🤖 The Rise of AI Agents
According to Gartner's latest report, in the next three years, AI Agents will become one of the key driving forces for digitalization in enterprises. Currently, the application of AI Agents in enterprises is less than 1%, but with continuous technological evolution, it is expected that by 2028, 15% of daily business processes will be automated by AI Agents. For example, some leading companies have already started to apply AI Agents in customer service, data processing, and inventory management scenarios, preliminarily validating their potential in improving efficiency and reducing costs. According to McKinsey's research, by 2028, the application of AI in enterprises is expected to bring economic benefits amounting to trillions of dollars, further accelerating its adoption. This marks the transition of AI technology from foundational models and infrastructure to more widespread practical applications.
This transformation signifies that the digital transformation of enterprises is about to enter a new stage. Traditional business processes often rely on manual execution; however, the emergence of AI Agents will be able to automate many repetitive and standardized tasks, thereby enhancing the efficiency of business processing, reducing human errors, and improving overall operational levels. In the future, the digital capabilities of enterprises will increasingly rely on the deployment and application breadth of AI Agents, which will be a key driving force for industry transformation.
🏭 The Potential of AI Agents in Manufacturing and Our Practice
In the manufacturing industry, the potential of AI Agents is evident. From R&D, production, supply chain, sales to service, each link can benefit from the application of AI.
🔬 R&D Phase: Knowledge-based AI Agents can organize and analyze historical design drawings and engineering change records, providing data-driven decision-making recommendations for engineers. For instance, during new product development, an AI Agent can quickly extract relevant historical experiences to help engineers avoid repetitive mistakes and shorten R&D cycles.
🙌 Our Practice: We trained our internal R&D code architecture for Agent Cody so that he can understand our code. Product managers then communicate the product documentation described in natural language to the Agent via conversations, and then a miraculous thing happens — the Agent automatically generates the production environment code we want.
ChatGPT's official Canvas
Our practice with Canvas
⚙️ Production Phase: AI Agents have powerful image recognition and detection capabilities, capable of replacing some manual inspection work, improving efficiency and quality stability. By monitoring product quality on the production line in real-time, AI can automatically identify defects and classify them, which not only reduces the workload of manual inspections but also significantly enhances detection accuracy and consistency.
📦 Supply Chain Phase: In supply chain management, AI Agents can help enterprises optimize procurement plans, forecast raw material needs, and effectively communicate with suppliers to ensure production continuity. By monitoring inventory and supply chain data in real-time, AI Agents can issue timely alerts to prevent shortages or surpluses of raw materials, reducing the risk of supply chain disruptions.
🛒 Sales and After-Sales Phase: As “digital employees,” AI Agents can handle common customer inquiries, providing answers quickly and accurately, thereby enhancing the customer experience. By deeply learning customer behaviors and needs, AI Agents can offer personalized recommendations and services, further increasing customer satisfaction and brand loyalty.
🙌 Our Practice: This is our Agent Sally in the sales phase, which can help sales determine the authenticity of customer needs. When sales visit the market, it can assist in creating work plans and content and create relevant information in the background for the next steps. Additionally, during the sales management process, many stories are often shared among sales staff. It is important not to rely solely on fragmented information about what was discussed during customer interactions and whether key needs were clear. Therefore, we integrated Feishu Miaoj记录 and recorded the entire hour-long meeting, performing AI analysis on Feishu Miaoj. Based on the experiences of our TOP SALES, the Agent provides suggestions for pain points or decision-maker information not described in the meeting minutes and inputs this information into the CRM.
Simplifying Complexity: One-click CRM Data Entry
Driving Power: Generating Performance Rankings, Quickly Estimating Commissions
Baidu CEO Robin Li once mentioned in a public speech that in the future, enterprises might not need traditional official websites, but rather interact with customers through the enterprise's AI Agent. This vision depicts a future scenario: AI Agents will master all knowledge within the enterprise, providing service to customers around the clock, even offering personalized solutions. This type of emotionless, 24/7 online digital employee will significantly reduce operating costs for enterprises and improve work efficiency.
