Integrating Data with AI and Large Models to Build Enterprise Intelligence

By leveraging Artificial Intelligence (AI) and Large Language Models (LLMs) on the foundation of data assetization and centralized storage, enterprises can achieve intelligent decision-making, automated business processes, and data-driven innovation. This enables them to build unique competitive advantages in the era of intelligence. The following discussion delves into how data integrates with AI and LLMs, core application scenarios, intelligent decision-making approaches, business automation, innovation pathways, and key challenges.

Integration of Data, AI, and Large Models

With centralized data storage, enterprises can utilize AI to extract deeper insights, conduct analysis, and make predictions to support the development of intelligent applications. Key integration methods include:

1. Intelligent Data Analysis

   • Utilize Machine Learning (ML) and Deep Learning (DL) models to unlock data value, enhancing predictive and decision-making capabilities.

   • Apply large models (such as GPT, BERT, Llama, etc.) for Natural Language Processing (NLP) to enable applications like intelligent customer service, smart search, and knowledge management.

2. Enhancing Large Model Capabilities with Data

   • Enterprise-Specific Knowledge Base Construction: Fine-tune large models using historical enterprise data and industry insights to embed domain-specific expertise.

   • Real-Time Data Integration: Combine large models with real-time data (e.g., market trends, user behavior, supply chain data) to improve forecasting accuracy.

3. Data-Driven Intelligent Application Development

   • Convert structured and unstructured data (text, images, voice, video, etc.) into actionable insights via AI models to support enterprise-level intelligent application development.

Core Application Scenarios of AI and Large Models

Enterprises can leverage Data + AI + LLMs to build intelligent applications in the following scenarios:

(1) Intelligent Decision Support

• Real-Time Data Analysis and Insights: Utilize large models to automatically analyze enterprise data and generate actionable business insights.

• Intelligent Reporting and Forecasting: AI-powered data visualization reports, predicting trends such as sales forecasts and supply chain dynamics based on historical data.

• Automated Strategy Optimization: Employ reinforcement learning and A/B testing to continuously refine pricing, inventory management, and resource allocation strategies.

(2) Smart Marketing and Customer Intelligence

• Precision Marketing and Personalized Recommendations: Predict user needs with AI to deliver highly personalized marketing strategies, increasing conversion rates.

• Intelligent Customer Service and Chatbots: AI-driven customer service systems provide 24/7 intelligent responses based on enterprise knowledge bases, reducing labor costs.

• User Sentiment Analysis: NLP-based customer feedback analysis to detect emotions and enhance product and service experiences.

(3) Intelligent Supply Chain Management

• Demand Forecasting and Inventory Optimization: AI combines market trends and historical data to predict product demand, optimizing inventory and reducing waste.

• Logistics and Transportation Optimization: AI-driven route planning enhances logistics efficiency while minimizing costs.

• Supply Chain Risk Management: AI-powered risk analysis improves supply chain security and reliability while reducing operational costs.

(4) Enterprise Automation

• RPA (Robotic Process Automation) + AI: Automate repetitive tasks such as financial reporting, contract review, and order processing to improve efficiency.

• Intelligent Financial Analysis: AI-driven financial data analysis automatically detects anomalies and predicts cash flow risks.

(5) Data-Driven Product Innovation

• AI-Assisted Product Development: Analyze market data to predict product trends and optimize design.

• Intelligent Content Generation: AI-powered generation of high-quality marketing content, including product descriptions, ad copy, and social media promotions.

How AI and Large Models Empower Enterprise Decision-Making

(1) Data-Driven Intelligent Recommendations

• AI learns from historical data to automatically recommend optimal actions, such as refining marketing strategies or adjusting inventory.

(2) Large Models Enhancing Business Intelligence (BI)

• Traditional BI tools often require complex data modeling and SQL queries. With AI and LLMs, users can query data using natural language, for example:

  • Business and financial queries: "How did sales perform last quarter?"

  • AI-generated analysis reports: "Sales increased by 10% last quarter, with a 15% growth in North America. Key driving factors include..."

