Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations

This research report, 《Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations》, authored by the Anthropic team, presents a systematic analysis of AI usage patterns in economic tasks by leveraging privacy-preserving data from millions of conversations on Claude.ai. The study aims to provide empirical insights into how AI is integrated into different occupational tasks and its impact on the labor market.

Research Background and Objectives

The rapid advancement of artificial intelligence (AI) has profound implications for the labor market. However, systematic empirical research on AI’s actual application in economic tasks remains scarce. This study introduces a novel framework that maps over four million conversations on Claude.ai to occupational categories from the U.S. Department of Labor’s O*NET database, identifying AI usage patterns and its impact on various professions. The research objectives include:

1. Measuring the scope of AI adoption in economic tasks, identifying which tasks and professions are most affected by AI.

2. Quantifying the depth of AI usage within occupations, assessing the extent of AI penetration in different job roles.

3. Evaluating AI’s application in different occupational skills, identifying the cognitive and technical skills where AI is most frequently utilized.

4. Analyzing the correlation between AI adoption, wage levels, and barriers to entry, determining whether AI usage aligns with occupational salaries and skill requirements.

5. Differentiating AI’s role in automation versus augmentation, assessing whether AI primarily functions as an automation tool or an augmentation assistant enhancing human productivity.

Key Research Findings

1. AI Usage is Predominantly Concentrated in Software Development and Writing Tasks

• The most frequently AI-assisted tasks include software engineering (e.g., software development, data science, IT services) and writing (e.g., technical writing, content editing, marketing copywriting), together accounting for nearly 50% of total AI usage.

• Approximately 36% of occupations incorporate AI for at least 25% of their tasks, indicating AI’s early-stage integration into diverse industry roles.

• Occupations requiring physical interaction (e.g., anesthesiologists, construction workers) exhibit minimal AI usage, suggesting that AI’s influence remains primarily within cognitive and text-processing domains.

2. Quantifying the Depth of AI Integration Within Occupations

• Only 4% of occupations utilize AI for over 75% of their tasks, indicating deep AI integration in select job roles.

• 36% of occupations leverage AI for at least 25% of tasks, signifying AI’s expanding role in various professional task portfolios, though full-scale adoption is still limited.

3. AI Excels in Tasks Requiring Cognitive Skills

• AI is most frequently employed for tasks that demand reading comprehension, writing, and critical thinking, while tasks requiring installation, equipment maintenance, negotiation, and management see lower AI usage.

• This pattern underscores AI’s suitability as a cognitive augmentation tool rather than a substitute for physically intensive or highly interpersonal tasks.

4. Correlation Between AI Usage, Wage Levels, and Barriers to Entry

• Wage Levels: AI adoption peaks in mid-to-high-income professions (upper quartile), such as software development and data analysis. However, very high-income (e.g., physicians) and low-income (e.g., restaurant workers) occupations exhibit lower AI usage, possibly due to:

  • High-income roles often requiring highly specialized expertise that AI cannot yet fully replace.

  • Low-income roles frequently involving significant physical tasks that are less suited for AI automation.

• Barriers to Entry: AI is most frequently used in occupations requiring a bachelor’s degree or higher (Job Zone 4), whereas occupations with the lowest (Job Zone 1) or highest (Job Zone 5) education requirements exhibit lower AI usage. This suggests that AI is particularly effective in knowledge-intensive, mid-tier skill professions.

5. AI’s Dual Role in Automation and Augmentation

• AI usage can be categorized into:

  • Automation (43%): AI directly executes tasks with minimal human intervention, such as document formatting, marketing copywriting, and code debugging.

  • Augmentation (57%): AI collaborates with users in refining outputs, optimizing code, and learning new concepts.

• The findings indicate that in most professions, AI is utilized for both automation (reducing human effort) and augmentation (enhancing productivity), reinforcing AI’s complementary role in the workforce.

Research Methodology

This study employs the Clio system (Tamkin et al., 2024) to classify and analyze Claude.ai’s vast conversation data, mapping it to O*NET’s occupational categories. The research follows these key steps:

1. Data Collection:

   • AI usage data from December 2024 to January 2025, encompassing one million interactions from both free and paid Claude.ai users.

   • Data was analyzed with strict privacy protection measures, excluding interactions from enterprise customers (API, team, or enterprise users).

2. Task Classification:

   • O*NET’s 20,000 occupational tasks serve as the foundation for mapping AI interactions.

   • A hierarchical classification model was applied to match AI interactions with occupational categories and specific tasks.

3. Skills Analysis:

   • The study mapped AI conversations to 35 occupational skills from O*NET.

   • Special attention was given to AI’s role in complex problem-solving, system analysis, technical design, and time management.

4. Automation vs. Augmentation Analysis:

   • AI interactions were classified into five collaboration modes:

     • Automation Modes: Directive execution, feedback-driven corrections.

     • Augmentation Modes: Task iteration, knowledge learning, validation.

   • Findings indicate a near 1:1 split between automation and augmentation, highlighting AI’s varied applications across different tasks.

Policy and Economic Implications

1. Comparing Predictions with Empirical Findings

• The research findings validate some prior AI impact predictions while challenging others:

  • Webb (2019) predicted AI’s most significant impact in high-income occupations; however, this study found that mid-to-high-income professions exhibit the highest AI adoption, while very high-income professions (e.g., doctors) remain less affected.

  • Eloundou et al. (2023) forecasted that 80% of occupations would see at least 10% of tasks impacted by AI. This study’s empirical data shows that approximately 57% of occupations currently use AI for at least 10% of their tasks, slightly below prior projections but aligned with expected trends.

2. AI’s Long-Term Impact on Occupations

• AI’s role in augmenting rather than replacing human work suggests that most occupations will evolve rather than disappear.

• Policy recommendations:

  • Monitor AI-driven workforce shifts to identify which occupations benefit and which face displacement risks.

  • Adapt education and workforce training programs to ensure workers develop AI collaboration skills rather than being displaced by automation.

Conclusion

This research systematically analyzes over four million Claude.ai conversations to assess AI’s integration into economic tasks, revealing:

• AI is primarily applied in software development, writing, and data analysis tasks.