🔍 Current Challenges in Manufacturing and Digital Transformation Pathways
Although the future of AI is promising, the Chinese manufacturing industry still faces many practical challenges before large-scale application of AI. For instance, insufficient transparency in the production process of small-batch, varied products, difficulties in quality traceability, and fluctuations in delivery times and quality upstream in the supply chain. These issues need to be gradually addressed through digital means (such as MES systems).
Challenges Facing the Manufacturing Industry
Insufficient transparency in the production process may lead to enterprises being unable to accurately grasp production progress, affecting the formulation and execution of delivery plans. Additionally, the production mode of small batches with multiple varieties requires enterprises to have extremely high production flexibility, and traditional management methods generally struggle to effectively meet these demands. Therefore, establishing a transparent, efficient production management system is the first step toward a digital factory.
Full process traceability and comprehensive quality management is an advanced process in building a digital factory. Ensuring that every production node has clear records facilitates tracing and management. Next, by comprehensively collecting and analyzing data, we establish a closed-loop management system from production to quality, achieving refined control over each link. From batch to SN traceability, comprehensive quality information must close the loop, which has become a mandatory requirement for current downstream clients towards upstream suppliers.
Integrated supply chains and collaboration between upstream and downstream is a goal after factories have developed to a certain scale. Besides digitizing their own factories, they also hope to integrate specific production lines of upstream suppliers (such as subcontracted workshops) into their platform, constructing an integrated supply chain platform. Amid globalization and refined operational trends, companies are more clearly defining their roles, making outsourcing/sub-contracting more economically viable.
🚀 NewCore Cloud's Practice and Exploration
In the practice of New Core Cloud, we discovered that only by fully digitizing fundamental aspects such as the production process, quality management, and supply chain collaboration can we lay a solid foundation for the application of AI Agents. For instance, to address the transparency issue in the production process, we implemented a series of data collection devices and tag tracking technologies, achieving detailed management of every production node. This not only enables enterprises to grasp production progress in real-time but also accumulates valuable data for future AI applications.
In terms of quality management, we introduced AI Agents to monitor and analyze product quality data in real-time, helping enterprises identify potential quality issues early, reducing rework and scrap rates. AI Agents can also conduct deep learning on historical data to identify key factors affecting quality, aiding enterprises in continuously improving their quality management systems.
We are also actively exploring how to empower manufacturing with AI. For example, by replacing manual repetitive work with AI visual inspection, increasing efficiency and precision; and by integrating with collaboration tools like Feishu to achieve real-time information sharing and collaboration between internal factory operations and upstream and downstream supply chains. With these applications, we believe that AI will gradually embed into every aspect of manufacturing, becoming an indispensable "digital assistant" for enterprises.
Additionally, we work closely with multiple partners to jointly create complete intelligent manufacturing solutions. For example, we collaborate with hardware suppliers to integrate AI Agents into production equipment, enabling devices to have self-learning and optimization capabilities, thereby further enhancing production efficiency. Simultaneously, we partner with ERP system suppliers to connect various information systems of enterprises, achieving comprehensive data sharing and business collaboration, facilitating synergy of data and business across enterprises.
🤔 AI is Not Omnipotent, But It is the Best Assistant
Despite the exciting prospects of AI, we must recognize that it is not omnipotent. It cannot fully replace human creativity and complex decision-making abilities but can become our best assistant by improving efficiency and reducing human errors. Therefore, the digital transformation of the manufacturing industry should be predicated on refining basic management, gradually transitioning towards AI empowerment. Only in this way can we usher in the era of truly intelligent factories.