(3) Intelligent Risk Management and Prediction

• AI identifies patterns in historical data to predict risks such as credit defaults, financial fraud, and supply chain disruptions.

Business Automation and Intelligence

Enterprises can leverage AI and LLMs to construct intelligent business workflows, enabling:

• End-to-End Process Optimization: Automate the entire workflow from data collection to decision execution, such as automated approval systems and intelligent contract management.

• AI-Driven Knowledge Management: Transform internal documentation and historical insights into an intelligent knowledge base for efficient information retrieval.

How Data, AI, and Large Models Drive Enterprise Innovation

Enterprises can establish data intelligence-driven innovation capabilities through:

1. Building AI Experimentation Platforms

   • Enable collaboration among data scientists, business analysts, and engineers for AI experimentation.

2. Developing Industry-Specific Large Models

   • Train proprietary large models tailored to industry needs, such as AI assistants for finance, healthcare, and e-commerce.

3. Creating AI + Data Ecosystems

   • Share AI capabilities with external partners via open APIs to facilitate data monetization.

Challenges and Risks

(1) Data Security and Privacy Compliance

• AI models require access to vast datasets, necessitating strict compliance with regulations such as China’s Cybersecurity Law, Personal Information Protection Law, GDPR, and CCPA.

• Implement techniques like data anonymization, federated learning, and access control to mitigate privacy risks.

(2) Data Quality and Model Bias

• AI models rely on high-quality data; biased or erroneous data can lead to flawed decisions.

• Enterprises must establish data quality management frameworks and continuously refine models.

(3) Technical Complexity and Implementation Barriers

• AI and large model applications require substantial computational resources, leading to high infrastructure costs.

• Enterprises must develop AI talent or collaborate with external AI service providers to lower the technical threshold.

Conclusion

Centralized data storage lays the foundation for AI and large model applications, enabling enterprises to build competitive advantages through data-driven decision-making, business automation, and product innovation. In the AI-powered future, enterprises can achieve greater efficiency in marketing, supply chain optimization, and automated operations while exploring new data monetization and AI ecosystem opportunities. However, successful implementation requires addressing challenges such as data security, model bias, and computational costs. A well-crafted AI strategy will be essential for maximizing business value from AI technologies.

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Comprehensive Analysis of Data Assetization and Enterprise Data Asset Construction

Data has become one of the most critical assets for enterprises. Data assetization and centralized data storage are key pathways for digital transformation. Drawing on HaxiTAG’s enterprise services and practical experience in Data Intelligence solutions, this analysis explores the objectives, concepts, necessity, implementation methods and pathways, value and utility, as well as potential issues and risks associated with data assetization and centralized storage.

Objectives of Data Assetization and Centralized Data Storage

(1) Enhancing Data Value: Transforming "Burden" into "Asset"

• The core goal of data assetization is to ensure data is manageable, computable, and monetizable, enabling enterprises to leverage data for decision-making, business process optimization, and new value creation.

• Historically, data was often perceived as an operational burden due to high costs of storage, organization, and analysis, leading to inefficient data utilization. Data assetization transforms data into a core competitive advantage.

(2) Eliminating Data Silos and Achieving Unified Management

• Traditional enterprises often rely on decentralized data storage, where different departments manage data independently, leading to redundancy, inconsistent standards, and limited cross-departmental collaboration.

• Through centralized data storage, enterprises can construct a unified data view, ensuring data consistency and integrity to support precise decision-making.

(3) Strengthening Data-Driven Decision-Making

• Data assetization enables enterprises to achieve data-driven intelligence in areas such as precision marketing, intelligent recommendations, customer behavior analysis, and supply chain optimization, thereby enhancing business agility and competitiveness.

Concepts of Data Assetization and Centralized Data Storage

(1) Data as an Asset

• Data, like capital and labor, is a core production factor. Enterprises must manage data as they do financial assets, encompassing collection, cleansing, storage, analysis, operation, and monetization.

