and real-time contexts to implement intervention tactics like smart coupon distribution and dynamic pricing, effectively boosting immediate conversion efficiency. However, AI's application in operations still has significant limitations. Essentially, AI excels at optimizing clear objectives but lacks deep emotional insight, struggling to understand the underlying motives of user behaviors and often requiring human intervention for non-standard scenarios. Moreover, operational scenarios are diverse and highly specialized, making it difficult for a single model to cover all tasks.
Faced with these constraints, brands should position AI as a collaborative tool rather than a full replacement. Operationally, brands should establish standardized workflows for human-AI collaboration; organizationally, they can redefine team roles, delegating rule-based, repetitive tasks to AI while personnel focus on strategic planning, emotional connection, and creative input. It is crucial to recognize that building trust and loyalty between brands and users ultimately relies on warm, humanized interactions. AI can efficiently manage traffic, but bridging the gap from "traffic operations" to "mindshare operations" ultimately requires human wisdom and empathy.
In summary, within the current AI-driven marketing transformation, brands should prioritize building proprietary data assets as a strategic imperative to form differentiated barriers. Simultaneously, they should actively integrate external mature AI tools and, based on organizational strengths, prudently develop underlying AI capabilities to seize a competitive advantage in AI marketing.
**Medium Term: AI-Driven Value Shift in the Industry Chain** As AI technology deeply permeates the marketing industry chain, its impact is expanding from single-process efficiency gains for brands to structural adjustments on the supply side. For brands, low-threshold, democratized AI tools are becoming key capability levers. Through self-developed or fine-tuned models, or deploying established tools like DeepSeek and Midjourney, brands can achieve greater autonomy in core areas like opportunity insight and creative production, gradually reducing reliance on executable services replaceable by AI, and instead seeking higher-level technical integration and strategic synergy support.
Consequently, the value of execution services driven by human labor from traditional advertising agencies is continuously compressed, potentially significantly narrowing their生存空间, necessitating a shift toward providing high-value-added, knowledge-intensive services for brands. Simultaneously, traffic platforms—such as Tencent and ByteDance—are further expanding their influence leveraging data, algorithms, and ecosystem advantages. They are no longer mere advertising channels but are becoming full-stack AI infrastructures integrating insight, creation, deployment, and operations, potentially leading to more direct cooperation between brands and AI-enhanced platforms, centralizing the marketing value chain.
While profoundly impacting the industry chain, AI will become the next key traffic gateway, reshaping marketing paradigms and the overall ecosystem. As AI's capabilities in information integration, understanding, and decision-making leap forward, user purchase journeys will shift from "independently searching and processing fragmented information" to "requesting needs from AI and receiving integrated solutions," drastically shortening decision paths. AI becomes the core actor in information filtering, comparison, and recommendation. This means the primary entry points for user information acquisition and decision-making will gradually shift from traditional explicit interfaces—like search engines, social platforms, and e-commerce apps—to emerging native AI portals. AI will become the new decision starting point, wielding greater control over traffic and information display allocation.
This migration of gateway power will define new competitive dimensions. For brands, the marketing target will not only be end consumers; AI assistants become a "new audience" requiring priority engagement. Since AI is impervious to emotional advertising and primarily makes decisions based on objective parameters like product performance and price, the core of brand marketing shifts from influencing consumers through emotional appeals to securing a favorable position within AI's source databases and reasoning chains. Therefore, brands' marketing strategies need to evolve from traditional Search Engine Optimization (SEO) to AI-mode adaptations like Generative Engine Optimization (GEO) or AI Engine Optimization (AEO).
This involves systematically building authoritative, structured public content systems for mainstream large models like ChatGPT and DeepSeek, forming interconnected knowledge networks to enhance machine readability, ensuring brand-related positive information is accurately identified, trusted, and prioritized by AI for citation, thereby indirectly influencing final user decisions. Essentially, this is a battle for data influence in the AI era; brands need to preemptively occupy user mindshare and shape brand perception within emerging traffic gateways and information distribution ecosystems. Consequently, marketing logic will reorganize around AI's cognition and decision-making patterns, with synergy capabilities becoming a new marketing foundation, and brands' high-quality proprietary data assets constituting the core competitiveness in this model.
**Long Term: Agent Paradigm Reshapes Underlying Logic** Looking further ahead, as AI evolves beyond being a gateway for product recommendations to enabling end-to-end purchase journeys—from demand perception to service fulfillment via the Agent paradigm—the underlying logic of marketing will undergo even deeper evolution. The value increment lies in AI Agents proactively identifying user needs, acting as search and evaluation agents to execute tasks, and continuously learning from feedback post-payment, forming a self-reinforcing decision closed loop. The implementation of this model could fundamentally alter user interaction habits, with AI directly managing entire life service processes like shopping, travel, and dining reservations.
Under this model, a key change is that consumers' decision basis will shift from human brand trust to algorithmic trust in the Agent's objective, professional assessment, reconstructing the trust mechanism. This extends to brand value orientations, driving a trend toward polarization. For functionally oriented standardized products, whose core parameters are easily understood, quantified, and compared by large model algorithms, users will tend toward highly rational decision-making with AI assistance. Brand premium space is greatly compressed, competition may导向 "de-branding" extreme cost-performance models, weakening leading advantages and allowing high-performance latecomers opportunities to break through.
Conversely, since algorithms struggle to quantify subjective experiences like aesthetics, emotional value, and social identity, brands whose appeal lies in aesthetics and emotion can maintain their brand premium. Indeed, as users delegate daily rational decisions to AI and potentially save money, they may be more inclined to pay premium prices for少数 unique emotional experiences—such as "niche design products," "limited-edition sneakers," or "symbolic brands representing specific identity markers"—further strengthening brand premiums.
Brands' strategic choices will also diverge significantly. First, some brands will pursue极致的数据权威与透明化 (ultimate data authority and transparency), suitable for brands with competitive products and high confidence. They can establish detailed, standardized knowledge graphs for their products, backed by third-party authoritative channels, following a "persuade the AI" path. Second, other brands will focus on极致的情感价值创造 (ultimate emotional value creation), avoiding competition with countless generic brands on performance parameters, instead concentrating resources on building emotional connections and aesthetic experiences that AI cannot replicate, directly targeting users themselves to capture mindshare and emotional high ground.
It is crucial to警惕 (be wary of) potential new risks. Some brands, pursuing short-term gains under AI recognition, might attempt to污染数据源 (pollute data sources)—for example, injecting self-serving false data into public corpora, attacking competitors, or fabricating user reviews—to mislead AI decisions. Therefore, more robust anti-cheat algorithms, along with data and algorithm auditing, will become vital, further prompting societal reflection on AI marketing risks and driving the深化 (deepening) of AI governance norms.
In summary, AI is powerfully reshaping the marketing industry chain. Brands need not only to develop前瞻且系统的认知 (forward-looking, systematic understanding) at the strategic level but also to quickly translate this understanding into action plans, making clear deployments based on their own resource endowments to actively participate in this AI-driven transformation and build sustainable competitive advantages.