Positive Reviews: AI Reconstructs the Marketing Value Chain, Unleashing Efficiency and Innovation Dividends
The transformation of marketing by AI has evolved from “tool assistance” to a “strategic hub.” The efficiency improvement, experience upgrade, and decision – making enhancement it brings are reshaping the underlying logic of the entire industry.
First of all, the “Industrial Revolution – level breakthrough in cost reduction and efficiency improvement” is particularly remarkable. Technologies such as digital human live – streaming and AI content generation have completely rewritten the “labor – intensive” characteristics of marketing. For example, during the 618 promotion, the digital human “Luoyonghao” used the knowledge base 13,000 times and generated 97,000 words of explanation content, equivalent to the information volume of 130 copies of the “Ci Hai” and 18 hours of manual typing. Tencent’s “Wonderful Digital Human” has achieved 7×24 – hour unattended live – streaming, compressing the costs of short – video production, interaction, and conversion by 90%. A traditional MCN agency that used to require a 7 – 8 – person team to produce 8,000 videos per month can now complete the task with only 2 people, and the operators don’t need industry backgrounds. This efficiency improvement not only lowers the entry threshold for small and medium – sized enterprises but also allows brands to concentrate resources on core strategies rather than basic execution.
Secondly, the user experience has evolved from “one – way output” to “co – creation by all”, redefining the relationship between brands and users. McDonald’s launched the “M’s Family Heirloom” cultural relic reproduction competition through AI. Users can freely choose materials such as bronze and agate to generate exclusive cultural relics, transforming the fast – food symbol into a cultural carrier. The AI artists on Xiaohongshu deconstructed the Big Mac into creative works like the “Bronze Taotie – patterned Ding,” triggering an explosion of UGC. This “brand provides the stage, users perform” model not only enhances user participation but also, through AI’s accurate analysis of the preference for Chinese – style fashion, predicts the potential for content to go viral, achieving a leap from “traffic harvesting” to “emotional precipitation.”
Thirdly, the data – driven market insight and decision – making closed – loop have shifted marketing from “empiricism” to “scientism.” The case of Netflix is highly representative: by analyzing 95 billion hours of user viewing data, it accurately identified the potential of theme combinations such as “dystopia + family ethics,” which promoted the renewal of “The Glory”; it predicted the global spread explosion point of the “violent aesthetics” symbol in “Squid Game,” realizing an intelligent closed – loop for content development and marketing. On the personalized recommendation side, AI generated different covers (romantic version, comedy version) for “Good Will Hunting” and customized horror or youth – themed trailers for “Stranger Things,” significantly improving user stickiness and contributing over 95 billion hours of viewing time. This closed – loop of “watching – analyzing – creating – verifying” essentially transforms user behavior data into a “real – time map” for business decisions, greatly reducing the risk of decision – making errors.
Finally, the technological evolution has shifted from “single – point tools” to a “multi – Agent collaborative ecosystem”, promoting the intelligence of the entire marketing chain. Alibaba’s Mama’s full – chain AI system of “insight – strategy – content – placement – attribution” mines high – potential customer groups through 1 billion – level user behavior data, helping Midea Air Conditioners increase ROI by 40% during the 618 promotion. Amazon’s closed – loop system of “demand forecasting – production – logistics” has penetrated AI into the entire technology stack from chips to applications. Multi – Agent collaboration not only achieves seamless connection between various links (such as demand forecasting directly driving production) but also makes marketing the “nerve center” of the enterprise’s overall strategy rather than an “execution tool” for an independent department.
Negative Reviews: Hidden Worries Amidst the Rush for Efficiency, with Ecosystem Imbalance and Ethical Challenges Prominent
Behind the prosperity of AI marketing lies a multitude of contradictions, including monopoly by giants, the digital divide, job replacement, and ethical risks. If these issues are not resolved, they may undermine the long – term healthy development of the industry.