The application of AI Agents can significantly alleviate the burden on employees in repetitive, standardized jobs, allowing them to focus more on innovation and complex tasks. The introduction of AI is not just an enhancement of efficiency; it also represents a transformation of organizational structures and operational models. In this process of transformation, human-AI collaboration will become the new norm, and enterprises will need to continuously adjust and optimize to achieve the best combination of human resources and AI.
🌟 Looking Forward to the Future
NewCore Cloud is committed to helping manufacturing enterprises solve current pain points through digital means while actively exploring AI application scenarios to achieve truly intelligent factories. In the future, we look forward to collaborating with more partners to jointly promote the intelligent and digital development of China's manufacturing industry. We believe that the combination of AI and manufacturing will create unprecedented opportunities and value.
The intelligent factories of the future will not only be places for producing products, but smart systems. In this system, the physical and virtual worlds will fully merge, with every device, product, and production line becoming part of the data, interacting with the entire system in real-time. Through this highly collaborative intelligent system, enterprises will be able to respond more agilely to market changes and achieve the goals of rapid production and on-demand customization.
We also firmly believe that the future of intelligence does not only belong to large enterprises. By continuously lowering the barriers to digitalization and intelligence, we hope that more small and medium-sized enterprises can also enjoy the benefits brought by this transformation. We will continue to provide flexible, user-friendly solutions to help various manufacturing enterprises achieve digital transformation and collectively embrace the arrival of the AI era.
🤖 The Rise of AI Agents
According to Gartner's latest report, in the next three years, AI Agents will become one of the key driving forces for digitalization in enterprises. Currently, the application of AI Agents in enterprises is less than 1%, but with continuous technological evolution, it is expected that by 2028, 15% of daily business processes will be automated by AI Agents. For example, some leading companies have already started to apply AI Agents in customer service, data processing, and inventory management scenarios, preliminarily validating their potential in improving efficiency and reducing costs. According to McKinsey's research, by 2028, the application of AI in enterprises is expected to bring economic benefits amounting to trillions of dollars, further accelerating its adoption. This marks the transition of AI technology from foundational models and infrastructure to more widespread practical applications.
This transformation signifies that the digital transformation of enterprises is about to enter a new stage. Traditional business processes often rely on manual execution; however, the emergence of AI Agents will be able to automate many repetitive and standardized tasks, thereby enhancing the efficiency of business processing, reducing human errors, and improving overall operational levels. In the future, the digital capabilities of enterprises will increasingly rely on the deployment and application breadth of AI Agents, which will be a key driving force for industry transformation.
🏭 The Potential of AI Agents in Manufacturing and Our Practice
In the manufacturing industry, the potential of AI Agents is evident. From R&D, production, supply chain, sales to service, each link can benefit from the application of AI.
🔬 R&D Phase: Knowledge-based AI Agents can organize and analyze historical design drawings and engineering change records, providing data-driven decision-making recommendations for engineers. For instance, during new product development, an AI Agent can quickly extract relevant historical experiences to help engineers avoid repetitive mistakes and shorten R&D cycles.
🙌 Our Practice: We trained our internal R&D code architecture for Agent Cody so that he can understand our code. Product managers then communicate the product documentation described in natural language to the Agent via conversations, and then a miraculous thing happens — the Agent automatically generates the production environment code we want.
ChatGPT's official Canvas
Our practice with Canvas
⚙️ Production Phase: AI Agents have powerful image recognition and detection capabilities, capable of replacing some manual inspection work, improving efficiency and quality stability. By monitoring product quality on the production line in real-time, AI can automatically identify defects and classify them, which not only reduces the workload of manual inspections but also significantly enhances detection accuracy and consistency.
📦 Supply Chain Phase: In supply chain management, AI Agents can help enterprises optimize procurement plans, forecast raw material needs, and effectively communicate with suppliers to ensure production continuity. By monitoring inventory and supply chain data in real-time, AI Agents can issue timely alerts to prevent shortages or surpluses of raw materials, reducing the risk of supply chain disruptions.