• AI adoption is widespread but not universal, with 36% of occupations utilizing AI for at least 25% of tasks.

• AI usage exhibits a balanced distribution between automation (43%) and augmentation (57%).

• Mid-to-high-income occupations requiring a bachelor’s degree show the highest AI adoption, while low-income and elite specialized professions remain less affected.

As AI technologies continue to evolve, their role in the economy will keep expanding. Policymakers, businesses, and educators must proactively leverage AI’s benefits while mitigating risks, ensuring AI serves as an enabler of productivity and workforce transformation.

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Research on the Role of Generative AI in Software Development Lifecycle

In today's fast-evolving information technology landscape, software development has become a critical factor in driving innovation and enhancing competitiveness for businesses. As artificial intelligence (AI) continues to advance, Generative AI (GenAI) has demonstrated significant potential in the field of software development. This article will explore, from the perspective of the CTO of HaxiTAG, how Generative AI can support the software development lifecycle (SDLC), improve development efficiency, and enhance code quality.

Applications of Generative AI in the Software Development Lifecycle

Requirement Analysis Phase: Generative AI, leveraging Natural Language Processing (NLP) technology, can automatically generate software requirement documents. This assists developers in understanding business logic, reducing manual work and errors.

Design Phase: Using machine learning algorithms, Generative AI can automatically generate software architecture designs, enhancing design efficiency and minimizing risks. The integration of AIGC (Artificial Intelligence Generated Content) interfaces and image design tools facilitates creative design and visual expression. Through LLMs (Large Language Models) and Generative AI chatbots, it can assist in analyzing creative ideas and generating design drafts and graphical concepts.

Coding Phase: AI-powered code assistants can generate code snippets based on design documents and development specifications, aiding developers in coding tasks and reducing errors. These tools can also perform code inspections, switching between various perspectives and methods for adversarial analysis.

Testing Phase: Generative AI can generate test cases, improving test coverage and reducing testing efforts, ensuring software quality. It can conduct unit tests, logical analyses, and create and execute test cases.

Maintenance Phase: AI technologies can automatically analyze code and identify potential issues, providing substantial support for software maintenance. Through automated detection, evaluation analysis, and integration with pre-trained specialized knowledge bases, AI can assist in problem diagnosis and intelligent decision-making for problem-solving.

Academic Achievements in Generative AI

Natural Language Processing (NLP) Technology: NLP plays a crucial role in Generative AI. In recent years, China has made significant breakthroughs in NLP, such as with models like BERT and GPT, laying a solid foundation for the application of Generative AI in software development.

Machine Learning Algorithms: Machine learning algorithms are key to enabling automatic generation and supporting development in Generative AI. China has rich research achievements in machine learning, including deep learning and reinforcement learning, which support the application of Generative AI in software development.

Code Generation Technology: In the field of code generation, products such as GitHub Copilot, Sourcegraph Cody, Amazon Q Developer, Google Gemini Code Assist, Replit AI, Microsoft IntelliCode, JetBrains AI Assistant, and others, including domestic products like Wenxin Quick Code and Tongyi Lingma, are making significant strides. China has also seen progress in code generation technologies, including template-based and semantic-based code generation, providing the technological foundation for the application of Generative AI in software development.

Five Major Trends in the Development of AI Code Assistants

Core Feature Evolution

• Tab Completion: Efficient completion has become a “killer feature,” especially valuable in multi-file editing.
• Speed Optimization: Users have high expectations for low latency, directly affecting the adoption of these tools.

Support for Advanced Capabilities

• Architectural Perspective: Tools like Cursor are beginning to help developers provide high-level insights during the design phase, transitioning into the role of solution architects.

Context Awareness

• The ability to fully understand the project environment (such as codebase, documentation) is key to differentiated competition. Tools like GitHub Copilot and Augment Code offer contextual support.

Multi-Model Support

• Developers prefer using multiple LLMs simultaneously to leverage their individual strengths, such as the combination of ChatGPT and Claude.

Multi-File Creation and Editing

Supporting the creation and editing of multi-file contexts is essential, though challenges in user experience (such as unintended deletions) still remain.

As an assistant for production, research and coding knowledge

technology codes and products documents embedded with LLM frameworks, build the knowledge functions, components and data structures used in common company business, development documentation products, etc., it becomes a basic copilot to assist R&D staff to query information, documentation and debug problems. Hashtag and algorithm experts will discuss with you to dig the potential application opportunities and possibilities.

Challenges and Opportunities in AI-Powered Coding

As a product research and development assistant, embedding commonly used company frameworks, functions, components, data structures, and development documentation products into AI tools can act as a foundational "copilot" to assist developers in querying information, debugging, and resolving issues. HaxiTAG, along with algorithm experts, will explore and discuss potential application opportunities and possibilities.

Achievements of HaxiTAG in Generative AI Coding and Applications

As an innovative software development enterprise combining LLM, GenAI technologies, and knowledge computation, HaxiTAG has achieved significant advancements in the field of Generative AI:

• HaxiTAG CMS AI Code Assistant: Based on Generative AI technology, this tool integrates LLM APIs with the Yueli-adapter, enabling automatic generation of online marketing theme channels from creative content, facilitating quick deployment of page effects. It supports developers in coding, testing, and maintenance tasks, enhancing development efficiency.

• Building an Intelligent Software Development Platform: HaxiTAG is committed to developing an intelligent software development platform that integrates Generative AI technology across the full SDLC, helping partner businesses improve their software development processes.

• Cultivating Professional Talent: HaxiTAG actively nurtures talent in the field of Generative AI, contributing to the practical application and deepening of AI coding technologies. This initiative provides crucial talent support for the development of the software development industry.

Conclusion

The application of Generative AI in the software development lifecycle has brought new opportunities for the development of China's software industry. As an industry leader, HaxiTAG will continue to focus on the development of Generative AI technologies and drive the transformation and upgrading of the software development industry. We believe that in the near future, Generative AI will bring even more surprises to the software development field.

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How Is AI Transforming Content Creation and Distribution? Unpacking the Phenomenon Behind NotebookLM's Viral Success

With the rapid growth of AI language model applications, especially the surge of Google’s NotebookLM since October, discussions around "How AI is Transforming Content" have gained widespread attention.