Firstly, the monopoly of giants exacerbates ecosystem imbalance, leaving small and medium – sized service providers struggling to survive. Cloud giants (Amazon, Tencent), AI platforms (OpenAI), and large – scale media platforms (Meta, ByteDance) have built triple barriers of “technology – data – traffic” with their advantages in technology, data, and computing power. Amazon’s vertical integration from self – developed chips (Trainium), basic models (Nova) to advertising business led to an advertising revenue of $13.9 billion in the first quarter of 2025, a year – on – year increase of 19%. Tencent, relying on the 1 billion – level user behavior data of WeChat and QQ, squeezes the living space of small and medium – sized marketing service providers. Small and medium – sized service providers mostly rely on fine – tuning open – source models, and their functions are limited to single – point tools such as copywriting generation. Their creative diversity is only 30% of that of leading systems, forcing them to sink into regional markets (such as Shushangyun targeting Southeast Asia) or transform into vertical Agents. This “winner – takes – all” pattern may suppress the innovation vitality of the industry, and small and medium – sized players may become “data fuel.”
Secondly, the digital divide on the demand side is widening, and small and medium – sized enterprises are becoming “data vassals”. Leading brands (such as L’Oréal, Nike) have mastered the sovereignty of core data by building their own AI teams (L’Oréal’s CREAITECH laboratory) or acquiring data companies (Nike’s acquisition of Celect), and they may even “collude” with platforms to set traffic distribution rules. Small and medium – sized enterprises, due to insufficient data volume, have difficulty effectively using AI tools, and their marketing effects are limited. Eventually, they become “free data contributors” to the platforms. For example, although AI has lowered the threshold for technology use, leading brands can continuously strengthen their advantages through the positive feedback loop of “data – algorithm – resources,” while small and medium – sized enterprises may fall into a vicious cycle of “the more they use, the weaker they become” due to lack of data accumulation, further widening the “resource gap.”
Thirdly, the rise of GEO brings risks of traffic entry migration, and the algorithm black box threatens fairness. Generative Engine Optimization (GEO), as a new traffic entry, is fundamentally different from traditional SEO: its target is the generative AI engine, and the optimization method relies on semantic relevance, with the results directly integrated into AI answers. However, GEO service providers (such as Profound) face a double squeeze: traditional SEO companies are transforming with their customer resources, and the high – frequency iteration of large – model platform algorithms (updated weekly) makes optimization strategies ineffective, and the attribution of results is ambiguous (it’s hard to tell whether the increase in exposure is due to GEO optimization or algorithm adjustment). More seriously, GEO may be异化 into a tool for “algorithm – level monopoly”: leading brands can buy out professional information sources with sky – high budgets, manipulate AI’s judgment of “authority,” and small and medium – sized enterprises may even lose the chance of being “seen” by AI. If AI platforms open “bidding recommendation” positions without clarifying the mechanism, users may think they are getting neutral information but actually encounter paid advertisements, which may pose safety hazards in fields such as healthcare.
Fourthly, job replacement and the skills gap are endangering the survival of grass – roots positions. The replacement of basic execution positions such as copywriting, design, and placement optimization by AI has become a trend: a traditional 10 – 15 – person operation team used to support a GMV of 1 billion, but now only 2 – 3 people plus AI tools are needed; an independent AI creative studio can complete an advertising blockbuster in 10 days, which used to take a traditional 4A agency 6 – 8 weeks. Microsoft predicts that by 2030, 95% of code will be generated by AI, and basic execution positions in the marketing field also face a replacement risk of over 90%. However, transformation is not easy: executors need to upgrade from “operators” to “strategists/AI tuners,” which requires compound abilities such as product knowledge, technology understanding, and emotional insight. Most practitioners lack relevant training and may be trapped in the dilemma of “being replaced but unable to transform.”