🛒 Sales and After-Sales Phase: As “digital employees,” AI Agents can handle common customer inquiries, providing answers quickly and accurately, thereby enhancing the customer experience. By deeply learning customer behaviors and needs, AI Agents can offer personalized recommendations and services, further increasing customer satisfaction and brand loyalty.
🙌 Our Practice: This is our Agent Sally in the sales phase, which can help sales determine the authenticity of customer needs. When sales visit the market, it can assist in creating work plans and content and create relevant information in the background for the next steps. Additionally, during the sales management process, many stories are often shared among sales staff. It is important not to rely solely on fragmented information about what was discussed during customer interactions and whether key needs were clear. Therefore, we integrated Feishu Miaoj记录 and recorded the entire hour-long meeting, performing AI analysis on Feishu Miaoj. Based on the experiences of our TOP SALES, the Agent provides suggestions for pain points or decision-maker information not described in the meeting minutes and inputs this information into the CRM.
Simplifying Complexity: One-click CRM Data Entry
Driving Power: Generating Performance Rankings, Quickly Estimating Commissions
Baidu CEO Robin Li once mentioned in a public speech that in the future, enterprises might not need traditional official websites, but rather interact with customers through the enterprise's AI Agent. This vision depicts a future scenario: AI Agents will master all knowledge within the enterprise, providing service to customers around the clock, even offering personalized solutions. This type of emotionless, 24/7 online digital employee will significantly reduce operating costs for enterprises and improve work efficiency.
🔍 Current Challenges in Manufacturing and Digital Transformation Pathways
Although the future of AI is promising, the Chinese manufacturing industry still faces many practical challenges before large-scale application of AI. For instance, insufficient transparency in the production process of small-batch, varied products, difficulties in quality traceability, and fluctuations in delivery times and quality upstream in the supply chain. These issues need to be gradually addressed through digital means (such as MES systems).
Challenges Facing the Manufacturing Industry
Insufficient transparency in the production process may lead to enterprises being unable to accurately grasp production progress, affecting the formulation and execution of delivery plans. Additionally, the production mode of small batches with multiple varieties requires enterprises to have extremely high production flexibility, and traditional management methods generally struggle to effectively meet these demands. Therefore, establishing a transparent, efficient production management system is the first step toward a digital factory.
Full process traceability and comprehensive quality management is an advanced process in building a digital factory. Ensuring that every production node has clear records facilitates tracing and management. Next, by comprehensively collecting and analyzing data, we establish a closed-loop management system from production to quality, achieving refined control over each link. From batch to SN traceability, comprehensive quality information must close the loop, which has become a mandatory requirement for current downstream clients towards upstream suppliers.
Integrated supply chains and collaboration between upstream and downstream is a goal after factories have developed to a certain scale. Besides digitizing their own factories, they also hope to integrate specific production lines of upstream suppliers (such as subcontracted workshops) into their platform, constructing an integrated supply chain platform. Amid globalization and refined operational trends, companies are more clearly defining their roles, making outsourcing/sub-contracting more economically viable.
🚀 NewCore Cloud's Practice and Exploration
In the practice of New Core Cloud, we discovered that only by fully digitizing fundamental aspects such as the production process, quality management, and supply chain collaboration can we lay a solid foundation for the application of AI Agents. For instance, to address the transparency issue in the production process, we implemented a series of data collection devices and tag tracking technologies, achieving detailed management of every production node. This not only enables enterprises to grasp production progress in real-time but also accumulates valuable data for future AI applications.
In terms of quality management, we introduced AI Agents to monitor and analyze product quality data in real-time, helping enterprises identify potential quality issues early, reducing rework and scrap rates. AI Agents can also conduct deep learning on historical data to identify key factors affecting quality, aiding enterprises in continuously improving their quality management systems.