The viral popularity of NotebookLM showcases the revolutionary role AI plays in content creation and information processing, fundamentally reshaping productivity on various levels. AI applications in news editing, for example, significantly boost efficiency while reducing labor costs. The threshold for content creation has been lowered by AI, improving both the precision and timeliness of information.

Exploring the entire content production chain, we delve into the widespread popularity of Google Labs’ NotebookLM and examine how AI’s lowered entry barriers have transformed content creation. We analyze the profound impacts of AI in areas such as information production, content editing and presentation, and information filtering, and we consider how these transformations are poised to shape the future of the content industry.

This article discusses how NotebookLM’s applications are making waves, exploring its use cases and industry background to examine AI's infiltration into the content industry, as well as the opportunities and challenges it brings.

Ten Viral NotebookLM Use Cases: Breakthroughs in AI Content Tools

1. Smart Summarization: NotebookLM can efficiently condense lengthy texts, allowing journalists and editors to quickly grasp event summaries, saving significant time and effort for content creators.

2. Multimedia Generation: NotebookLM-generated podcasts and audio content have gone viral on social media. By automatically generating audio from traditional text content, it opens new avenues for diversified content consumption.

3. Quick Knowledge Lookup: Users can instantly retrieve background information on specific topics, enabling content creators to quickly adapt to rapidly evolving news cycles.

4. Content Ideation: Beyond being an information management tool, NotebookLM also aids in brainstorming for new projects, encouraging creators to shift from passive information intake to proactive ideation.

5. Data Insight and Analysis: NotebookLM supports creators by generating insights and visual representations, enhancing their persuasiveness in writing and presentations, making it valuable for market analysis and trend forecasting.

6. News Preparation: Journalists use NotebookLM to organize interview notes and quickly draft initial articles, significantly shortening the content creation process.

7. Educational Applications: NotebookLM helps students swiftly grasp complex topics, while educational content creators can tailor resources for learners at various stages.

8. Content Optimization: NotebookLM’s intelligent suggestions enhance written expression, making content easier to read and more engaging.

9. Knowledge System Building: NotebookLM supports content creators in constructing thematic knowledge libraries, ideal for systematic organization and knowledge accumulation over extended content production cycles.

10. Cross-Disciplinary Content Integration: NotebookLM excels at synthesizing information across multiple fields, ideal for cross-domain reporting and complex topics.

How AI Is Redefining Content Supply and Demand

Content creation driven by AI transcends traditional supply-demand dynamics. Tools like NotebookLM can simplify and organize complex, specialized information, meeting the needs of today’s fast-paced readers. AI tools lower production barriers, increasing content supply while simultaneously balancing supply and demand. This shift also transforms the roles of traditional content creators.

Jobs such as designers, editors, and journalists can accomplish tasks more efficiently with AI assistance, freeing up time for other projects. Meanwhile, AI-generated content still requires human screening and refinement to ensure accuracy and applicability.

The Potential Risks of AI Content Production: Information Distortion and Data Bias

As AI tools become widely used in content creation, the risk of misinformation and data bias is also rising. Tools like NotebookLM rely on large datasets, which can unintentionally amplify biases if present in the training data. These risks are especially prominent in fields such as journalism and education. Therefore, AI content creators must exercise strict control over information sources to minimize misinformation.

The proliferation of AI content production tools may also lead to information overload, overwhelming audiences. Users need to develop discernment skills, verifying information sources to improve content consumption quality.

The Future of AI Content Tools: From Assistance to Independent Creation?

Currently, AI content creation tools like NotebookLM primarily serve as aids, but future developments suggest they may handle more independent content creation tasks. Google Labs’ development of NotebookLM demonstrates that AI content tools are not merely about extracting information but are built on deep-seated logical understanding. In the future, NotebookLM is expected to advance with deep learning technology, enabling more flexible content generation, potentially understanding user needs proactively and producing more personalized content.

Conclusion: AI in Content Production — A Double-Edged Sword

NotebookLM’s popularity reaffirms the tremendous potential of AI in content creation. From smart summarization to multimedia generation and cross-disciplinary integration, AI is not only a tool for content creators but also a driving force within the content industry. However, as AI permeates the content industry, the risks of misinformation and data bias increase. NotebookLM provides new perspectives and tools for content creation, yet balancing creativity and authenticity remains a critical challenge that AI content creation must address.

AI is progressively transforming every aspect of content production. In the future, AI may undertake more independent creation tasks, freeing humans from repetitive foundational content work and becoming a powerful assistant in content creation. At the same time, information accuracy and ethical standards will be indispensable aspects of AI content creation.

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HaxiTAG PreSale BOT: Build Your Conversions from Customer login

With the rapid advancement of digital technology, businesses face increasing challenges, especially in efficiently converting website visitors into actual customers. Traditional marketing and customer management approaches are becoming cumbersome and costly. To address this challenge, HaxiTAG PreSale BOT was created. This embedded intelligent solution is designed to optimize the conversion process of website visitors. By harnessing the power of LLM (Large Language Models) and Generative AI, HaxiTAG PreSale BOT provides businesses with a robust tool, making customer acquisition and conversion more efficient and precise.

                Image: From Tea Room to Intelligent Bot Reception

1. Challenges of Reaching Potential Customers

In traditional customer management, converting potential customers often involves high costs and complex processes. From initial contact to final conversion, this lengthy process requires significant human and resource investment. If mishandled, the churn rate of potential customers will significantly increase. As a result, businesses are compelled to seek smarter and more efficient solutions to tackle the challenges of customer conversion.

2. Automation and Intelligence Advantages of HaxiTAG PreSale BOT

HaxiTAG PreSale BOT simplifies the pre-sale service process by automatically creating tasks, scheduling professional bots, and incorporating human interaction. Whether during a customer's first visit to the website or during subsequent follow-ups and conversions, HaxiTAG PreSale BOT ensures smooth transitions throughout each stage, preventing customer churn due to delays or miscommunication.

This automated process not only reduces business operating costs but also greatly improves customer satisfaction and brand loyalty. Through in-depth analysis of customer behavior and needs, HaxiTAG PreSale BOT can adjust and optimize touchpoints in real-time, ensuring customers receive the most appropriate service at the most opportune time.

