The Evolution of Artificial Intelligence and Its Impact on the Business World

In recent years, the rapid development of artificial intelligence (AI) technology has profoundly influenced business operations, strategic planning, and employee roles. From 2024 to 2025, the application and implementation of AI have undergone significant transformations, primarily in the following areas:

1. Enhanced Awareness and Cognition: Business leaders have deepened their understanding of AI, gradually recognizing its potential to drive business transformation.

2. Breakthroughs in Technological Maturity: AI models have evolved from general language processing to highly efficient tools tailored for specific business tasks. AI agents have been introduced, and the capabilities for generating images, videos, and virtual avatars have significantly improved.

3. Optimized Infrastructure: Major cloud platforms now feature built-in AI functionalities, enabling businesses to leverage AI capabilities more conveniently without requiring large IT teams.

Key Transformations of AI in Business

1. Strategic Impacts

Businesses must consider the following core questions:

• Shifts in Industry Dynamics: The widespread adoption of AI will influence customer demands and willingness to pay, potentially replacing certain traditional services while creating new business opportunities.

• Exploration of Value-Added Services: AI enables businesses to offer services that were previously too costly or complex, enhancing market competitiveness.

• Market Expansion and Diversification: AI facilitates entry into new markets by eliminating language and geographical barriers.

2. Enhanced Operational Intelligence

AI contributes to daily business operations in several ways:

• Efficiency Improvement: Reduces human effort in repetitive, low-value tasks such as data organization and report generation.

• Optimized Customer Experience: AI applications, including intelligent customer service and personalized recommendation systems, enhance customer satisfaction while reducing operational costs.

• Enhanced Decision-Making: AI-driven data analytics provide precise market insights and forecasts, assisting businesses in formulating optimal strategies.

• Intelligent Operations Management: AI automates supply chain optimization, inventory management, and marketing strategies, improving overall business efficiency.

3. Data Security and Privacy Protection

As AI becomes more deeply integrated into business operations, data security emerges as a critical challenge:

• Compliance with Data Privacy Regulations: Businesses must ensure adherence to global regulations such as GDPR and CCPA when utilizing AI.

• AI Model Security: Protecting AI systems from malicious attacks and data tampering is essential for maintaining business stability.

• Privacy-Preserving Computing Technologies: Techniques like federated learning and differential privacy enable AI-driven analytics while safeguarding data security.

4. Workforce Transformation

With the expansion of AI-driven automation, employee roles are evolving in the following ways:

• Focus on Strategic Planning and Innovation: AI alleviates repetitive work, allowing employees to concentrate on business optimization and market expansion.

• Solving Complex Problems: While AI provides data-driven insights, ultimate decision-making remains a human responsibility.

• Upgraded Human-AI Collaboration Models: Employees must enhance their AI application skills to leverage AI-assisted decision-making for improved efficiency.

5. Broad Adoption of AI Tools

Businesses are increasingly relying on AI-powered tools to enhance efficiency and streamline workflows:

• Intelligent Document Processing: Automated translation, text summarization, and semantic analysis tools improve information management.

• AI-Driven Enterprise Search: Accelerates internal knowledge retrieval, enhancing team collaboration.

• Automated IT Operations: AI-powered monitoring systems predict equipment failures, reducing maintenance costs.

6. HashTag EiKM's Innovative Practices

HashTag EiKM focuses on enterprise-level intelligent information management and has achieved breakthroughs in AI application, including:

• Intelligent Knowledge Management: AI-driven automatic classification, semantic search, and intelligent recommendations enhance knowledge circulation within enterprises.

• Business Process Automation: By integrating AI agents, EiKM optimizes data processing, report generation, and task management, reducing operational costs.

• Industry-Specific AI Solutions: Tailored AI-driven solutions for manufacturing, finance, and healthcare industries help businesses enhance their competitive edge.

• Robust Data Security Framework: AI-powered access control and compliance auditing solutions ensure enterprise data security.

Future Challenges and Considerations

• Employment and Skill Transition: While AI may reduce traditional job roles, it will also create new career opportunities. Businesses must help employees adapt to technological advancements.

• Ethical and Regulatory Issues: AI applications must comply with relevant regulations to ensure data security and privacy protection.

• Long-Term Competitiveness: Establishing internal AI expertise is crucial for businesses to maintain a competitive edge in the AI era.

