✅ Title: How Functional Features Are Shaping the Future of AI

Major global big tech companies and AI startups are accelerating competition around advanced functionalities in the large language model (LLM) market. Beyond improving model performance, the race is now focused on delivering sophisticated features and enhancing user experience to support practical business applications.

Since the beginning of this year, companies such as OpenAI, Anthropic, and Alibaba have announced successive model updates, while domestic AI companies are responding by launching industry-specific platforms.

1. Shift Towards Functionality-Driven LLM Trends

The AI industry is rapidly moving beyond the refinement of LLM performance toward functionality that supports real user tasks. A growing number of advanced interface features — including image recognition, search capabilities, and workflow automation — are being introduced, signaling a shift where AI evolves from a conversational tool to an operational asset.

Leading players like OpenAI and Anthropic are now focusing on advanced functionalities aimed at enhancing user productivity:

• OpenAI: Expanded text-to-image generation capabilities and introduced custom GPTs and API-based workflow automation features.
• Anthropic: Introduction of the Model Context Protocol (MCP), a framework designed to enable bidirectional connectivity between data and models.

This trend highlights a transition from simple interactions with AI models to practical application as functional tools. Companies are now competing not only on performance but also on operational effectiveness and applicability.

2. Global AI Company Initiatives

(1) OpenAI: Expanding Image Generation and Workflow Automation

OpenAI recently unveiled Operator, a native automation feature that extends the use of AI into backend business operations. Operator automates API requests, data processing, and workflow integration using GPT models, offering significantly improved scalability and integration compared to existing plugins and tools.

Additionally, OpenAI integrated image generation capabilities based on the GPT-4o model into ChatGPT. This feature supports text-based image creation, multi-turn image editing, visual object representation, and in-image text manipulation, enhancing both usability and output quality.

(2) Anthropic: Claude and the Model Context Protocol (MCP)

Anthropic introduced the MCP, a protocol that connects AI tools and data. MCP enables modular task instructions and autonomous code execution by the LLM. Through MCP, Claude has evolved from merely generating responses to becoming an "operational agent" capable of organizing data, automating reports, and performing calculations within a single workflow.

(3) Butterfly Effect: Manus, a Real-Time Document Editing Agent

Chinese AI startup Butterfly Effect launched Manus, an LLM agent designed to support real-time document work. Manus autonomously performs coding, analysis, and planning tasks.

Whereas traditional AI merely generated content, Manus has been recognized for evolving into a collaborative partner that assists users in editing and refining documents. It also topped the GAIA Benchmark shortly after its release, demonstrating technical excellence. However, concerns arose over a potential issue involving unintentional exposure of source code to users.

3. Domestic AI Companies Responding to the Trend

As global tech companies accelerate the functional evolution of LLMs, domestic AI providers are responding by releasing industry-specialized AI platforms and new technologies to meet market demands.

One notable example is S2W, an AI-powered Data Operations Company, which has launched the SAIP (S2W AI Platform)—a domain-specific generative AI platform designed for enterprise application.

(1) S2W: Tailored Generative AI Platform 

S2W’s SAIP addresses the limitations of general-purpose large language models by structuring internal enterprise data and industry expertise into ontology-based knowledge graphs. Leveraging multi-domain cross-analysis technology, SAIP delivers customized insights tailored to each organization's specific needs.

SAIP integrates and analyzes heterogeneous data across diverse industries, enabling accurate semantic interpretation of domain-specific nuances. This structured approach empowers enterprises to execute advanced analytics tasks such as customer behavior analysis, trends monitoring, and business analysis, supported by deep contextual understanding of industry terminology and business processes.

SAIP is actively deployed in production environments, enhancing data-driven decision-making and operational efficiencies at organizations including Lotte Members’ Segment Lab and Hyundai Steel’s Hyundai Intelligence Platform (HIP).

4. Conclusion

The generative AI market is undergoing a paradigm shift from performance-driven competition to a race centered on functional application. As AI companies accelerate efforts to deliver user-centric features and industry-specialized solutions, the ability of AI platforms to function as practical, actionable tools will be a key determinant of market leadership in the years ahead.

🧑‍💻 Author: S2W AI Team & K-RND.NET

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*Discover more about SAIP, S2W’s Generative AI Platform, in the details below.