(2) Data Lifecycle Management

• The key to data assetization is lifecycle management, including:

  • Data Collection (standardized input, IoT data integration)

  • Data Governance (cleansing, standardization, compliance management)

  • Data Storage (structured and unstructured data management)

  • Data Computation (real-time processing, batch analysis)

  • Data Application (BI reporting, AI modeling, business strategy)

  • Data Monetization (internal value creation, data sharing, and trading)

(3) Centralized vs. Distributed Storage

• Centralized data storage does not imply all data resides in a single physical location but rather that:

  • Data lakes or data warehouses are used for unified management.

  • Data remains logically centralized while being physically distributed, leveraging cloud and edge computing for efficient data flow.

Necessity of Data Assetization and Centralized Data Storage

(1) Supporting Enterprise Data Governance

• Centralized storage allows enterprises to establish standardized data models, enhance governance, improve data quality, and reduce inconsistencies and redundancy.

(2) Enhancing Data Analysis and Application Capabilities

• Centralized storage provides a solid foundation for big data analytics, AI, and machine learning, accelerating enterprise intelligence.

(3) Strengthening Security and Compliance

• Dispersed data storage increases the risk of data breaches and compliance violations. Centralized storage facilitates access control, encrypted storage, and compliance auditing.

(4) Improving Data Sharing and Business Collaboration

• Centralized storage breaks down data silos between business departments and branches, enhancing efficiency. For example:

  • Marketing teams can access real-time user behavior data to improve precision marketing.

  • Supply chain management can optimize inventory in real time, reducing waste.

  • Customer service can leverage unified data views for better customer experiences.

Implementation Methods and Pathways for Data Assetization and Centralized Data Storage

(1) Establishing Data Standards and Governance Frameworks

• Define a data management architecture (e.g., Data Backbone, Data Lake, Data Warehouse).

• Set data standards (format specifications, metadata management, quality rules).

• Implement data access control mechanisms to ensure compliant data usage.

(2) Adopting Modern Data Storage Architectures

• Data Warehouses (DWH): Suitable for structured data analysis, such as business reports and financial data management (e.g., Snowflake, BigQuery).

• Data Lakes: Designed for storing structured, semi-structured, and unstructured data, supporting machine learning and big data analytics (e.g., Amazon S3, Databricks).

• Hybrid Storage Architectures: Combining data lakes and warehouses for both real-time processing and historical data analysis.

(3) Data Collection and Integration

• Utilize ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) tools for efficient data pipelines.

• Integrate multiple data sources, including CRM, ERP, IoT, and third-party data, to form a comprehensive data asset.

(4) Data-Driven Applications

• Precision Marketing: Personalized recommendations and ad targeting based on customer profiles.

• Intelligent Operations: IoT-driven equipment monitoring to enhance maintenance efficiency.

• Supply Chain Optimization: Real-time inventory and order tracking for procurement decision-making.

Value and Utility of Data Assetization and Centralized Data Storage

(1) Improving Data Utilization Efficiency

• Standardization and sharing reduce redundant storage and computations, optimizing data usage efficiency.

(2) Enhancing Enterprise Data Insights

• Advanced analytics and machine learning reveal hidden patterns, such as:

  • Customer churn prediction

  • Optimized product pricing

  • Market strategy adjustments

(3) Boosting Operational Efficiency and Automation

• Automated data workflows and intelligent analytics reduce manual data handling and improve operational efficiency.

(4) Enabling Data Monetization

• Enterprises can monetize data through data sharing, open APIs, and data trading, such as:

  • Banks leveraging user data for optimized financial product recommendations.

  • Retailers enhancing supply chain efficiency through data partnerships.

Enterprise Intelligence: The Integration of Data Assetization, Centralized Storage, and AI

Data assetization and centralized storage serve as the foundation for enterprise digitalization, eliminating data silos and enabling data-driven decision-making. By establishing data lakes and warehouses, enterprises can achieve efficient data management, analysis, and sharing, paving the way for intelligent applications.