Fifthly, the lag in ethics and supervision allows technological rationality to squeeze out human values. The “worship of efficiency” in AI marketing is blurring the boundaries of business ethics: AI digital humans induce the elderly to buy inferior products through “talking points for lonely elders” and “health – anxiety scripts,” transforming marketing into “emotional manipulation”; OpenAI was fined 15 million euros by GDPR for user data leakage, exposing blind spots in data sovereignty and copyright supervision; GEO may form an “algorithm – level information cocoon,” where leading enterprises manipulate AI recommendation results, misleading consumer decisions. More importantly, although AI can optimize user behavior data, it is difficult to replicate the “thrill of a teenager stealing a sip from grandma’s teacup.” When emotional warmth is devoured by algorithms, the connection between brands and users will become a “data transaction” rather than a “value resonance.”
Advice for Entrepreneurs: Find a Balance between Efficiency and Humanity, Build Differentiated Survival Capabilities
Facing the opportunities and challenges of AI marketing, entrepreneurs need to grasp the following key points:
Make good use of AI tools, focus on vertical scenarios: Avoid direct competition with giants in the general field. Deeply cultivate niche markets (such as overseas expansion, vertical industries) to build differentiated advantages. For example, Navos focuses on the entire overseas marketing chain and can complete a full – case project in 3 days, which used to take a traditional 4A agency 3 months; Youche Technology provides AI marketing services for the automotive industry and can complete an advertising blockbuster in 10 days. Entrepreneurs can use AI tools (such as Alibaba Mama’s AIGC, Tencent’s “Spirit Animation Canvas”) to reduce content generation and placement costs. At the same time, train vertical Agents combined with industry knowledge to improve the accuracy of creativity and user resonance.
Balance technology and humanity, strengthen emotional connection: AI can solve efficiency problems, but users’ emotional needs need to be met by “people.” Entrepreneurs should use AI as an “efficiency engine” and human creativity and emotional insight as the “core barrier.” For example, McDonald’s inspired user co – creation through the “AI Cultural Relic Reproduction Competition,” which not only utilized AI’s content generation ability but also retained the brand’s cultural warmth; the explosion of 45 million creators on the Keling AI platform is because AI has liberated basic execution, allowing humans to focus on higher – value creative thinking.
Be vigilant about data sovereignty, build private assets: Avoid over – relying on platform data. Accumulate user behavior data through private – domain traffic (such as enterprise WeChat, independent APPs) to build a self – owned “data asset library.” For example, Nike acquired Celect to build a privatized demand forecasting engine and mastered the sovereignty of core data; small and medium – sized enterprises can precipitate user preference data through membership systems, community operations, etc., and combine AI tools to improve marketing accuracy, reducing their “data dependence” on platforms.
Pay attention to ethical compliance, establish risk defense lines: Lay out compliance mechanisms for data privacy, content copyright, algorithm fairness, etc. in advance. For example, clearly define the scope of authorization when using user data to avoid risks similar to OpenAI’s “data abuse”; mark “AI – created” on AI – generated content to avoid misleading consumers; refuse “bidding manipulation” in GEO optimization to maintain the authenticity of information. Ethical compliance is not only a legal requirement but also the cornerstone of long – term brand trust.
Embrace human – machine collaboration, upgrade team capabilities: Promote the transformation of the team from an “execution – oriented” to a “strategy – oriented” one, and cultivate new roles such as “AI tuners” and “emotional insight analysts.” For example, train employees to master the use of AI tools (such as AIGC content generation, intelligent placement optimization), and at the same time strengthen their strategic thinking (such as brand positioning, user emotional analysis) and ethical judgment (such as identifying algorithmic bias) abilities. Cross – functional agile teams (marketing + technology + data) are the future trend, and entrepreneurs need to break down departmental barriers to achieve in – depth integration of capabilities.
The transformation of AI marketing is essentially a double – movement of an “efficiency revolution” and a “return to humanity.” Entrepreneurs need to embrace technology while adhering to the irreplaceable values of humans – strategic vision, emotional connection, and ethical bottom – line. Only in this way can they build a core competitiveness that combines “efficiency and warmth” in the wave of AI and achieve long – term sustainable development.
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