We are also actively exploring how to empower manufacturing with AI. For example, by replacing manual repetitive work with AI visual inspection, increasing efficiency and precision; and by integrating with collaboration tools like Feishu to achieve real-time information sharing and collaboration between internal factory operations and upstream and downstream supply chains. With these applications, we believe that AI will gradually embed into every aspect of manufacturing, becoming an indispensable "digital assistant" for enterprises.
Additionally, we work closely with multiple partners to jointly create complete intelligent manufacturing solutions. For example, we collaborate with hardware suppliers to integrate AI Agents into production equipment, enabling devices to have self-learning and optimization capabilities, thereby further enhancing production efficiency. Simultaneously, we partner with ERP system suppliers to connect various information systems of enterprises, achieving comprehensive data sharing and business collaboration, facilitating synergy of data and business across enterprises.
🤔 AI is Not Omnipotent, But It is the Best Assistant
Despite the exciting prospects of AI, we must recognize that it is not omnipotent. It cannot fully replace human creativity and complex decision-making abilities but can become our best assistant by improving efficiency and reducing human errors. Therefore, the digital transformation of the manufacturing industry should be predicated on refining basic management, gradually transitioning towards AI empowerment. Only in this way can we usher in the era of truly intelligent factories.
The application of AI Agents can significantly alleviate the burden on employees in repetitive, standardized jobs, allowing them to focus more on innovation and complex tasks. The introduction of AI is not just an enhancement of efficiency; it also represents a transformation of organizational structures and operational models. In this process of transformation, human-AI collaboration will become the new norm, and enterprises will need to continuously adjust and optimize to achieve the best combination of human resources and AI.
🌟 Looking Forward to the Future
NewCore Cloud is committed to helping manufacturing enterprises solve current pain points through digital means while actively exploring AI application scenarios to achieve truly intelligent factories. In the future, we look forward to collaborating with more partners to jointly promote the intelligent and digital development of China's manufacturing industry. We believe that the combination of AI and manufacturing will create unprecedented opportunities and value.
The intelligent factories of the future will not only be places for producing products, but smart systems. In this system, the physical and virtual worlds will fully merge, with every device, product, and production line becoming part of the data, interacting with the entire system in real-time. Through this highly collaborative intelligent system, enterprises will be able to respond more agilely to market changes and achieve the goals of rapid production and on-demand customization.
We also firmly believe that the future of intelligence does not only belong to large enterprises. By continuously lowering the barriers to digitalization and intelligence, we hope that more small and medium-sized enterprises can also enjoy the benefits brought by this transformation. We will continue to provide flexible, user-friendly solutions to help various manufacturing enterprises achieve digital transformation and collectively embrace the arrival of the AI era.
🤖 The Rise of AI Agents
According to Gartner's latest report, in the next three years, AI Agents will become one of the key driving forces for digitalization in enterprises. Currently, the application of AI Agents in enterprises is less than 1%, but with continuous technological evolution, it is expected that by 2028, 15% of daily business processes will be automated by AI Agents. For example, some leading companies have already started to apply AI Agents in customer service, data processing, and inventory management scenarios, preliminarily validating their potential in improving efficiency and reducing costs. According to McKinsey's research, by 2028, the application of AI in enterprises is expected to bring economic benefits amounting to trillions of dollars, further accelerating its adoption. This marks the transition of AI technology from foundational models and infrastructure to more widespread practical applications.
This transformation signifies that the digital transformation of enterprises is about to enter a new stage. Traditional business processes often rely on manual execution; however, the emergence of AI Agents will be able to automate many repetitive and standardized tasks, thereby enhancing the efficiency of business processing, reducing human errors, and improving overall operational levels. In the future, the digital capabilities of enterprises will increasingly rely on the deployment and application breadth of AI Agents, which will be a key driving force for industry transformation.
🏭 The Potential of AI Agents in Manufacturing and Our Practice
In the manufacturing industry, the potential of AI Agents is evident. From R&D, production, supply chain, sales to service, each link can benefit from the application of AI.
