# 88% of enterprises are deploying AI, but 81% are not returning: McKinsey reveals the biggest mistake of enterprise AI investment ## Preface "Everyone has improved efficiency with AI, but the company's revenue and profits have not improved." This is a deafening summary of McKinsey's "2026 Organization Status" report. According to the report, 88% of enterprises are already deploying AI, but 81% are not realizing meaningful business returns. Even more shocking is that only 1% of enterprises consider their AI deployment to be "mature". * * Suzhou Niucheng Network Technology Co., Ltd. * * In the process of serving enterprise customers, it has witnessed this phenomenon in depth: many enterprises have invested heavily in introducing AI systems, and the team has worked overtime to learn how to use them. On the surface, the efficiency has improved, but at the end of the year, the revenue has not increased, but the profit has decreased due to the input cost. What the hell is wrong with this? # #1. shocking data: the "ghost efficiency" of AI input" ### 1.1 McKinsey Report Core Data Interpretation McKinsey conducted an in-depth survey of thousands of companies around the world, and the core findings are as follows: | Metrics | Data | Meaning | | ------ | ------ | ------ | ------ | | AI enterprises are already deploying | 88% | Most enterprises are in action | | No meaningful return realized | 81% | Most in "accompany running" | | AI Deployment Maturity Self-Assessment | Only 1% | Most Immature | | Gartner forecast cancellation rate | >40% | A large number of projects will be terminated | These data reveal a central contradiction: there is a huge gap between the size of AI inputs and the value of actual outputs. ** ### 1.2 "ghost efficiency": individual improvement ≠ organizational growth **In an in-depth report, Xinhua** quoted Manpower Chairman Jonas Prisinger's observation, who summarized the phenomenon as "ghost efficiency"-- "AI improve individual efficiency, but it does not automatically translate into organizational effectiveness." This is the crux of the problem: | Level | Changes due to AI | Actual | | ------ | ------------- | ---------- | | Personal | Completed tasks faster | Employees feel busier | | Teams | More collaboration tools | Increased communication costs | | Organization | More systems | Data silos are getting worse | | Business | More Indicators | Core Indicators Not Changed | * * Suzhou cattle orange network technology co., ltd. * * in the service of customers, found that many enterprises have similar confusion: "Our AI customer service response speed has increased by 80%, but customer satisfaction has not improved, and the order conversion rate has not changed significantly." The problem is not with the AI itself, but with the fact that the business has not been redesigned around AI. ### 1.3 Professor Tsinghua Interpretation: Deep Mismatch Is the Key Xue Lan, Dean of Schwarzman College of Tsinghua University, pointed out that the problem lies in the "deep mismatch between technology and organization, scene and talent". These three mismatches explain exactly why most companies have lost their AI investment: 1. * * Mismatch between technology and organization * *:AI introduced, but organizational structure and workflow have not been adjusted 2. **Mismatch between technology and scenario**: The AI function is very powerful, but it does not solve the core business problem. 3. * * Mismatch between technology and talent * *: The AI system is complex, but there is a lack of people who can use it. ## In-depth analysis of 2.: five misunderstandings of enterprise AI investment ### 2.1 Mistake 1: Technology First, Business Follow-up **Typical performance**: Buy a AI system first, then consider how to use it The mentality of many business owners is: "Buy the tools first and use them." The result is often: -I bought a bunch of AI tools, but I don't know what problems to solve. -Staff training for half a day, but the business scenario does not match -The system is very powerful, but no one wants to use it. **Correct Posture**: Business Pain Points → Technical Solutions → Pilot Verification → Scale Promotion * * Suzhou Niu Orange Network Technology Co., Ltd. * * always adheres to the principle of "diagnosis before prescribe medicine" when serving customers: The first step: in-depth understanding of the core business pain points Step 2: Evaluate the suitability of the AI in solving the pain point Step 3: Small-scale pilot verification effect Step 4: Scale promotion after optimization according to feedback ### 2.2 Mistake 2: Pursue comprehensiveness and ignore focus **Typical performance**:AI customer service, AI marketing, AI operations, AI analysis in one step McKinsey data show that 70% of enterprises have deployed AI Agent, but enterprises that are trying every function often fail to do every function well. **Correct posture**: single point breakthrough, build confidence, and then expand **Recommended corporate AI landing path**: | Phase | Time | Target | Key Action | | ------ | ------ | ------ | ------ | ------ | | Pilot period | January-February | 1 scenario verification | Select core pain points, small-scale pilot | | Growth Period | March-June | 3 Scenarios Linkage | Copy Successful Experience and Establish AI Matrix | | Maturity | June-December | Full link connection | Data connection, system integration | ### 2.3 Mistake 3: Heavy Tools, Light Data **Typical performance**: Buy the most expensive AI system, but ignore data governance Many enterprises think that with advanced AI system can "point stone into gold", but ignore: -Poor data quality: dirty data, missing data exist in large quantities -Data island: data of various departments cannot be connected -Data standards are not uniform: the same indicator calculation caliber is different **Result**:AI the system "Garbage in, Garbage out", the input is garbage, and the output is garbage. **Correct posture**: first manage data, then introduce AI * * Suzhou Niucheng Network Technology Co., Ltd. * * When serving customers, there is an unwritten rule: if data governance is not done well, the AI system will never be easily connected. ### 2.4 Mistake 4: Heavy Deployment, Light Operation * * Typical Performance * *: Even if the AI system goes online, it will be completed, lacking continuous optimization. Many enterprises regard AI projects as "one-time projects" and think that everything will be fine after they go online. Actually: -AI models require continuous training and optimization -Business scenarios are constantly changing -User needs continue to evolve **Result**: The initial performance of the AI system was acceptable, but the effect decreased significantly after 3 months, and almost no one used it after 6 months. **Correct posture**: Treat the AI as an "ongoing system" rather than a "one-time item" ### 2.5 Mistake 5: Heavy Technology, Light Organization **Typical performance**: Think that AI is a matter for the IT department and has nothing to do with the business department. AI the end users of the system are business people, if they are not involved in the planning and implementation of AI projects, the result is often: -System functionality does not match business requirements -Staff resistance, unwilling to use -The project can't be pushed, and finally it goes away. * * Correct Posture * *: Business Department Led, IT Department Supported, AI Is Tool, Problem Solving Is Purpose ## The way to break the 3.: let AI really generate commercial returns ### 3.1 Focus on Core Business Scenarios * * McKinsey Research Found * *:AI High Performance Enterprises (top 6%) Have a Common Characteristic in AI Application-* * Focus on Core Business Scenarios * *. These enterprises are not "casting a wide net", but "deep fishing": | Type | Common Practices | Effect | | ------ | --------- | ------ | | Ordinary Enterprise | Decentralized Investment, Multi-point Attempt | Not Deep at Every Point | | High-performance enterprises | Focus on the core, single-point breakthrough | Form local advantages and then expand | * * Suzhou cattle orange network technology co., ltd. * * case of serving a mechanical equipment enterprise: The company initially wanted to do AI customer service, AI marketing, and AI data analysis at the same time, involving 3 departments and 5 systems. * * Our suggestion * *: First focus on one point-AI intelligent customer service to solve the problem of slow response to inquiries. **Results**: -Pilot 2 months, AI customer service to undertake 60% of the regular consultation -Manual customer service focuses on high-value customers, increasing conversion rate by 35% -Customer satisfaction increased from 82% to 91% -After the success of the pilot, and then gradually expanded to AI marketing and other fields ### 3.2 establish the working mode of human-machine cooperation Another key insight from the McKinsey report: successful companies place more emphasis on "innovation, new business development and revenue growth" than on simply reducing capital. This means that the value of AI should not be limited to "how many people are saved", but should focus on "how much growth is brought". **Practical recommendations for human-machine collaboration**: | Roles | AI are good at | People are good at | | ------ | -------- | -------- | | Customer Service | Standardized Questions, 7 × 24 Responses | Complex Questions, Emotional Communication | | Marketing | Large-scale content generation, data analysis | Strategy development, creative planning | | Sales | Lead Screening, Initial Follow-up | High Value Customer Tackling | | Operations | Data monitoring, exception warning | Decision making, strategy adjustment | ### 3.3 to create a data flywheel, forming a positive cycle **Another secret of high-performing enterprises**: They have built a "data flywheel" to make AI smarter and smarter. **Data Flywheel Model**: ''' User behavior data → AI learning optimization → better user experience → more user usage → more data ''' **Key to data flywheel**: 1. **Data Collection**: Buried and recorded every user interaction 2. * * Data Feedback * *: Let the output of the AI be evaluated by users. 