3. End-to-End Digital Transformation and Asset Management

The core value of HaxiTAG PreSale BOT lies in its comprehensive coverage and optimization of the customer journey. Through digitalized and intelligent management, businesses can convert their customer service processes into valuable assets at a low cost, achieving full digital transformation. This intelligent customer engagement approach not only shortens the time between initial contact and conversion but also reduces the risk of customer churn, ensuring that businesses maintain a competitive edge in the market.

4. Future Outlook: The Core Competitiveness of Intelligent Transformation

In the future, as technology continues to evolve and the market environment shifts, HaxiTAG PreSale BOT will become a key competitive edge in business marketing and service, thanks to its efficient conversion capabilities and deep customer insights. For businesses seeking to stay ahead in the digital wave, HaxiTAG PreSale BOT is not just a powerful tool for acquiring potential customers but also a vital instrument for achieving intelligent transformation.

By deeply analyzing customer profiles and building accurate conversion models, HaxiTAG PreSale BOT helps businesses deliver personalized services and experiences at every critical touchpoint in the customer journey, ultimately achieving higher conversion rates and customer loyalty. Whether improving brand image or increasing sales revenue, HaxiTAG PreSale BOT offers businesses an effective solution.

HaxiTAG PreSale BOT is not just an embedded intelligent tool; it features a consultative and service interface for customer access, while the enterprise side benefits from statistical analysis, customizable data, and trackable customer profiles. It represents a new concept in customer management and marketing. By integrating LLM and Generative AI technology into every stage of the customer journey, HaxiTAG PreSale BOT helps businesses optimize and enhance conversion rates from the moment customers log in, securing a competitive advantage in the fierce market landscape.

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Building "Living Software Systems": A Future Vision with Generative and Agentic AI

 In modern society, software has permeated every aspect of our lives. However, a closer examination reveals that these systems are often static and rigid. As user needs evolve, these systems struggle to adapt quickly, creating a significant gap between human goals and computational operations. This inflexibility not only limits the enhancement of user experience but also hampers further technological advancement. Therefore, finding a solution that can dynamically adapt and continuously evolve has become an urgent task in the field of information technology.

Generative AI: Breathing Life into Software

Generative AI, particularly large language models (LLMs), presents an unprecedented opportunity to address this issue. These models not only understand and generate natural language but also adapt flexibly to different contexts, laying the foundation for building "living software systems." The core of generative AI lies in its powerful "translation" capability—it can seamlessly convert human intentions into executable computer operations. This translation is not merely limited to language conversion; it extends to the smooth integration between intention and action.

With generative AI, users no longer need to face cumbersome interfaces or possess complex technical knowledge. A simple command is all it takes for AI to automatically handle complex tasks. For example, a user might simply instruct the AI: "Process the travel expenses for last week's Chicago conference," and the AI will automatically identify relevant expenses, categorize them, summarize, and submit the reimbursement according to company policy. This highly intelligent and automated interaction signifies a shift in software systems from static to dynamic, from rigid to flexible.

Agentic AI: Creating Truly "Living Software Systems"

However, generative AI is only one part of building "living software systems." To achieve true dynamic adaptability, the concept of agentic AI must be introduced. Agentic AI can flexibly invoke various APIs (Application Programming Interfaces) and dynamically execute a series of operations based on user instructions. By designing "system prompts" or "root prompts," agentic AI can autonomously make decisions in complex environments to achieve the user's ultimate goals.

For instance, when processing a travel reimbursement, agentic AI would automatically check existing records to avoid duplicate submissions and process the request according to the latest company policies. More importantly, agentic AI can adjust based on actual conditions. For example, if an unrelated receipt is included in the reimbursement, the AI won't crash or refuse to process it; instead, it will prompt the user for further confirmation. This dynamic adaptability makes software systems no longer "dead" but truly "alive."

Step-by-Step Guide to Building "Living Software Systems"

To achieve the aforementioned goals, a systematic guide is required:

1. Demand Analysis and Goal Setting: Deeply understand the user's needs and clearly define the key objectives that the system needs to achieve, ensuring the correct development direction.

2. Integration of Generative AI: Choose the appropriate generative AI model according to the application scenario, and train and fine-tune it with a large amount of data to improve the model's accuracy and efficiency.

3. Implementation of Agentic AI: Design system prompts to guide agentic AI on how to use underlying APIs to achieve user goals, ensuring the system can flexibly handle various changes in actual operations.

4. User Interaction Design: Create context-aware user interfaces that allow the system to automatically adjust operational steps based on the user's actual situation, enhancing the user experience.

5. System Optimization and Feedback Mechanisms: Continuously monitor and optimize the system's performance through user feedback, ensuring the system consistently operates efficiently.

6. System Deployment and Iteration: Deploy the developed system into the production environment and continuously iterate and update it based on actual usage, adapting to new demands and challenges.

Conclusion: A Necessary Path to the Future

"Living software systems" represent not only a significant shift in software development but also a profound transformation in human-computer interaction. In the future, software will no longer be just a tool; it will become an "assistant" that understands and realizes user needs. This shift not only enhances the operability of technology but also provides users with unprecedented convenience and intelligent experiences.

Through the collaboration of generative and agentic AI, we can build more flexible, dynamically adaptive "living software systems." These systems will not only understand user needs but also respond quickly and continuously evolve in complex and ever-changing environments. As technology continues to develop, building "living software systems" will become an inevitable trend in future software development, leading us toward a more intelligent and human-centric technological world.

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EiKM: Rebuilding Competitive Advantage through Knowledge Innovation and Application

In modern enterprises, the significance of Knowledge Management (KM) is undeniable. However, the success of KM projects relies not only on technological sophistication but also on a clear vision for organizational service delivery models and effective change management. This article delves into the critical elements of KM from three perspectives: management, technology, and personnel, revealing how knowledge innovation can be leveraged to gain a competitive edge.

1. Management Perspective: Redefining Roles and Responsibility Matrices

The success of KM practices directly impacts employee experience and organizational efficiency. Traditional KM often focuses on supportive metrics such as First Contact Resolution (FCR) and Time to Resolution (TTR). However, these metrics frequently conflict with the core objectives of KM. Therefore, organizations need to reassess and adjust these operational metrics to better reflect the value of KM projects.