🔬 R&D Phase: Knowledge-based AI Agents can organize and analyze historical design drawings and engineering change records, providing data-driven decision-making recommendations for engineers. For instance, during new product development, an AI Agent can quickly extract relevant historical experiences to help engineers avoid repetitive mistakes and shorten R&D cycles.
🙌 Our Practice: We trained our internal R&D code architecture for Agent Cody so that he can understand our code. Product managers then communicate the product documentation described in natural language to the Agent via conversations, and then a miraculous thing happens — the Agent automatically generates the production environment code we want.
ChatGPT's official Canvas
Our practice with Canvas
⚙️ Production Phase: AI Agents have powerful image recognition and detection capabilities, capable of replacing some manual inspection work, improving efficiency and quality stability. By monitoring product quality on the production line in real-time, AI can automatically identify defects and classify them, which not only reduces the workload of manual inspections but also significantly enhances detection accuracy and consistency.
📦 Supply Chain Phase: In supply chain management, AI Agents can help enterprises optimize procurement plans, forecast raw material needs, and effectively communicate with suppliers to ensure production continuity. By monitoring inventory and supply chain data in real-time, AI Agents can issue timely alerts to prevent shortages or surpluses of raw materials, reducing the risk of supply chain disruptions.
🛒 Sales and After-Sales Phase: As “digital employees,” AI Agents can handle common customer inquiries, providing answers quickly and accurately, thereby enhancing the customer experience. By deeply learning customer behaviors and needs, AI Agents can offer personalized recommendations and services, further increasing customer satisfaction and brand loyalty.
🙌 Our Practice: This is our Agent Sally in the sales phase, which can help sales determine the authenticity of customer needs. When sales visit the market, it can assist in creating work plans and content and create relevant information in the background for the next steps. Additionally, during the sales management process, many stories are often shared among sales staff. It is important not to rely solely on fragmented information about what was discussed during customer interactions and whether key needs were clear. Therefore, we integrated Feishu Miaoj记录 and recorded the entire hour-long meeting, performing AI analysis on Feishu Miaoj. Based on the experiences of our TOP SALES, the Agent provides suggestions for pain points or decision-maker information not described in the meeting minutes and inputs this information into the CRM.
Simplifying Complexity: One-click CRM Data Entry
Driving Power: Generating Performance Rankings, Quickly Estimating Commissions
Baidu CEO Robin Li once mentioned in a public speech that in the future, enterprises might not need traditional official websites, but rather interact with customers through the enterprise's AI Agent. This vision depicts a future scenario: AI Agents will master all knowledge within the enterprise, providing service to customers around the clock, even offering personalized solutions. This type of emotionless, 24/7 online digital employee will significantly reduce operating costs for enterprises and improve work efficiency.
🔍 Current Challenges in Manufacturing and Digital Transformation Pathways
Although the future of AI is promising, the Chinese manufacturing industry still faces many practical challenges before large-scale application of AI. For instance, insufficient transparency in the production process of small-batch, varied products, difficulties in quality traceability, and fluctuations in delivery times and quality upstream in the supply chain. These issues need to be gradually addressed through digital means (such as MES systems).
Challenges Facing the Manufacturing Industry
Insufficient transparency in the production process may lead to enterprises being unable to accurately grasp production progress, affecting the formulation and execution of delivery plans. Additionally, the production mode of small batches with multiple varieties requires enterprises to have extremely high production flexibility, and traditional management methods generally struggle to effectively meet these demands. Therefore, establishing a transparent, efficient production management system is the first step toward a digital factory.
Full process traceability and comprehensive quality management is an advanced process in building a digital factory. Ensuring that every production node has clear records facilitates tracing and management. Next, by comprehensively collecting and analyzing data, we establish a closed-loop management system from production to quality, achieving refined control over each link. From batch to SN traceability, comprehensive quality information must close the loop, which has become a mandatory requirement for current downstream clients towards upstream suppliers.
