Unlocking Commercial Value for Businesses through Multimodal AI and Video

With the advent of large language models, generative AI has gained unprecedented attention. From the initial single-modality generation (text generation) to now encompassing multiple modalities such as images, audio, and photos, multimodal models are becoming one of the core trends in the field of generative AI. According to GII's market report, the generative AI market size is projected to reach US$281.9 billion in 2029, maintaining a high compound annual growth rate of 50.87% between 2024 and 2029. 

The rapid growth of the generative AI market has led to increased demand, presenting companies with both technical challenges and escalating training costs. The widespread use of multimodal models has made balancing technological research and development with commercialization a critical concern for businesses.

Multimodal Models: The Next Wave of Artificial Intelligence

A multimodal model is an artificial intelligence system that can simultaneously process and generate different forms of data, such as text, video, and images. Its purpose is to build an interactive communication bridge between different modalities, further enhancing the ability to understand and apply information.

Traditional AI systems that analyze only text or images are single-modality. In contrast, multimodal AI models break down barriers between modalities by uniformly processing multiple modalities such as text descriptions, audio content, and visual emotional cues to generate multi-dimensional semantic representations. This expands the potential applications of artificial intelligence.

For businesses, the value of multimodal models lies in their ability to significantly improve the efficiency and accuracy of data processing, thereby optimizing decision-making processes and bringing stronger competitive advantages. With the rapid growth of data volume, traditional data analysis tools struggle to cope with the diverse and unstructured data needs, and multimodal models solve this challenge.

Unstructured data like customer feedback, meeting recordings, and market research reports can be integrated and analyzed using multimodal models to create a unified semantic representation, allowing decision-makers to quickly understand key points. Additionally, cross-analyzing data from different modalities, such as text and images, can uncover previously unseen business insights and enable the development of more forward-thinking strategies.

With the increase in technical requirements, the training costs of multimodal models are increasing exponentially. Training a large model may require hundreds of millions or even billions of dollars in investment, including hardware construction, power consumption, and data processing.

The high training costs and resource requirements also put some pressure on enterprises. To achieve a balance between technology and business, companies need to adopt effective resource allocation and cost control strategies.

From Understanding to Generation, Creating a Multi-Dimensional Interactive Experience

The Asia-Pacific region is experiencing substantial progress in AI-powered multimedia technology. Industries such as media, education, e-commerce, and enterprise are increasingly implementing innovative solutions to improve their operations.. These technologies are designed to adapt to industry-specific needs, providing efficient and scalable solutions for a rapidly evolving digital landscape: 

1. Streamlining data processing: Designed data cleaning and compression pipelines, retaining only the most valuable information. For example, for video analysis, the process will remove 80% of unnecessary pixels and focus only on key frames.
2. Batch processing and model fusion: Combine multiple data segments for analysis through batch processing technology, which greatly reduces the waste of computing resources.
3. Optimization for diverse scenarios: Focusing on deep integration with business needs, the multimodal models are optimized for different scenarios, maximizing the benefits of multimodal models in commercial implementation.

AI-driven models are increasingly applied across various fields, including knowledge management, entertainment, sports, e-commerce, and education. These technologies enable more efficient content organization, personalized user experiences, and data-driven decision-making, helping businesses and institutions streamline operations and enhance engagement.

Knowledge Management and Rapid Internal Information Retrieval

In enterprises, the scale of internal data is exploding, and most of the data exists in unstructured form, such as meeting recordings, educational videos, internal documents, and customer service records. The dispersion and diversity of these data make manual organization and retrieval extremely time-consuming.

Multimodal models can quickly structure these unstructured data through automatic labeling and summary generation technology, helping companies build efficient knowledge management systems. For example, in meeting record analysis, the model can automatically identify and extract key issues and decision points in the meeting, and generate key summaries for employees to quickly review or for decision-makers to develop the next plan.

Highlight Clips of Sports and Entertainment Content

Multimodal AI is revolutionizing sports broadcasting by accurately identifying and tagging key moments in games. For example, in baseball, the system can recognize home runs and strikeouts, while in soccer, it can detect goals and yellow card incidents. Even without explicit verbal cues from referees or commentators, the model cross-analyzes visual elements such as player movements, jersey numbers, and score changes to determine critical events.

These highlights can then be automatically edited into short clips, allowing fans to relive the most exciting moments in a fraction of the time. Furthermore, AI-driven analytics provide deeper insights into viewer preferences, enabling platforms to deliver personalized highlight reels—for instance, catering to audiences who favor goal-scoring moments.

Beyond sports, multimodal AI can be applied to entertainment and live performances, such as movies or concerts, by identifying narrative peaks, dramatic turning points, or emotional crescendos in music. This enables the automatic creation of engaging short-form previews or highlight reels that enhance viewer engagement.

E-commerce Live Streaming and Short-Form Video Analytics

Live streaming is now crucial for e-commerce, with real-time host-audience interaction significantly impacting sales. However, the extended duration of these live streams, often spanning hours, complicates content extraction and repurposing, thereby diminishing their enduring value.

Multimodal AI addresses this issue by analyzing various data points during a live stream, including spoken content, audience engagement, and product showcases. This allows for the automatic generation of promotional tags, product highlights, and curated video snippets. For instance, when a host presents a product, the AI identifies its crucial details design, functionalities, and advantages and promptly produces short, optimized content suitable for social media and retargeting campaigns.

Moreover, AI-driven analytics can further refine product presentations by identifying the items that triggered the highest audience engagement. By understanding viewer preferences, the system can generate more accurate product recommendation lists. For long-form live stream replays, AI can automatically extract promotional highlights, such as discount offers or best-selling products, significantly reducing content editing costs.

Seamless Cross-Modal Transitions: Unlocking Future AI Applications

The continuous evolution of multimodal AI highlights its vast potential in cross-modal understanding and application. With a strong foundation in the Asia-Pacific market, this technology is already driving tangible business benefits across media content management, e-commerce live streaming, and education.

Looking ahead, as multimodal AI advances toward "Any-to-Any" cross-modal conversion capabilities, businesses will gain access to more efficient data processing and decision-making tools. This shift will not only reshape business models but also redefine user experiences, accelerating the transition from single-modal to fully interactive multimodal engagements.

For enterprises seeking to stay ahead in digital transformation, integrating multimodal AI is no longer just a technological upgrade, but a strategic imperative for maintaining competitive advantage. Continued innovation and real-world applications will unlock new AI-driven possibilities, empowering businesses to optimize their operations and enhance engagement at scale.

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