By introducing the Enterprise Intelligence Knowledge Management (EiKM) system, organizations can exponentially enhance KM outcomes. This system not only integrates enterprise private data, industry-shared data, and public media information but also ensures data security through privatized knowledge computing engines. For managers, the key lies in continuous multi-channel communication to clearly convey the vision and the “why” and “how” of KM implementation. This approach not only increases employee recognition and engagement but also ensures the smooth execution of KM projects.

2. Personnel Perspective: Enhancing Execution through Change Management

The success of KM projects is not just a technological achievement but also a deep focus on the “people” aspect. Leadership often underestimates the importance of organizational change management, which is critical to the success of KM projects. Clear role and responsibility allocation is key to enhancing the execution of KM. During this process, communication strategies are particularly important. Shifting from a traditional command-based communication approach to a more interactive dialogue can help employees better adapt to changes, enhancing their capabilities rather than merely increasing their commitment.

Successful KM projects need to build service delivery visions based on knowledge and clearly define their roles in both self-service and assisted-service channels. By integrating KM goals into operational metrics, organizations can ensure that all measures are aligned, thereby improving overall organizational efficiency.

3. Technology and Product Experience Perspective: Integration and Innovation

In the realm of KM technology and product experience, integration is key. Modern KM technologies have already been deeply integrated with Customer Relationship Management (CRM) and ticketing systems, such as customer interaction platforms. By leveraging unified search experiences, chatbots, and artificial intelligence, these technologies significantly simplify knowledge access, improving both the quality of customer self-service and employee productivity.

In terms of service delivery models, the article proposes embedding knowledge management into both self-service and assisted-service channels. Each channel should operate independently while ensuring interoperability to form a comprehensive and efficient service ecosystem. Additionally, by introducing gamification features such as voting, rating, and visibility of knowledge contributions into the KM system, employee engagement and attention to knowledge management can be further enhanced.

4. Conclusion: From Knowledge Innovation to Rebuilding Competitive Advantage

In conclusion, successful knowledge management projects must achieve comprehensive integration and innovation across technology, processes, and personnel. Through a clear vision of service delivery models and effective change management, organizations can gain a unique competitive advantage in a fiercely competitive market. The EiKM system not only provides advanced knowledge management tools but also redefines the competitive edge of enterprises through knowledge innovation.

Enterprises need to recognize that knowledge management is not merely a technological upgrade but a profound transformation of the overall service model and employee work processes. Throughout this journey, precise management, effective communication strategies, and innovative technological approaches will enable enterprises to maintain a leading position in an ever-changing market, continuously realizing the competitive advantages brought by knowledge innovation.

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Utilizing Generative AI and LLM Tools for Competitor Analysis: Gaining a Competitive Edge

In today’s fiercely competitive market, how businesses conduct in-depth competitor analysis to identify market opportunities, optimize strategies, and devise plans to outmaneuver competitors is crucial to maintaining a leading position. HaxiTAG, through its robust AI-driven market research tools, offers comprehensive solutions for competitor analysis, helping businesses stand out in the competition.

Core Features and Advantages of HaxiTAG Tools

1. Data Collection and Integration
   HaxiTAG tools utilize AI technology to automatically gather public information about competitors from multiple data sources, such as market trends, consumer feedback, financial data, and product releases. This data is integrated and standardized to ensure accuracy and consistency, laying a solid foundation for subsequent analysis.

2. Competitor Analysis
   Once the data is collected, HaxiTAG employs advanced AI algorithms to conduct in-depth analysis. The tools identify competitors’ strengths, weaknesses, market strategies, and potential risks, providing businesses with comprehensive and detailed insights into their competitors. The analysis results are presented in a visualized format, making it easier for businesses to understand and apply the findings.

3. Trend Forecasting and Opportunity Identification
   HaxiTAG tools not only focus on current market conditions but also use machine learning models to predict future market trends. Based on historical data and market dynamics, the tools help businesses identify potential market opportunities and adjust their strategies accordingly to gain a competitive edge.

4. Strategic Optimization Suggestions
   Based on AI analysis results, the tools offer specific action recommendations to help businesses optimize existing strategies or develop new ones. These suggestions are highly targeted and practical, enabling businesses to effectively respond to competitors’ challenges.

5. Continuous Monitoring and Adjustment
   Markets are dynamic, and HaxiTAG supports real-time monitoring of competitors’ activities. By promptly identifying new threats or opportunities, businesses can quickly adjust their strategies based on real-time data, ensuring they maintain flexibility and responsiveness in the market.

Beginner’s Guide to Practice

• Getting Started
  New users can input target markets and key competitors’ information into the HaxiTAG platform, which will automatically gather and present relevant data. This process simplifies traditional market research steps, allowing users to quickly enter the core aspects of competitor analysis.

• Understanding Analysis Results
  Users need to learn how to interpret AI-generated analysis reports and visual charts. Understanding this data and grasping competitors’ market strategies are crucial for formulating effective action plans.

• Formulating Action Plans
  Based on the optimization suggestions provided by HaxiTAG tools, users can devise specific action steps and continuously monitor their effectiveness during implementation. The tools’ automated recommendations ensure that strategies are highly targeted.

• Maintaining Flexibility
  Given the ever-changing market environment, users should regularly use HaxiTAG tools for market monitoring and timely strategy adjustments to maintain a competitive advantage.

Limitations and Constraints

• Data Dependency
  HaxiTAG’s analysis results depend on the quality and quantity of available data. If data sources are limited or inaccurate, it may affect the accuracy of the analysis. Therefore, businesses need to ensure the breadth and reliability of data sources.

• Market Dynamics Complexity
  Although HaxiTAG tools can provide detailed market analysis and forecasts, the dynamic and unpredictable nature of the market may exceed the predictive capabilities of AI models. Thus, final strategic decisions still require human expertise and judgment.

• Implementation Challenges
  For beginners, although HaxiTAG tools offer detailed strategic suggestions, effectively implementing these suggestions may still be challenging. This may require deeper market knowledge and execution capabilities.