Conclusion

AI is reshaping the business landscape, and enterprises must proactively adapt to changes in strategy, operations, data security, and talent development. HashTag EiKM will continue to explore the deep integration of AI in information management, providing intelligent, efficient, and secure solutions for businesses. By strategically deploying AI and fostering an innovation-driven mindset, businesses can fully capitalize on AI’s opportunities, enhance overall competitiveness, and build a sustainable, intelligent business model.

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Leveraging Data, AI, and Large Models to Build Enterprise Intelligent Decision-Making and Applications

On the foundation of data assetization and centralized storage, enterprises can further integrate Artificial Intelligence (AI) and Large Language Models (LLM) to achieve intelligent decision-making, automated business processes, and data-driven innovation—thus establishing a unique competitive advantage in the era of intelligence. This article explores how data integrates with AI and large models, core application scenarios, intelligent decision-making methods, business automation, innovation pathways, and potential challenges in depth.

Integrating Data, AI, and Large Models

Once data is centrally stored, enterprises can leverage AI to conduct deep mining, analysis, and predictions, supporting the development of intelligent applications. The key approaches include:

1. Intelligent Data Analysis

• Using machine learning (ML) and deep learning (DL) models to extract data value, enhance predictive and decision-making capabilities.
• Applying large models (such as GPT, BERT, and Llama) in Natural Language Processing (NLP) to enable applications like intelligent customer service, smart search, and knowledge management.

2. Enhancing Large Models with Data

• Building enterprise-specific knowledge bases: Fine-tuning large models with historical enterprise data and industry insights to incorporate domain-specific expertise.
• Real-time data integration: Merging large models with real-time data (such as market trends, user behavior, and supply chain data) to enhance predictive capabilities.

3. Developing Data-Driven Intelligent Applications

• Transforming structured and unstructured data (text, images, voice, video) into actionable insights through AI models to support enterprise-level intelligent applications.

Core Application Scenarios of AI and Large Models

1. Intelligent Decision Support

• Real-time Data Analysis & Insights: AI models automatically analyze business data and generate actionable business decisions.
• Automated Reports & Forecasting: AI generates data visualization reports and forecasts future trends, such as sales projections and supply chain fluctuations.
• Automated Strategy Optimization: AI continuously refines pricing strategies, inventory management, and resource allocation through reinforcement learning and A/B testing.

2. Smart Marketing & Customer Intelligence

• Precision Marketing & Personalized Recommendations: AI predicts user needs, creating highly personalized marketing strategies to enhance conversion rates.
• AI-Powered Customer Service: Large model-driven chatbots and virtual assistants provide 24/7 intelligent Q&A based on enterprise knowledge bases, reducing manual workload.
• Sentiment Analysis: NLP technology analyzes customer feedback, identifying emotions to improve product and service experiences.

3. Intelligent Supply Chain Management

• Demand Forecasting & Inventory Optimization: AI integrates market trends and historical data to predict product demand, reducing waste.
• Smart Logistics & Transportation Scheduling: AI optimizes delivery routes to enhance logistics efficiency and reduce costs.
• Supply Chain Risk Management: AI assists in background checks, risk monitoring, and data analysis, improving supply chain security and resilience.

4. Enterprise Process Automation

• AI + RPA (Robotic Process Automation): AI automates repetitive tasks such as financial reporting, contract review, and order processing, enhancing business automation.
• Smart Financial Analytics: AI detects abnormal transactions and predicts cash flow risks through financial data analysis.

5. Data-Driven Product Innovation

• AI-Assisted Product Development: AI analyzes market data to forecast product trends and optimize product design.
• Intelligent Content Generation: AI generates high-quality marketing content, such as product descriptions, advertising copy, and social media content.

How AI and Large Models Enable Intelligent Decision-Making

1. Data-Driven Intelligent Recommendations

• AI learns from historical data to automatically suggest optimal actions to decision-makers, such as marketing strategy adjustments and inventory optimization.

2. Enhancing Business Intelligence (BI) with Large Models

• Traditional BI tools require complex data modeling and SQL queries. With AI, users can query data using natural language, such as:
  • Business and Financial Queries: "What was the sales performance last quarter?"
  • AI-Generated Reports: "Sales grew by 10% last quarter, with North America experiencing a 15% increase. The key drivers were..."

3. AI-Driven Risk Management & Forecasting

• AI detects patterns in historical data to predict credit risk, financial fraud, and supply chain disruptions.

