At an AI-related forum during the 2026 Beijing International High-tech Expo, Wang Feng, the technical lead of Beijing Yunao Intelligent Technology Co., Ltd., delivered a keynote speech titled "Xuanjia Intelligent Agent: Enterprise Communication AI Driven by MeshAI." Wang Feng stated that the AI industry is currently accelerating its transition from a technology demonstration phase to an industrial implementation phase. The core of enterprise-level AI competition is shifting from singular model capabilities to a comprehensive contest involving communication capabilities, industry understanding, and compliant delivery.

"What enterprises truly need is not an AI that can chat, but an AI that can genuinely integrate into enterprise communication systems, undertake business processes, and create closed-loop value," Wang Feng emphasized.

As one of the key topics in the AI segment of this expo, "how AI can truly be implemented in industries" became a focal point of discussion. Compared to showcasing the capabilities of general-purpose large models, the new competitive direction in the industry is how enterprise-level AI can enter real business scenarios to solve problems related to cost reduction and efficiency improvement.

**Enterprise AI Enters the 'Deep Water Zone': From Model Competition to System Competition**

Wang Feng pointed out in his speech that many enterprise AI projects remain stuck in a "demonstrable but difficult to implement" stage. The core reasons are the common shortcomings in traditional solutions, such as insufficient communication capabilities, lack of industry knowledge, and weak security and compliance capabilities.

He noted that while many large models possess text generation and dialogue capabilities, they cannot support enterprise-level, high-concurrency, real-time communication scenarios. In industries like insurance, government affairs, and healthcare, once deployed in real business environments, systems often suffer from issues like high voice latency, insufficient stability, and even call interruptions.

Simultaneously, industry scenarios impose extremely high demands on processes, knowledge, and regulatory requirements, making it difficult for general AI models to adapt directly.

"Insurance, government affairs, and healthcare are not simple Q&A scenarios; they are business systems with strong processes, strict rules, and heavy regulation," Wang Feng explained. Without industry knowledge graphs and business logic capabilities, enterprises often require extensive secondary development, making it hard to achieve scalable replication of AI projects.

Furthermore, as data security requirements continue to rise, industries like finance and government affairs are rapidly increasing their demands for private deployment, data localization, Level-3 classified protection, and adaptation to information technology innovation ecosystems.

Industry insiders believe this indicates that the enterprise AI market has entered a stage of "competition in systems engineering capabilities." Relying solely on the capabilities of general models is no longer sufficient to establish a genuine industrial barrier.

**'Communication-Native + AI Integration' Emerges as a New Path for Enterprise AI**

To address these industry challenges, Yunao Intelligent proposes a technical approach of "deep integration of communication-native architecture with large models."

Wang Feng stated that the traditional model often involves a patchwork approach of "large model + third-party communication interfaces." In contrast, Yunao Intelligent has redesigned the AI system architecture from the communication foundation, natively integrating communication capabilities, AI capabilities, and industry scenarios.

According to information disclosed at the event, Yunao Intelligent has built a complete three-layer technical system: * The bottom layer is the communication foundation, MeshCC, featuring a self-developed distributed communication kernel that supports various communication capabilities like voice, SMS, video, and IM, with carrier-grade stability of 99.99%. * The middle layer is the intelligent agent platform, MeshAI, which uses a unified API to be compatible with mainstream large models, enabling flexible multi-model switching. It also integrates multimodal voice processing, ultra-long context memory, and enterprise knowledge base capabilities. * The upper layer provides scenario-specific solutions directly deployable in industries like insurance, government affairs, and healthcare.

"We are not simply selling AI tools; we are delivering business capabilities that can be truly implemented," Wang Feng said.

This direction also reflects a significant shift in the current enterprise AI market. As the capabilities of large models gradually converge, industry competition is shifting from "model parameters" to the "depth of business integration."

Especially in high-frequency communication scenarios like customer service, outbound calls, follow-ups, and quality inspection, AI not only needs dialogue capabilities but also must simultaneously perform complex business actions such as process coordination, work order generation, data archiving, and compliance auditing.

**'Digital Employees' Begin Entering Real Business Systems**

At this expo, Yunao Intelligent prominently showcased its Deeptalker digital employee system.

Unlike traditional customer service robots, Deeptalker emphasizes a "performance-duty AI" positioning, meaning the AI is responsible not just for conversation but also for undertaking actual business processes.

According to the introduction, the system currently covers multiple scenarios including insurance claims processing, renewal reminders, health follow-ups, government affairs consultation, public opinion surveys, and healthcare chronic disease management.

For example, in insurance claims scenarios, the AI can conduct real-time calls with customers while simultaneously completing tasks like work order generation, survey guidance, photo uploads, and business coordination. In government affairs scenarios, it can automatically handle policy inquiries, satisfaction surveys, and result archiving.

Wang Feng stated that the future development direction of enterprise AI is no longer "assistive tools" but "digital employees" with genuine performance capabilities.

Data disclosed on-site shows that the Deeptalker digital employees currently achieve a human efficiency substitution ratio of approximately 1:15, with labor costs reduced by up to 60%. In a case study with a leading insurance company, the system increased outbound call conversion rates by 25% and improved claims processing efficiency by 50% after deployment.

Industry observers believe that as enterprise labor costs continue to rise and AI capabilities mature, "digital employees" are expected to become an important component of future enterprise organizational structures.

**Enterprise AI Competition Shifts Focus to ROI and Delivery Capability**

Notably, compared to the industry's general emphasis on model capabilities, Yunao Intelligent repeatedly stressed "result delivery" and "quantifiable ROI" in its expo presentation.

Wang Feng stated that enterprises ultimately care about whether AI can truly deliver business value, not just technological demonstration.

Based on this, Yunao Intelligent proposes quantifiable delivery centered on metrics like conversion rate improvement, labor cost reduction, and service efficiency optimization. It also introduced a cooperation mechanism involving "service fee refund if performance targets are not met."

Industry insiders view this as reflecting a shift in the enterprise AI market from being "concept-driven" to being "operation-driven."

In the past, many AI projects remained in the Proof of Concept (POC) validation stage. As the industry matures, enterprises are paying more attention to whether systems can operate stably long-term, possess scalable replication capabilities, and can genuinely integrate into core business workflows.

This also implies that the core of future AI industry competition will not just be the models themselves, but a comprehensive capability system encompassing communication, industry expertise, delivery, security, and ecosystem.

**Multi-Agent Systems and Industry Models Become Key Focus for Next Phase**

Regarding future plans, Wang Feng revealed in his speech that Yunao Intelligent will focus on advancing capabilities in multi-agent collaboration, cross-session long-term memory, lightweight edge-side deployment, and low-code AI orchestration.

According to the plan, the company will also launch dedicated industry-specific large models for insurance, government affairs, and healthcare, while simultaneously advancing its market layout in Southeast Asia.

"The real value of AI lies not in the demonstration phase, but in the daily, real business processes of enterprises," Wang Feng stated. "Those who can truly bridge the last mile of enterprise communication will be the ones who can genuinely enter the core scenarios of the industry."