Conclusion

By utilizing Generative AI and LLM technologies, HaxiTAG helps businesses gain critical market insights and strategic advantages in competitor analysis. The core strength lies in the automated data processing and in-depth analysis, providing businesses with precise, real-time market insights to maintain a leading position in the competitive landscape. Despite some challenges, HaxiTAG’s comprehensive advantages make it an indispensable tool for businesses in market research and competitor analysis.

By leveraging this tool, business partners can better seize market opportunities, devise action plans that surpass competitors, and ultimately achieve an unassailable position in the competition.

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NVIDIA Unveils NIM Agent Blueprints: Accelerating the Customization and Deployment of Generative AI Applications for Enterprises

As generative AI emerges as a key driver of digital transformation, NVIDIA has introduced NIM Agent Blueprints—a pre-trained and customizable directory of AI workflows designed to support enterprises in developing and operating generative AI applications. The release of NIM Agent Blueprints marks a new phase in enterprise AI adoption, providing a comprehensive set of tools from code to deployment, enabling businesses to swiftly build, optimize, and seamlessly deploy tailored AI applications.

Core Value of NIM Agent Blueprints

Powered by the NVIDIA AI Enterprise platform, NIM Agent Blueprints include reference code, deployment documentation, and Helm charts, offering pre-trained and customizable AI workflows for a variety of business scenarios. Global partners such as Accenture, Cisco, and Dell have expressed that NIM Agent Blueprints will accelerate the deployment and expansion of generative AI applications in enterprises. NVIDIA founder and CEO Jensen Huang emphasized that NIM Agent Blueprints enable enterprises to customize open-source models, thereby building proprietary AI applications and achieving efficient deployment and operation.

This blueprint directory supports specific workflows such as digital human customer service, virtual screening for drug discovery, and multimodal PDF data extraction. Moreover, it can be customized according to an enterprise's business data, forming a data-driven AI flywheel. This customization capability allows businesses to optimize AI applications based on actual business needs and continuously improve them as user feedback accumulates, significantly enhancing operational efficiency and user experience.

Strategic Significance of Global Partner Involvement

The success of NIM Agent Blueprints is closely tied to the support of global partners. These partners not only provide full-stack infrastructure, specialized software, and services but also play a crucial role in the implementation of generative AI applications within enterprises. Companies like Accenture, Deloitte, and SoftServe have already integrated NIM Agent Blueprints into their solutions, helping corporate clients gain an edge in digital transformation through rapid deployment and scalability.

The CEOs of these partners unanimously agree that generative AI requires robust infrastructure as well as dedicated tools and services to support its deployment and optimization in enterprise-level applications. NIM Agent Blueprints are designed with this purpose in mind, offering enterprises a comprehensive support system from inception to maturity, enabling the full potential of generative AI to be realized.

Application Prospects of NIM Agent Blueprints

Through NIM Agent Blueprints, enterprises can not only customize generative AI applications but also achieve rapid deployment and scalability with the help of partners. This capability allows companies to maintain competitiveness in the wave of digital transformation, especially in industries that require quick responses to market changes and user demands.

For instance, the digital human workflow within NIM Agent Blueprints, leveraging NVIDIA's Tokkio technology, can provide a more humanized customer service experience. This demonstrates that generative AI can not only enhance business efficiency but also significantly improve the quality of user interactions, leading to higher customer satisfaction and loyalty.

HaxiTAG Consulting Team’s Assistance and Outlook

When evaluating the applicability of NVIDIA NIM Agent Blueprints, the HaxiTAG consulting team will offer professional advisory services to help enterprises better understand and apply this toolset. Through close collaboration with partners, HaxiTAG will ensure that enterprises can fully leverage the advantages of NIM Agent Blueprints to achieve seamless deployment and efficient operation of generative AI applications.

In summary, NIM Agent Blueprints not only provide enterprises with a powerful starting tool but also offer strong support for continuous growth through their customizable and optimizable capabilities. As the application of generative AI continues to expand, NIM Agent Blueprints will become a significant driver of digital transformation and innovation for enterprises.

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Exploring Human-Machine Interaction Patterns in Applications of Large Language Models and Generative AI

In the current technological era, intelligent software applications driven by Large Language Models (LLMs) and Generative AI (GenAI) are rapidly transforming the way we interact with technology. These applications present various forms of interaction, from information assistants to scenario-based task execution, each demonstrating powerful functionalities and wide-ranging application prospects. This article delves into the core forms of these intelligent software applications and their significance in the future digital society.

1. Chatbot: Information Assistant

The Chatbot has become the most well-known representative tool in LLM applications. Top applications such as ChatGPT, Claude, and Gemini, achieve smooth dialogue with users through natural language processing technology. These Chatbots can not only answer users' questions but also provide more complex responses based on context, even engaging in creative processes and problem-solving. They have become indispensable tools in daily life, greatly enhancing the efficiency and convenience of information acquisition.

The strength of Chatbots lies in their flexibility and adaptability. They can learn from user input, gradually offering more personalized and accurate services. This ability allows Chatbots to go beyond providing standardized answers, adapting their responses according to users' needs, thereby playing a role in various application scenarios. For instance, on e-commerce platforms, Chatbots can act as customer service representatives, helping users find products, track orders, or resolve after-sales issues. In the education sector, Chatbots can assist students in answering questions, providing learning resources, and even offering personalized tutoring as virtual mentors.

2. Copilot Models: Task Execution Assistant

Copilot models represent another important form of AI applications, deeply embedded in various platforms and systems as task execution assistants. These assistants aim to improve the efficiency and quality of users' primary tasks. Examples like Office 365 Copilot, GitHub Copilot, and Cursor can provide intelligent suggestions and assistance during task execution, reducing human errors and improving work efficiency.

The key advantage of Copilot models is their embedded design and efficient task decomposition capabilities. During the execution of complex tasks, these assistants can provide real-time suggestions and solutions, such as recommending best practices during coding or automatically adjusting formats and content during document editing. This task assistance capability significantly reduces the user's workload, allowing them to focus on more creative and strategic work.

3. Semantic Search: Integrating Information Sources

Semantic Search is another important LLM-driven application, demonstrating strong capabilities in information retrieval and integration. Similar to Chatbots, Semantic Search is also an information assistant, but it focuses more on the integration of complex information sources and the processing of multimodal data. Top applications like Perplexity and Metaso use advanced semantic analysis technology to quickly and accurately extract useful information from vast amounts of data and present it in an integrated form to users.