Business Automation & Intelligence

AI and large models help enterprises automate business processes and optimize decision-making:

• End-to-End Intelligent Process Optimization: Automating everything from data collection to execution, such as automated approval systems and smart contract management.
• AI-Driven Knowledge Management: Transforming enterprise documents and historical knowledge into intelligent knowledge bases, allowing employees to access critical information efficiently.

How AI, Data, and Large Models Drive Enterprise Innovation

1. Establishing AI Experimentation Platforms

• Creating collaborative AI labs where data scientists, business analysts, and engineers can develop and test AI solutions.

2. Industry-Specific Large Models

• Training customized AI models tailored to specific industries (e.g., finance, healthcare, and e-commerce).

3. Building AI + Data Ecosystems

• Developing open APIs to share AI capabilities with external partners, enabling data commercialization.

Challenges and Risks

1. Data Security & Privacy Compliance

• AI models require access to large datasets, necessitating compliance with data protection regulations such as GDPR, CCPA, and China’s Cybersecurity Law.
• Implementing data masking, federated learning, and access controls to minimize privacy risks.

2. Data Quality & Model Bias

• AI models rely on high-quality data; biased or erroneous data may lead to incorrect decisions.
• Establishing data governance frameworks and continuously refining AI models is essential.

3. Technical Complexity & Deployment Challenges

• AI and large model applications demand significant computational power, posing high cost barriers.
• Enterprises must cultivate AI talent or collaborate with AI service providers to lower technical barriers.

Conclusion

Centralized data storage lays the foundation for AI and large model applications, allowing enterprises to leverage data-driven intelligent decision-making, business automation, and product innovation to gain a competitive edge. With AI enablement, enterprises can achieve efficient smart marketing, supply chain optimization, and automated operations, while also exploring data monetization and AI ecosystem development. However, businesses must carefully navigate challenges such as data security, model bias, and infrastructure costs, formulating a well-defined AI strategy to maximize the commercial value of AI.

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Challenges and Future of AI Search: Reliability Issues in Information Retrieval with LLM-Generated Search

 

Case Overview and Innovations

In recent years, AI-powered search (GenAI search) has emerged as a major innovation in information retrieval. Large language models (LLMs) integrate data and knowledge to facilitate Q&A and decision-making, representing a significant upgrade for search engines. However, challenges such as hallucinations and controllability modulation hinder their widespread reliable application. Tech giants like Google are actively exploring generative AI search to enhance competitiveness against products from OpenAI, Perplexity, and others.

A study conducted by the Tow Center for Digital Journalism at Columbia University analyzed the accuracy and consistency of eight GenAI search tools in news information retrieval. The results revealed that current systems still face severe issues in source citation, accurate responses, and the avoidance of erroneous content generation.

Application Scenarios and Performance Analysis

GenAI Search Application Scenarios

1. News Information Retrieval: Users seek AI-powered search tools to quickly access news reports, original article links, and key insights.

2. Decision Support: Businesses and individuals utilize LLMs for market research, industry trend analysis, and forecasting.

3. Knowledge-Based Q&A Systems: AI-driven solutions support specialized domains such as medicine, law, and engineering by providing intelligent responses based on extensive training data.

4. Customized general artificial intelligence experience: Improve the reliability and security of any generated artificial intelligence application by providing the most relevant paragraphs from unified enterprise content sources.

5. Chatbot & Virtual Assistant: Improve the relevance of your chatbot and virtual assistant answers, and make your user experience personalized and content-rich dialogue.

6. Internal knowledge management: Empower employees through personalized and accurate answers based on enterprise knowledge, reduce search time and improve productivity.

7. Customer-oriented support and case transfer: Provide accurate self-help answers based on support knowledge to minimize upgrades, reduce support costs and improve customer satisfaction.

Performance and Existing Challenges

• Inability to Reject Incorrect Answers: Research indicates that AI chatbots tend to provide speculative or incorrect responses rather than outright refusing to answer.

• Fabricated Citations and Invalid Links: LLM-generated URLs may be non-existent or even fabricated, making it difficult for users to verify information authenticity.

• Unstable Accuracy: According to the Tow Center's study, a test involving 1,600 news-based queries found high error rates. For instance, Perplexity had an error rate of 37%, while Grok 3's error rate reached a staggering 94%.

• Lack of Content Licensing Optimization: Even with licensing agreements between AI providers and news organizations, the issue of inaccurate AI-generated information persists.