Integrated supply chains and collaboration between upstream and downstream is a goal after factories have developed to a certain scale. Besides digitizing their own factories, they also hope to integrate specific production lines of upstream suppliers (such as subcontracted workshops) into their platform, constructing an integrated supply chain platform. Amid globalization and refined operational trends, companies are more clearly defining their roles, making outsourcing/sub-contracting more economically viable.
🚀 NewCore Cloud's Practice and Exploration
In the practice of New Core Cloud, we discovered that only by fully digitizing fundamental aspects such as the production process, quality management, and supply chain collaboration can we lay a solid foundation for the application of AI Agents. For instance, to address the transparency issue in the production process, we implemented a series of data collection devices and tag tracking technologies, achieving detailed management of every production node. This not only enables enterprises to grasp production progress in real-time but also accumulates valuable data for future AI applications.
In terms of quality management, we introduced AI Agents to monitor and analyze product quality data in real-time, helping enterprises identify potential quality issues early, reducing rework and scrap rates. AI Agents can also conduct deep learning on historical data to identify key factors affecting quality, aiding enterprises in continuously improving their quality management systems.
We are also actively exploring how to empower manufacturing with AI. For example, by replacing manual repetitive work with AI visual inspection, increasing efficiency and precision; and by integrating with collaboration tools like Feishu to achieve real-time information sharing and collaboration between internal factory operations and upstream and downstream supply chains. With these applications, we believe that AI will gradually embed into every aspect of manufacturing, becoming an indispensable "digital assistant" for enterprises.
Additionally, we work closely with multiple partners to jointly create complete intelligent manufacturing solutions. For example, we collaborate with hardware suppliers to integrate AI Agents into production equipment, enabling devices to have self-learning and optimization capabilities, thereby further enhancing production efficiency. Simultaneously, we partner with ERP system suppliers to connect various information systems of enterprises, achieving comprehensive data sharing and business collaboration, facilitating synergy of data and business across enterprises.
🤔 AI is Not Omnipotent, But It is the Best Assistant
Despite the exciting prospects of AI, we must recognize that it is not omnipotent. It cannot fully replace human creativity and complex decision-making abilities but can become our best assistant by improving efficiency and reducing human errors. Therefore, the digital transformation of the manufacturing industry should be predicated on refining basic management, gradually transitioning towards AI empowerment. Only in this way can we usher in the era of truly intelligent factories.
The application of AI Agents can significantly alleviate the burden on employees in repetitive, standardized jobs, allowing them to focus more on innovation and complex tasks. The introduction of AI is not just an enhancement of efficiency; it also represents a transformation of organizational structures and operational models. In this process of transformation, human-AI collaboration will become the new norm, and enterprises will need to continuously adjust and optimize to achieve the best combination of human resources and AI.
🌟 Looking Forward to the Future
NewCore Cloud is committed to helping manufacturing enterprises solve current pain points through digital means while actively exploring AI application scenarios to achieve truly intelligent factories. In the future, we look forward to collaborating with more partners to jointly promote the intelligent and digital development of China's manufacturing industry. We believe that the combination of AI and manufacturing will create unprecedented opportunities and value.
The intelligent factories of the future will not only be places for producing products, but smart systems. In this system, the physical and virtual worlds will fully merge, with every device, product, and production line becoming part of the data, interacting with the entire system in real-time. Through this highly collaborative intelligent system, enterprises will be able to respond more agilely to market changes and achieve the goals of rapid production and on-demand customization.
We also firmly believe that the future of intelligence does not only belong to large enterprises. By continuously lowering the barriers to digitalization and intelligence, we hope that more small and medium-sized enterprises can also enjoy the benefits brought by this transformation. We will continue to provide flexible, user-friendly solutions to help various manufacturing enterprises achieve digital transformation and collectively embrace the arrival of the AI era.