The application value of Semantic Search in today's information-intensive environment is immeasurable. As data continues to grow explosively, extracting useful information from it has become a major challenge. Semantic Search, through deep learning and natural language processing technologies, can understand users' search intentions and filter out the most relevant results from multiple information sources. This not only improves the efficiency of information retrieval but also enhances users' decision-making capabilities. For example, in the medical field, Semantic Search can help doctors quickly find relevant research results from a large number of medical literature, supporting clinical decision-making.

4. Agentic AI: Scenario-Based Task Execution

Agentic AI represents a new height in generative AI applications, capable of highly automated task execution in specific scenarios through scenario-based tasks and goal-loop logic. Agentic AI can autonomously program, automatically route tasks, and achieve precise output of the final goal through automated evaluation and path selection. Its application ranges from text data processing to IT system scheduling, even extending to interactions with the physical world.

The core advantage of Agentic AI lies in its high degree of autonomy and flexibility. In specific scenarios, this AI system can independently judge and select the best course of action to efficiently complete tasks. For example, in the field of intelligent manufacturing, Agentic AI can autonomously control production equipment, adjusting production processes in real-time based on data to ensure production efficiency and product quality. In IT operations, Agentic AI can automatically detect system failures and perform repair operations, reducing downtime and maintenance costs.

5. Path Drive: Co-Intelligence

Path Drive reflects a recent development trend in the AI research field—Co-Intelligence. This concept emphasizes the collaborative cooperation between different models, algorithms, and systems to achieve higher levels of intelligent applications. Path Drive not only combines AI's computing power with human wisdom but also dynamically adjusts decision-making mechanisms during task execution, improving overall efficiency and the reliability of problem-solving.

The significance of Co-Intelligence lies in that it is not merely a way of human-machine collaboration but also an important direction for the future development of intelligent systems. Path Drive achieves optimal decision-making in complex tasks by combining human judgment with AI's computational power. For instance, in medical diagnosis, Path Drive can combine doctors' expertise with AI's analytical capabilities to provide more accurate diagnostic results. In enterprise management, Path Drive can adjust decision strategies based on actual situations, thereby improving overall operational efficiency.

Summary and Outlook

LLM-based generative AI-driven intelligent software applications are comprehensively enhancing user experience and system performance through diverse interaction forms. Whether it's information consultation, task execution, or the automated resolution of complex problems, these application forms have demonstrated tremendous potential and broad prospects. However, as technology continues to evolve, these applications also face a series of challenges, such as data privacy, ethical issues, and potential impacts on human work.

Looking ahead, we can expect these intelligent software applications to continue evolving and integrating. For instance, we might see more intelligent Agentic systems that seamlessly integrate the functionalities of Chatbots, Copilot models, and Semantic Search. At the same time, as models continue to be optimized and new technologies are introduced, the boundaries of these applications' capabilities will continue to expand.

Overall, LLM-based generative AI-driven intelligent software is pioneering a new computational paradigm. They are not just tools but extensions of our cognitive and problem-solving abilities. As participants and observers in this field, we are in an incredibly exciting era, witnessing the deep integration of technology and human wisdom. As technology advances and the range of applications expands, we have every reason to believe that these intelligent software applications will continue to lead the future and become an indispensable part of the digital society.

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Unlocking the Future of Customer Interaction and Market Research: The Transformative Power of HaxiTAG AI for Comprehensive Coverage and Precise Insights

HaxiTAG AI is introducing this groundbreaking new technology into market research, customer support, and customer-facing service interactions. Whether it’s customer support, sales, or customer success teams, every conversation with your customers is an opportunity to understand your business and identify customer needs.

Understanding Customer and Market Challenges

1. Issues to Explore and Analyze:
   The problems that need to be examined in-depth.

2. Questions Needing Immediate Research:
   Inquiries from customers that require prompt investigation.

3. Signals from Daily Operations:
   Routine activities that may reveal underlying issues. While most companies have a general grasp of categories they need to manage, there's often a wealth of untapped information due to human resource limitations.

4. Listening to Customers:
   Strive to listen to your customers as thoroughly as possible and understand them within your capacity. However, as your company grows and the number of customers increases, daily communication with them may become challenging.

The Scale Problem in Customer and Market Interactions

This issue indeed accompanies success. When the number of customers is manageable, you can typically leverage your staff, sales teams, or customer support teams to gain insights and better guide your company toward greater revenue growth. But as you expand to a size where managing these vast conversations becomes nearly impossible, you’ll realize that much is happening without your awareness.

Traditional Methods of Customer Data Analysis

We believe that every large-scale enterprise is attempting to manually review and conduct small-sample analyses, aiming to collect and evaluate about 5% of conversations. This may involve checking compliance matters, like how agents handle situations, or identifying common themes in these conversations.

Ultimately, this is just sampling, and everyone is dissatisfied because they understand that it’s not a very accurate process. Then you begin involving engineers to write scripts, perform post-analysis, extract data from various customer interaction systems, and conduct lengthy analyses. Eventually, you hope to gain insights that can be tracked in the future.

The Role of Generative AI in Transformation

Next, you enter a stage of building software to look for very specific content in every conversation. But everything is retrospective—events have already occurred, and you were unaware of the signs. This is where generative AI can truly change the process.

Generative AI unlocks the incredible ability to cover 100% of the data. Now, you can use generative AI to discover things you didn’t even know you were looking for, reviewing everything at once, rather than just sampling or seeking known issues.

Practical Examples of AI in Customer Interactions

Here’s a great example: a brief interaction with a random agent handling customer chat. From this customer message, you can identify the reason for the customer’s communication—that’s your intent. Which aspects of our business are truly the root cause of this issue? The router, damaged delivery—perhaps it’s a supply chain issue. You can also gauge emotions, not just of the customer but also of your agent, which may be even more critical.

In the end, through every message, you can extract more in-depth information from a conversation than ever before. This is the service our platform strives to provide.