The Future of AI Search: Enhancing Reliability and Intelligence

To address the challenges LLMs face in information retrieval, AI search reliability can be improved through the following approaches:

1. Enhancing Fact-Checking and Source Tracing Mechanisms: Leveraging knowledge graphs and trusted databases to improve AI search capabilities in accurately retrieving information from credible sources.

2. Introducing Explainability and Refusal Mechanisms: Implementing transparent models that enable LLMs to reject uncertain queries rather than generating misleading responses.

3. Optimizing Generative Search Citation Management: Refining LLM strategies for URL and citation generation to prevent invalid links and fabricated content, improving traceability.

4. Integrating Traditional Search Engine Strengths: Combining GenAI search with traditional index-based search to harness LLMs' natural language processing advantages while maintaining the precision of conventional search methods.

5. Domain-Specific Model Training: Fine-tuning AI models for specialized industries such as healthcare, law, and finance to mitigate hallucination issues and enhance application value in professional settings.

6. Improving Enterprise-Grade Reliability: In business environments, GenAI search must meet higher reliability and confidence thresholds. Following best practices from HaxiTAG, enterprises can adopt private deployment strategies, integrating domain-specific knowledge bases and trusted data sources to enhance AI search precision and controllability. Additionally, establishing AI evaluation and monitoring mechanisms ensures continuous system optimization and the timely correction of misinformation.

Conclusion

While GenAI search enhances information retrieval efficiency, it also exposes issues such as hallucinations, citation errors, and lack of controllability. By optimizing data source management, strengthening refusal mechanisms, integrating traditional search technologies, and implementing domain-specific training, AI search can significantly improve in reliability and intelligence. Moving forward, AI search development should focus on "trustworthiness, traceability, and precision" to achieve truly efficient and secure intelligent information retrieval.

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HaxiTAG Deck: The Enterprise-Grade AI Workbench Driving Intelligent Transformation

HaxiTAG Deck is an innovative enterprise-grade AI workbench built on the HaxiTAG YueLi Knowledge Computation Engine and 21 leading open-source large language models. It provides a comprehensive, efficient, and secure development environment for AI applications, meeting diverse business needs such as creative content generation, intelligent search, intelligence analysis, and automation. Below is an in-depth analysis of its core features, advantages, and application scenarios.

Core Features

1. Integrated Functionality
   A key highlight of HaxiTAG Deck is its highly integrated design. The platform combines LLMs, search engines, automation tools, image generation, video generation algorithms, and data processing pipelines into an end-to-end AI application platform. This integration reduces the complexity of AI application development, enabling users to complete various tasks seamlessly without switching between tools.

2. Data Security
   Addressing enterprise concerns over data security, HaxiTAG Deck incorporates strict privacy and security standards. It supports private and isolated environments to ensure sensitive data is processed and stored securely. Additionally, the platform complies with industry-specific regulatory requirements, ensuring operational compliance.

3. User-Friendly Design
   Designed for employees without technical backgrounds, HaxiTAG Deck features an intuitive interface for creating and customizing AI agents. The platform simplifies complex AI technologies, empowering non-technical staff to harness AI effectively and improve productivity.

4. Performance and Scalability
   Leveraging advanced generative AI technologies, HaxiTAG Deck delivers tailored solutions based on private enterprise data. It supports diverse business scenarios, including chatbots and platform-based agents. The platform's AI Agent Builder tool has proven effective in market research, product development, financial management, HR, and customer support.

5. Seamless Integration
   HaxiTAG Deck integrates seamlessly with existing tools and internal applications, supporting various data formats such as images, PPTs, PDFs, and spreadsheets. Its data connectivity, enhanced by open interfaces like the YueLi-KGM-adapter, ensures high flexibility and scalability, particularly in dynamic scheduling and knowledge graph applications.

Advantages and Applications

1. Ease of Use and Efficiency
   HaxiTAG Deck significantly lowers the barrier to AI adoption, enabling rapid AI agent creation and customization. This accelerates automation and intelligent transformation across various business domains, boosting employee productivity.

2. Smart Industry Solutions
   The platform has demonstrated strong customization capabilities in key industries. For example, in ESG assessment and reporting, it provides precise data analysis and reporting tools. In banking and anti-money laundering investigations, its intelligent analysis tools help enterprises address compliance requirements and mitigate market risks.

