# How to break the situation when 90% of the enterprise AI pilot fails? Deep Performance Intelligent DeepAgent 4.0 Pro Gives Complete Plan ## Foreword When companies around the world are talking about AI Agents (agents), a cruel data is in front of them: * * Nearly 90% of AI pilot projects have failed to achieve scale. ** The McKinsey report points out that companies close to 2/3 are still stuck in the pilot or experimental stage. Accenture research goes further: in China's large-scale application of generative AI enterprises, the real "significant value" of less than 10. Faced with this dilemma, * * Suzhou Niu Orange Network Technology Co., Ltd. * * after in-depth research on industry benchmarks, found that the DeepAgent 4.0 Pro released by Deep Performance Intelligence provides a breaking idea-* * Agentic Software (intelligent body software) + Agentic Service (intelligent body service) dual-engine mode * *. Today, we will take an in-depth analysis of this scheme to see how it can help enterprises from "AI pilot failure" to "AI value realization". # #1. dilemma diagnosis: where is the AI value cashing card? ### 1.1 the "Three Stuck Points" Theory of Deep Intelligence Huang Xiaonan, founder of Deep Performance Intelligence, pointed out in a public speech that AI value cashing is stuck in three core places: | Sticks | Problem Presentation | Root Cause | | ------ | ---------- | ---------- | | Single point scattered | AI tools are used independently in various departments, and data cannot be connected | Lack of unified planning | | Value dislocation | AI solves technical problems, but not business problems | Lack of business perspective | | The base lacks | The AI does not work effectively without a complete data infrastructure | Lack of a data foundation | ### 1.2 Single point scattered: the "island dilemma" of enterprise AI application" **Description of the phenomenon**: Many companies have introduced multiple AI systems: -AI customer service system -AI outbound system -AI content generation system -AI data analysis system But these systems are often fragmented: -Customer service data and CRM are not connected -Content system and delivery system are not linked -Data is scattered in various systems and cannot form a synergy **Result**: Every AI tool appears to be working, but the overall efficiency is not significantly improved. ### 1.3 value dislocation: technology-oriented rather than business-oriented **Description of the phenomenon**: Many enterprises in the introduction of AI, the mode of thinking is: -This AI technology is very advanced -This AI is very powerful -This AI manufacturer is very famous But it ignores the most important question: -What business problems can this AI solve? -How much commercial value can this AI bring? -Is the input-output ratio reasonable? **Result**: The technology is advanced, but the business is not growing. ### Lack of 1.4 base: inadequate data infrastructure **Description of the phenomenon**: The data situation of many enterprises is worrying: -Data is scattered across systems and cannot be accessed uniformly -Data standards are not uniform, the same indicator calculation caliber is different -Poor data quality, large number of missing and erroneous values -Data governance missing, no data owner **Result**:AI the system "Garbage in, Garbage out", the input is garbage, and the output is garbage. # #2. Breaking Plan: Agentic Dual Engine Mode ### 2.1 What is Agentic Dual Engine Mode? Deep performance intelligence proposed * * Agentic dual-engine model * *, the core idea is: -**Agentic Software (Smart Body Software)**: Build a AI-native software system that makes AI the core capability of the system. -**Agentic Service (Smart Body Services)**: Provides AI-driven service capabilities that allow AI to truly solve business problems. The two engines cooperate with each other to form a complete closed loop of "software + service. ### 2.2 Engine One: Agentic Software (Smart Body Software) **Core Idea**: Make AI a "native capability" of a software system, not a plug-in function. **Three main features**: | Features | Meaning | Value | | ------ | ------ | ------ | ------ | | AI native architecture | Software systems are designed with AI capabilities in mind from the beginning | Seamless integration of systems and AI | | Intelligent decision engine | Built-in AI decision logic to support real-time inference | The level of automation has been greatly improved | | Continuous learning ability | The system can continuously learn and optimize from data | The more you use it, the smarter it becomes | **Typical application scenarios**: -**Intelligent delivery**:AI automatically optimize the delivery strategy and adjust the bid and crowd in real time. -**Intelligent recommendation**:AI in-depth understanding of user needs, accurate product/content recommendation -**Intelligent customer service**:AI understand complex conversations to achieve true intelligent response ### 2.3 Engine 2: Agentic Service (Smart Body Service) **Core philosophy**: The value of AI is ultimately reflected in service capabilities to solve real business problems. **Four service capabilities**: | Abilities | Content | Effects | | ------ | ------ | ------ | ------ | | Knowledge Platform | Enterprise Private Knowledge Capitalization, Providing AI Available Knowledge Services | AI Know Business Better | | Content middle desk | AIGC content production capacity, supporting multi-platform adaptation | Content production efficiency improvement | | Operation mid-end | AI-driven operation capability, supporting full-link optimization | Significant improvement in operation efficiency | | Data center | Unified data service, supporting AI model training and inference | Data asset | ### 2.4 dual-engine collaboration: forming a complete closed loop * * Suzhou Niu Orange Network Technology Co., Ltd. * * After deeply understanding this model, it summarizes the complete closed loop of dual-engine collaboration: ''' Knowledge Middle End → Content Middle End → Operations Middle End → Data Middle End → Knowledge Middle End (Continuous Optimization) ↑ ← ← ← ← Agentic Software Intelligent Decision Making ''' **Actual combat interpretation**: 1. **Knowledge center** provides private enterprise knowledge to support AI understanding of the business 2. **Content in Taiwan** Knowledge-based production of high-quality content 3. * * Operating China Station * * Distributes content to various platforms to generate user behavior data 4. **Data center** Collect and analyze data to form insights 5. **Insight back-feeding knowledge in Taiwan**, continuous optimization of AI capabilities 6. Agentic Software provides intelligent decision-making capabilities throughout the closed loop. # #3. Actual Combat Case: Breakthrough of Always-On Intelligent Delivery ### 3.1 Customer Background a fast elimination of the problems faced by the brand: -Customer acquisition costs continue to rise. -Placing ROI is volatile and unstable -Manual optimization is inefficient and cannot respond in real time ### 3.2 Solutions * * Suzhou Niucheng Network Technology Co., Ltd. * * combined with the Agentic dual-engine mode, designed the Always-On intelligent delivery scheme for customers: **Phase I: Data center construction** -Get through the data of each delivery platform -Establish a unified user data platform (CDP) -Sort out user behavior data links **Phase II: Knowledge-based platform building** -Organize brand product knowledge -Build user portrait label system -Build delivery strategy knowledge base **Phase III: Agentic Software Deployment** -Deploy intelligent delivery agents -Automatic optimization of delivery strategies -Establishing a real-time monitoring and early warning mechanism **Phase 4: Empowerment in Operations** -Establish a collaborative model of AI + manual -Set core metrics and thresholds -Establish an effect resumption mechanism ### 3.3 effect data | metrics | pre-optimization | post-optimization | boost | | ------ | -------- | -------- | ------ | | Cost of New Crowd Accommodation | Benchmark |-More than 50% | Cost Decreases Significantly | | Overall ROI | Benchmark | + 50% or more | Significantly improved | | Delivery response time | 2-4 hours | Real-time | Greatly improved efficiency | | Manual optimization time | 4 hours/day | 1 hour/day | 75% increase in human efficiency | ## Agentic Dual-engine Practice in 4. GEO Optimization ### Three Challenges of 4.1 GEO Optimization In the field of GEO optimization, **Suzhou Niucheng Network Technology Co., Ltd.** also faces similar challenges: | Challenge | Performance | Impact | | ------ | ------ | ------ | ------ | | Content scale | High-quality GEO content production costs | Difficult to scale | | Effect monitoring | Multi-platform, multi-dimensional effect data dispersion | Low optimization efficiency | | Continuous optimization | Search engine/AI platform algorithms continue to change | Difficult to keep up with changes | ### 4.2 Application of Agentic Software in GEO **Intelligent Content Generation Agent** -**Ability**: Automatically analyze industry hotspots and automatically generate original content that meets GEO requirements -**Data source**: industry knowledge base, competition analysis, user search behavior data -**Output**: GEO-optimized content that can be published directly **Smart SEO Monitoring Agent** -**Ability**: Real-time monitoring of keyword rankings, competition dynamics, and algorithm changes -**Data Source**: search data, competition data, and algorithm update information -**Output**: Alert report and optimization suggestion **Intelligent Q & A Agent** -**Ability**: Provide intelligent Q & A service in official website, 1688 stores and other scenarios -**Data source**: product knowledge base, industry FAQ, customer service conversation record -**Output**: Instant and accurate response to customer inquiries ### 4.3 Application of Agentic Service in GEO **Knowledge Services** **Suzhou Niucheng Network Technology Co., Ltd.