The Actual Impact of the HaxiTAG AI Platform

Here’s a great example from one of our clients, a wind power operator. One insight we provided was identifying defects in their wind turbine operations and maintenance. Some issues might persist for weeks without IT technical support to uncover them, potentially evolving into bigger problems. But our platform can detect these issues in real-time, significantly increasing the power generation revenue from their operations and maintenance.

The Process Behind AI Technology

How does all this work? It all starts with collecting all these conversations. This part is the non-AI mundane work, where we connect to numerous contact systems, ticket systems, and so forth. We pull all this information in, normalize it, clean it thoroughly, and prepare it for compression and processing by LLM prompts.

We have dozens of pipelines to evaluate these conversations in different ways, all of which can be configured by the user. Our customers can tell us what they care about, what they are searching for, and they actually collaborate with us to craft these prompts. Ultimately, they write the prompts themselves and manage them over time.

The Critical Importance of Accuracy in Enterprise AI

Why is accuracy ultimately the most important? When dealing with enterprise-scale operations, the primary concern is accuracy. There’s significant market concern about accuracy. Can I deploy generative AI to try to understand these conversations and truly trust these insights? When we work with customers, within seven days, we aim to demonstrate these insights to them. From that point forward, we strive to achieve 97% accuracy. However, this requires extensive sampling and trial and error. Ultimately, we seek to build trust with our customers because that will ensure they continue to renew and become long-term clients.

The Role of HaxiTAG AI in AI Implementation

HaxiTAG AI plays a crucial role in helping us achieve this goal. They not only provide our engineering team with a plethora of features and capabilities but also assist wind power domain experts, not IT specialists, in understanding the quality of the code they write through standardized components and interactive experiences. More importantly, our solution engineers and implementation engineers work with customers to debug and ultimately receive positive feedback. Customers tell us, “For certain things, the HaxiTAG AI tool is the go-to tool in this process.”

Conclusion and the Future of Self-Improving AI Systems

HaxiTAG AI has built an infrastructure layer in generative AI programs and LLM-driven large-scale data and knowledge application solutions to enhance the accuracy and reliability of AI applications while significantly lowering the barrier to entry. Our initial vision was to build a self-improving system—a system with LLM applications capable of refining prompts and models, ultimately driving accuracy and enhancing the utility of customer digital transformation.

The vision we are striving to achieve is one where HaxiTAG AI helps you turn your business data into assets, build new competitive advantages, and achieve better growth.

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HaxiTAG Path to Exploring Generative AI: From Purpose to Successful Deployment

The rise of generative AI marks a significant milestone in the field of artificial intelligence. It represents not only a symbol of technological advancement but also a powerful engine driving business transformation. To ensure the successful deployment of generative AI projects, the "HaxiTAG Generative AI Planning Roadmap" provides enterprises with detailed guidance covering all aspects from goal setting to model selection. This article delves into this roadmap, helping readers understand its core elements and application scenarios.

Purpose Identification: From Vision to Reality

Every generative AI project starts with clear goal setting. Whether it’s text generation, translation, or image creation, the final goals dictate resource allocation and execution strategy. During the goal identification phase, businesses need to answer key questions: What do we want to achieve with generative AI? How do these goals align with our business strategy? By deeply considering these questions, enterprises can ensure the project remains on track, avoiding resource wastage and misdirection.

Application Scenarios: Tailored AI Solutions

The true value of generative AI lies in its wide range of applications. Whether for customer-facing interactive applications or internal process optimization, each scenario demands specific AI capabilities and performance. To achieve this, businesses must deeply understand the needs of their target users and design and adjust AI functionalities accordingly. Data collection and compliance also play a crucial role, ensuring that AI operates effectively and adheres to legal and ethical standards.

Requirements for Successful Construction and Deployment: From Infrastructure to Compliance

Successful generative AI projects depend not only on initial goal setting and application scenario analysis but also on robust technical support and stringent compliance considerations. Team capabilities, data quality, tool sophistication, and infrastructure reliability are the cornerstones of project success. At the same time, privacy, security, and legal compliance issues must be integrated throughout the project lifecycle. This is essential not only for regulatory compliance but also for building user trust in AI systems, ensuring their sustainability in practical applications.

Model Selection and Customization: Balancing Innovation and Practice 

In the field of generative AI, model selection and customization are crucial steps. Enterprises must make informed choices between building new models and customizing existing ones. This process involves not only technical decisions but also resource allocation, innovation, and risk management. Choosing appropriate training, fine-tuning, or prompt engineering methods can help businesses find the best balance between cost and effectiveness, achieving the desired output.

Training Process: From Data to Wisdom

The core of generative AI lies in the training process. This is not merely a technical operation but a deep integration of data, algorithms, and human intelligence. The selection of datasets, allocation of specialized resources, and design of evaluation systems will directly impact AI performance and final output. Through a carefully designed training process, enterprises can ensure that their generative AI exhibits high accuracy and reliability while continually evolving and adapting to complex application environments.

Summary: The Path to Success with Generative AI

In summary, the "Generative AI Planning Roadmap" provides enterprises with a comprehensive guide to maintaining goal alignment, resource allocation, and compliance during the implementation of generative AI projects. It emphasizes the importance of comprehensive planning to ensure each phase of the project progresses smoothly. Although implementing generative AI may face challenges such as resource intensity, ethical complexity, and high data requirements, these challenges can be effectively overcome through scientific planning and meticulous execution.

As an expert in GenAI-driven intelligent industry application, HaxiTAG studio is helping businesses redefine the value of knowledge assets. By deeply integrating cutting-edge AI technology with business applications, HaxiTAG not only enhances organizational productivity but also stands out in the competitive market. As more companies recognize the strategic importance of intelligent knowledge management, HaxiTAG is becoming a key force in driving innovation in this field. In the knowledge economy era, HaxiTAG, with its advanced EiKM system, is creating an intelligent, digital knowledge management ecosystem, helping organizations seize opportunities and achieve sustained growth amidst digital transformation.

Generative AI holds immense potential, and the key to success lies in developing a clear and actionable planning roadmap from the outset. It is hoped that this article provides valuable insights for readers interested in generative AI, helping them navigate this cutting-edge field more effectively.

Join the HaxiTAG Generative AI Research Community to access operational guides.

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