3. Tailored Solutions
   Beyond standardized features, HaxiTAG Deck offers highly customizable solutions based on industry-specific needs. For instance, in finance, it can be configured to meet diverse regulatory demands, ensuring full compliance with industry standards and enterprise requirements.

Conclusion

HaxiTAG Deck is a robust and user-friendly enterprise-grade AI workbench that integrates advanced AI technologies and functionalities into a secure, reliable, and efficient platform. With applications in intelligent search, creative content generation, intelligence analysis, and more, it has delivered significant value across industries. As it continues to evolve and expand, HaxiTAG Deck is poised to play a pivotal role in driving digital transformation and intelligent innovation in enterprises worldwide.

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From Raw Data to Real Profits: A Guide to Building a Thriving Data Business

In today's digital age, data has become one of the most valuable assets for businesses. However, merely possessing large amounts of raw data is not enough to create value - the key lies in effectively transforming this data into tangible business profits. This article will unveil the path from raw data to actual profits, providing comprehensive guidance for building a prosperous data business.

The Rise and Opportunities of Data Businesses

Nearly two centuries ago, during the rapid expansion of American commerce, Lewis Tappan and John M. Bradstreet pioneered the concept of commercial credit reporting. In an era of limited information, they established firms dedicated to collecting, analyzing, and selling business data, laying the foundation for modern credit bureaus and risk assessment practices. Their innovative approach filled a critical gap in the burgeoning economy, enabling more informed lending and investment decisions.

Lewis Tappan and John M. Bradstreet demonstrated the potential of transforming data into profitable products. They established companies dedicated to collecting, analyzing, and selling data, filling a critical gap in the business world that urgently needed reliable credit assessment methods. Today, with the rapid advancement of technology, the opportunities for data businesses are even more extensive. According to McKinsey's latest survey, approximately 40% of business leaders expect to create data, analytics, and AI-based businesses within the next five years - the highest proportion among all new business categories.

Why is Now the Best Time to Build a Data Business?

Technological advancements have created favorable conditions for the rapid and cost-effective development of data businesses:

1. Enhanced Data Management Efficiency: Advanced data tools and technologies enable businesses to process, manage, and access real-time data more efficiently.
2. The Rise of Generative AI: Generative AI has significantly reduced the cost of processing unstructured data (such as text, images, and videos), making it easier to analyze and utilize.
3. The Proliferation of the Internet of Things (IoT): The decreasing cost of IoT technology allows businesses to collect and access real-world data faster and more economically.
4. Widespread Use of Internal Data Products: Leading enterprises increasingly treat data as internal products, laying the foundation for data monetization.

Evaluating Opportunities and Formulating the Right Strategy

The foundation of building a data business lies in having unique data of sufficient scale or possessing a distinctive method for processing data and extracting commercial value from it. Businesses can consider the following three broad strategies:

1. Creating Industry Standards: As Moody's, Standard & Poor's, and Fitch have done in the credit rating field. This strategy typically begins with large-scale aggregation of unique data and may eventually become an industry standard as network effects expand.
2. Leveraging Insights from Active User Groups: Transforming data collected from active user groups into valuable insights for advertisers, suppliers, partners, and users.
3. Converting Organizational Knowledge into Products: For example, evolving tools that solve internal business problems into profitable external products.

Key Considerations for Building a Sustainable Data Business

1. Defining a Strong Customer Value Proposition:
   • Consider the type of "intelligence" provided by data products (from raw data to information, knowledge, and wisdom)
   • Choose an appropriate product delivery model (data platform, insight platform, or intelligent application)
2. Adjusting the Operating Model:
   • Incentivize growth potential rather than short-term profits
   • Adopt new sales and pricing models
   • Invest in specialized technical skills
3. Modernizing Data Technologies:
   • Establish a robust data infrastructure
   • Invest in core and advanced technical capabilities based on data types and delivery methods
4. Managing Data Security, Privacy, and Intellectual Property:
   • Clarify data rights
   • Develop consistent data privacy principles
   • Pay attention to and comply with local laws
   • Prioritize data governance and security

Building a data business requires not only unique datasets but also the right capabilities to scale products. First movers often gain significant advantages in capturing untapped market opportunities. However, successful data businesses can not only create scalable and profitable models but also potentially establish lasting brands. By following the guidelines provided in this article, businesses can better navigate the complexities of data businesses, transform raw data into actual profits, and secure advantageous positions in the digital economy era.

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