Amid data fragmentation, tightening privacy regulations and rapidly evolving user demands, advertisers have long been pursuing more efficient delivery methods in the digital marketing wave.

In 2025, with the rapid advancement of large AI model technology, programmatic advertising is undergoing a profound transformation driven by these models. According to the 2025 Research Report on AI Application Trends in China's Digital Marketing Industry, AI technology has permeated the entire marketing chain. It not only enables deep insights into user behavior but also delivers efficient predictions and personalized delivery, making programmatic advertising truly "understand" users. So how exactly are large AI models reshaping the key pathways of programmatic advertising and helping advertisers gain a competitive edge in the fierce market?

01 Natural Language Interaction: From "Tag-Based Targeting" to "Semantic Understanding"

Traditional programmatic advertising relies on cookies and user behavior tags such as interests, geography and devices for targeted delivery, but this approach is limited by data fragmentation and privacy compliance issues. In contrast, large AI models, through natural language processing (NLP) and semantic understanding capabilities, can directly parse advertisers' marketing needs without relying on complex tag combinations.

Continuous Semantic Space

Traditional tag-based targeting depends on discrete classification systems, such as "25-35 years old", "male" and "automotive interest". Most of these tag dimensions are manually preset, resulting in fragmented user profiles. Large AI models, however, can map user behaviors and content features into a continuous semantic space, automatically capturing underlying correlations between tags without relying on seed users or tag libraries, breaking through the limitations of manual classification. A user's semantic vector is dynamically adjusted with real-time interactions—for example, after searching for "family travel", the model automatically associates demands such as "SUV" and "family-friendly hotels", whereas traditional tags require manual updates and suffer from lag.

Latent Intent Inference

Traditional tag-based targeting is essentially feature matching. For example, an advertiser selects the "sports shoes" tag to reach users with the "fitness" tag, but actual user needs may be more complex. A user browsing running shoes might actually be "preparing for a marathon" rather than simply shopping. Large AI models can analyze the global semantics of a user's current session and browsing path through the attention mechanism. For instance, consecutive views of "knee braces" and "pace training" can indicate professional running needs, rather than treating each behavior tag in isolation. They also break down advertiser demands into multi-layered semantics and perform probabilistic matching with users' implicit intents, achieving precise demand-scenario mapping.

02 Deep User Profiling: From "One-Size-Fits-All" to "Hyper-Personalization"

In traditional advertising delivery, user profiles are often based on basic tags such as age, gender and geography, making it difficult to efficiently reach target users. Large AI models, through deep learning and multi-dimensional data analysis, can build far more comprehensive user profiles.

Personalized Content Generation

Generative AI-powered advertising systems can create unique content experiences for each user. They go beyond basic user attributes to deeply mine deeper characteristics such as cultural backgrounds, language habits, hobbies and consumption preferences, providing advertisers with more targeted delivery strategies. For example, by analyzing users' browsing history, click preferences and social interaction data, the same user can receive distinct advertising content at different times and in different scenarios, truly realizing "one-to-one personalized strategies".

Continuous Dialogue Mechanism

AI-driven interactive advertising breaks through the limitations of single exposure. Microsoft's newly launched brand agents can maintain long-term interactions with users through intelligent conversations. This mechanism allows brands to accompany users throughout their entire decision journey, providing timely information support at different stages and building long-lasting commercial relationships based on trust. By analyzing users' interaction behaviors, dwell time and other information, AI systems can perceive emotional changes and adjust communication strategies, significantly improving user acceptance and brand favorability, and establishing long-term user relationships.

03 Delivery Strategy Optimization: From "Experience-Driven Navigation" to "Predictive Decision-Making"

Traditional experience-based delivery strategies often rely on marketers' industry intuition and accumulated past cases. This model has obvious limitations and cannot timely capture subtle fluctuations in market demand. The "predictive decision-making" model empowered by large AI models is reconstructing the entire system of refined operations.

Predictive Algorithm Matrix

In 2025, AI delivery systems no longer rely on post-hoc analysis of historical data. Instead, they build complex market simulation environments to predict changes in user demand and industry trends. AI Agents can perceive market changes in real time and dynamically optimize strategies. This forward-looking capability enables advertisers to seize market opportunities and precisely lay out marketing touchpoints before user demands become explicit, reshaping the underlying logic of delivery decisions.

Adaptive Optimization Network

Delivery algorithms based on deep reinforcement learning can, like experienced marketing experts, continuously monitor market feedback and adjust delivery parameters. Bidding strategies, audience selection and budget allocation no longer require frequent manual intervention. The system can complete tens of thousands of optimization decisions at the millisecond level, keeping marketing campaigns in optimal condition at all times.

04 Intelligent Creative Generation: From "Single Templates" to "Dynamic Generation"

Creativity is the soul of advertising, and large AI models have demonstrated enormous potential in creative generation. Diverse technologies not only enhance the richness and personalization of advertising content but also significantly shorten production cycles and reduce costs.

Dynamic Content Engine

By deep learning from massive amounts of high-quality creative content, large AI models have developed the ability to autonomously generate high-quality advertising creatives. Unlike traditional design processes that rely on templates and manual work, the new generation of AI creative systems can generate hundreds of creative solutions with different styles in real time based on brand tone, product features and marketing objectives, completely breaking the quantity bottleneck of content production. They can also collect user feedback data in real time and automatically optimize generation strategies, keeping advertising content fresh and appealing.

User Context Awareness

Cutting-edge AI creative platforms can parse multi-dimensional contextual information such as user devices, geographic locations and time nodes in real time, and automatically adjust advertising presentation forms. The same product may be presented as a concise and powerful short video during commute hours, and as a narrative long copy during evening leisure hours, truly achieving the intelligent adaptation of "context is content, and content is context".

In the 2025 advertising market, programmatic advertising will see more innovations and transformations. With the continuous enrichment of open-source protocols, advertisers will have greater autonomy to quickly access diverse AI services and build more competitive advertising ecosystems. For advertisers, deeply integrating AI strategies with business operations is not only the key to improving advertising effectiveness but also an imperative for winning market competition.

As a technology-driven domestic programmatic growth and monetization platform, Yiliang Yuedong is providing a one-stop solution for efficient monetization and precise delivery in this intelligent marketing transformation. It not only offers advanced technical insights but also delivers mature, implementable and measurable solutions that create tangible business value, and provides multi-strategy parallel creative solutions empowered by AI.