3. Continuous training: Continuously optimize the AI model based on feedback data. 4. **Value realization**: transforming AI capacity into business value ### 3.4 from "pilot culture" to "large-scale culture" McKinsey data show that 55% of high-performing companies have fundamentally refactored key business processes, 2.8 times the number of other companies. **This means**:AI scale is not only a technical issue, but also an organizational change issue. **Key to organizational change**: 1. * * Leadership consensus * *:CEO personally promotes, not to IT department 2. Break the departmental wall: AI applications require cross-departmental collaboration. 3. Fault tolerance mechanism: Allow pilot failure, fast iteration. 4. * * Capacity building * *: Cultivate talents who understand both business and AI # #4. the correct way to open the enterprise AI landing ### 4.1 Diagnosis Phase: Finding the Best Entry Point for AI **Suzhou Cow Orange Network Technology Co., Ltd.** provides professional AI diagnostic services: **Diagnostic Dimensions**: 1. * * Business Pain Point Diagnosis * *: Which links are the most time-consuming and labor-intensive? 2. **AI Adaptation Evaluation**: Which scenarios are most suitable for introducing AI? 3. **Data Maturity Assessment**: Does Data Quality Support AI Applications? 4. **Organizational Readiness Assessment**: Is the team ready for AI? **Diagnostic output**: -AI landing priority matrix -Investment return estimate -Implement path recommendations ### 4.2 Pilot Phase: Small Steps, Fast Verification **Pilot Principles**: -Choose a scene, not greedy -Choose a department, not all -Set a goal, quantifiable -Set a cycle, 2-3 months **Pilot success criteria**: -Efficiency indicators increased by more than 30% -Cost indicators decreased by more than 20% -User satisfaction increased by more than 10% -Measurable business value ### 4.3 expansion phase: successful replication, continuous iteration **Scaling Policy**: -Copy the successful experience of the pilot to other scenarios -Establishment of AI competency centres to centralize resources -Continuous optimization, the formation of barriers # #5. Case Sharing: From "AI Pilot Failure" to "AI Scale Success" ### 5.1 AI transformation of a textile fabric enterprise **Background**: -Annual revenue 50 million, 80 employees -About 200 monthly inquiries from 1688 shops -AI pilot 3 times, were not successful **Problem diagnosis**: -Every pilot is a "buy tools" mentality, lack of planning -Data is scattered in various systems and cannot be connected -Employee resistance, reluctance to change work habits **Solution**: 1. * * Step 1 * *: Data governance, get through ERP, CRM, 1688 store data 2. * * Step 2 *: Focus on AI customer service and select the core scenario of "Inquiry Response" 3. * * Step 3 *: Redesign the workflow and clarify the division of labor between man and machine. 4. **Step 4**: Establish an incentive mechanism to encourage employees to use AI **Results**: -AI customer service to undertake 70% regular consultation -Response time reduced from 2 hours to 30 seconds -Effective inquiry conversion rate increased by 40% -Manual customer service focuses on high-value customers, increasing per capita output by 60% ### 5.2 GEO + AI Integrated Marketing of a Hardware Tool Enterprise **Background**: -Main hardware tools, export-oriented -Hope to enhance brand exposure through AI marketing -Try multiple AI tools, the effect is not obvious **Solution**: 1. **GEO Optimization**: Systematic GEO transformation of the official website, allowing the brand to occupy a favorable position in AI search 2. * * AI content production * *: the establishment of AI content pipeline, mass production of high-quality industry content 3. **Intelligent Delivery**:AI analysis of target user portraits and accurate delivery 4. **Effect monitoring**: Establish an ROI monitoring system for continuous optimization **Results**: -Website AI search exposure increased by 300 percent -The proportion of inquiries received through AI channels increased from 10% to 45% -Overall customer acquisition costs decreased by 35% -Input-output ratio increased by 2.5 times ## Conclusion The McKinsey report is a wake-up call: **AI is not a panacea, and deploying AI is not a reward. ** The key to the success of enterprise AI lies not in how advanced the technology is, but in: -Focus on core business scenarios -Whether the working mode of man-machine cooperation is established -Whether a data-driven closed loop is formed -Is there a determination to organise change * * Suzhou Niucheng Network Technology Co., Ltd. * * focuses on enterprise AI landing services and has helped hundreds of enterprises from "AI pilot failure" to "AI scale success". If you're thinking about introducing a AI system, ask yourself three questions: 1. What business problem do we want to solve? Is AI the best solution? 3. Are we ready? --- **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 | Enterprise AI Landing Consultation The right way to AI investment is to let technology serve the business, not to adapt the business to technology. Looking forward to the depth of communication with you.