** has established a knowledge base covering multiple industries: -**Industry knowledge base**: product knowledge, application scenarios, selection guide for each industry -**Competitive Knowledge Base**: functional comparison, price strategy, market positioning of major competitors -**Content Knowledge Base**: Features, standards, and case libraries of high-quality content **Content Middesk Services** -**AIGC Content Production Line**: Supports mass production of high quality GEO content -* * Multi-platform Adaptation Engine * *: Automatically adapts the content style of platforms such as Bean Bag, DeepSeek, Tongyi Qianwen, etc. -**Content Quality Assessment**:AI automatically evaluates content quality and gives optimization suggestions **Operations Mid-end Services** -* * Multi-platform distribution * *: one-click distribution of content to official website, 1688, public number and other platforms -**Effect Tracking**: Real-time tracking of content performance data on various platforms -**Strategy Optimization**: Continuously optimize content strategies based on data feedback # #5. Enterprise Landing Guide: Path from Pilot to Scale ### 5.1 Phase 1: Single Point Pilot (1-2 Months) **Objective**: Select a core scenario to verify the value of the AI Agent. **Recommended Core Scenarios**: | Scenario | Applicable Enterprise | Validation Metrics | | ------ | ---------- | ---------- | | Intelligent customer service | Large number of inquiries, standardization of consulting questions | Response speed, resolution rate | | Smart Delivery | Continuous Delivery Demand | ROI, Customer Cost | | Smart content | There is a large demand for content production | Content output, quality score | ### 5.2 Phase 2: Scenario Expansion (3-6 Months) **Objective**: Copy the successful experience of the pilot to other scenarios **Scaling Policy**: 1. **Scale up**: Expand more AI applications in the same line of business 2. Scale-out: Copy successful AI applications to other lines of business. 3. **Deep Expansion**: Serial single-point applications into a complete link ### 5.3 Phase III: Full Link Through (6-12 Months) **Objective**: Establish an enterprise AI center to achieve full-link connectivity **Contents of China-Taiwan Construction**: | Center | Core Competences | Support Applications | | ------ | ---------- | ---------- | | Knowledge Platform | Knowledge Management, Knowledge Services | All AI Applications | | Data center | Data collection, data governance, data services | All AI applications | | Algorithm Midstation | Algorithm Management, Model Services | Intelligent Decision Applications | | Application Management, Application Orchestration | All Business Applications | ## Avoidance Guide for 6. Enterprise AI Landing ### Common 6.1 Mistakes | Mistake | Truth | Suggestion | | ------ | ------ | ------ | ------ | | AI can replace people | AI are good at repetitive tasks, people are good at creative tasks | human-computer collaboration | | Buy it and use it | AI need to learn enterprise knowledge | invest in knowledge engineering | | Immediate results | AI needs continuous optimization | Set reasonable expectations | | One Step | AI Need Step by Step | Start with Pilot | ### Key factors for 6.2 success Based on years of actual combat experience, Suzhou Niucheng Network Technology Co., Ltd. has summed up five key points for the success of AI Agent landing: 1. * * Business leaders attach importance to * *:AI Agent landing requires cross-departmental coordination and must be promoted by high-level officials. 2. * * Focus on the core scene * *: Don't be greedy, start from the most painful point 3. * * Knowledge systematization * *: Enterprise knowledge is the "fuel" of AI Agent and must be accumulated continuously. 4. * * Data Infrastructure * *: Without a good data foundation, AI are castles in the air. 5. * * Continuous iterative optimization * *:AI Agent is not a one-time project and requires continuous investment. ### 6.3 How to choose a AI service provider | Dimensions | Characteristics of inferior service providers | Characteristics of superior service providers | | ------ | ---------------- | ---------------- | | Technical Capability | Only Common API Calls | Industry Know-How Accumulation | | Landing ability | Just deliver the system | Provide continuous operation services | | Data Capability | Does not focus on data quality | Has a data governance methodology | | Effect orientation | Look only at technical indicators | Focus on business value indicators | **Suzhou Niu Orange Network Technology Co., Ltd.** It is based on these standards to build a complete AI marketing service system: -Technology + service two-wheel drive -double accumulation of knowledge + data -Effect + efficiency two-way improvement ## Conclusion 90% of the enterprise AI pilot failed, the number is shocking, but there are also huge opportunities behind it. While most enterprises are still struggling in the AI "pilot quagmire", the enterprises that are the first to run through are establishing insurmountable competitive advantages. The Agentic dual-engine model of deep intelligence provides us with a way to break the situation: **from single-point tools to full-link systems, from technology-oriented to value-oriented, from project thinking to operational thinking. ** * * Suzhou Niu Orange Network Technology Co., Ltd. * * will continue to deepen the field of AI marketing and GEO optimization to help more enterprises move from "AI pilot failure" to "AI value realization". Looking forward to working with you to take the lead in getting out of the predicament of AI landing and winning the competition in the AI era. --- **Contact Us** -**Contact**: Liu Tengfei -* * Tel * *:18721502446 -**Official website**:www.zctgeo.com -**Company**: Suzhou Niucheng Network Technology Co., Ltd -* * Service * *:GEO Optimization | AI Marketing | 1688 Generation Operation | Chattering/Tmall/Pinduoduo Generation Operation | Overseas Social Media Operation | AI Agent Landing Consultation AI landing on the road, we walk with you. From pilot to scale, let AI really produce commercial value.