With the integration of AI and Large Language Models (LLM), enterprises can unlock deeper data insights and drive business innovation. AI facilitates precision marketing, intelligent customer service, supply chain optimization, and financial analysis, enhancing automation and operational efficiency. LLMs, combined with real-time data, elevate decision-making capabilities, supporting automated BI analytics, intelligent risk control, and personalized recommendations.

However, enterprises must address data security, compliance, data quality, and technological costs to ensure AI applications are reliable. The future lies in building an ecosystem where AI and data converge, enabling intelligent decision-making, automated operations, and data-driven innovation, securing a competitive edge in the intelligent era.

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Unlocking the Full Potential of Data: HaxiTAG Data Intelligence Drives Enterprise Value Transformation

In an era where data-driven decision-making reigns supreme, enterprises are increasingly seeking more efficient ways to extract valuable insights from their vast data assets. According to IDC forecasts, by 2024, unstructured data—such as PDFs, emails, and large datasets—will account for 93% of all enterprise data. This trend underscores the critical importance of data management and intelligence, while the advent of Generative AI further accelerates the unlocking of data’s inherent value.

However, the true potential of data is often constrained by challenges such as data fragmentation, inconsistent quality, data silos, and inadequate governance. As Ritika Gunnar, General Manager of Data and AI at IBM, aptly stated: “Enterprises must first untangle the chaos of data.” To address these challenges, leading technology companies like Salesforce and IBM are intensifying efforts to develop advanced data intelligence solutions, empowering enterprises to achieve transformative, data-driven outcomes.

Data Intelligence: From Chaos to Value

Data intelligence serves as the foundation for modern enterprises to effectively manage and leverage data. It encompasses the entire process—from data cataloging, quality assurance, governance, and lineage tracking to data sharing. By establishing a unified intelligent data framework, enterprises can unlock the following benefits:

• Efficient Data Discovery and Organization: Automated cataloging and classification enable enterprises to quickly locate, understand, and utilize data.
• Improved Data Quality: Intelligent cleansing and validation mechanisms ensure data accuracy and consistency.
• Robust Data Governance and Compliance: Transparent lineage tracking and access controls ensure compliant data usage.
• Enhanced Data Sharing and Collaboration: Breaking down data silos fosters seamless cross-departmental collaboration, strengthening the data value chain.

HaxiTAG Data Intelligence Solution

As a dedicated innovator in the field of data intelligence, HaxiTAG is committed to building intelligent data pipelines that transform raw data into strategic assets capable of guiding business decisions. HaxiTAG Data Intelligence is a comprehensive suite of smart data tools focused on data management, operations, and standardization, designed to handle unstructured and semi-structured data with enterprise-grade governance and optimization.

What sets HaxiTAG apart is its seamless integration with AI, Large Language Models (LLMs), and business processes through a series of intelligent adapters. These adapters enable flexible, on-demand connections between data, AI capabilities, and business workflows, ensuring enterprises can fully harness their data potential in real time.

Key Advantages

• Full Lifecycle Data Management: Encompasses the entire closed-loop process of data collection, storage, processing, analysis, and visualization.
• Intelligent Processing of Unstructured Data: Offers advanced capabilities for parsing, structural transformation, and knowledge extraction from complex data types (e.g., PDFs and emails).
• Enhanced Search and Insight Generation: Leverages intelligent indexing and semantic analysis technologies for precise data retrieval and deep analytical insights.
• Scalable Enterprise-Grade Architecture: Compatible with mainstream cloud platforms and on-premises deployments, supporting high-concurrency and high-availability data computing needs.
• AI and LLM Integration via Adapters: Seamlessly connects data with AI and LLM functionalities to automate insights, enhance decision-making, and streamline business processes.

As data intelligence technology continues to evolve, enterprises stand at the forefront of overcoming long-standing barriers to data utilization, achieving a transformative leap from mere data accumulation to intelligence-driven value creation. HaxiTAG empowers enterprises with its cutting-edge data intelligence tools, helping partners unlock the full potential of their data and transform data assets into a true competitive advantage.

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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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