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.

Related Topic

Unlocking the Potential of RAG: A Novel Approach to Enhance Language Model's Output Quality - HaxiTAG
Enterprise-Level LLMs and GenAI Application Development: Fine-Tuning vs. RAG Approach - HaxiTAG
Innovative Application and Performance Analysis of RAG Technology in Addressing Large Model Challenges - HaxiTAG
Revolutionizing AI with RAG and Fine-Tuning: A Comprehensive Analysis - HaxiTAG
The Synergy of RAG and Fine-tuning: A New Paradigm in Large Language Model Applications - HaxiTAG
How to Build a Powerful QA System Using Retrieval-Augmented Generation (RAG) Techniques - HaxiTAG
The Path to Enterprise Application Reform: New Value and Challenges Brought by LLM and GenAI - HaxiTAG
LLM and GenAI: The New Engines for Enterprise Application Software System Innovation - HaxiTAG
Exploring Information Retrieval Systems in the Era of LLMs: Complexity, Innovation, and Opportunities - HaxiTAG
AI Search Engines: A Professional Analysis for RAG Applications and AI Agents - GenAI USECASE

Read more →

2025 AI Security Analysis and Insights

 The Evolution of AI Security Trends

With the widespread adoption of artificial intelligence, enterprises are facing increasingly prominent security risks, particularly those associated with DeepSeek. Research conducted by the HaxiTAG team indicates that the speed of AI adoption continues to accelerate, largely driven by advancements in technologies such as DeepSeek R1. While managed AI services are favored for their ease of deployment, the growing demand for data privacy and lifecycle control has led to a significant rise in enterprises opting for self-hosted AI models.

Key Security Challenges in Enterprise AI Adoption

Enterprises must focus on three critical areas when implementing AI solutions:

1. Data Security and Control

• As the core asset for AI training, data integrity and privacy are paramount.
• Organizations should implement stringent data encryption, access control, and compliance checks before AI deployment to prevent data breaches and unauthorized usage.

2. Proactive AI Security Governance

• Enterprises should establish AI asset discovery and cataloging systems to ensure that AI models, data, and their usage can be effectively tracked and monitored.
• Key governance measures include data provenance tracking, transparent reporting mechanisms, and clear accountability structures for AI usage.

3. AI Runtime Security

• The runtime phase presents a crucial opportunity for AI protection. While traditional cybersecurity measures can mitigate some risks, significant vulnerabilities remain in addressing AI-specific security threats.
• Threats such as model poisoning, adversarial attacks, and data exfiltration require specialized security architectures to counteract.

Current Market Landscape and Security Solutions

HaxiTAG's research categorizes existing AI security solutions into two primary groups:

1. Ensuring Secure AI Usage for Employees and Agents

• This category focuses on internal AI applications within enterprises, addressing risks related to data leakage, misuse, and regulatory compliance.
• Representative solutions include AI Identity and Access Management (AI IAM), AI usage auditing, and secure AI sandbox testing.

2. Safeguarding AI Product and Model Lifecycle Security

• These solutions prioritize AI supply chain security, as well as protection mechanisms for the training and inference phases of AI models.
• Core technologies in this domain include privacy-preserving computing, secure federated learning, model watermarking, and AI threat detection.

Industry Insights and Future Trends

1. AI Security Will Become a Core Pillar of Enterprise Digital Transformation

• In the future, AI adoption strategies will be deeply integrated with security frameworks, with Zero Trust AI security architectures likely to emerge as industry standards.

2. Acceleration of Autonomous and Controllable AI Ecosystems

• Rising concerns over data sovereignty and AI model autonomy will drive more enterprises toward privatized AI solutions and stricter data security management frameworks.

3. Growing Demand for Generative AI Security Governance

• As AIGC (AI-Generated Content) becomes more prevalent, addressing misinformation, bias, and misuse in AI-generated content will be a critical aspect of AI security governance.

AI security has become a fundamental pillar of enterprise AI adoption. From data security to runtime protection, enterprises must establish comprehensive AI security governance frameworks to ensure the integrity, transparency, and compliance of AI assets. HaxiTAG’s research further highlights the emergence of specialized AI security solutions, indicating that future industry developments will focus on closed-loop AI security management, enabling AI to create greater value within a trusted and secure environment.

Related Topic

How to Effectively Utilize Generative AI and Large-Scale Language Models from Scratch: A Practical Guide and Strategies - GenAI USECASE
Leveraging Large Language Models (LLMs) and Generative AI (GenAI) Technologies in Industrial Applications: Overcoming Three Key Challenges - HaxiTAG
Identifying the True Competitive Advantage of Generative AI Co-Pilots - GenAI USECASE
Leveraging LLM and GenAI: The Art and Science of Rapidly Building Corporate Brands - GenAI USECASE
Optimizing Supplier Evaluation Processes with LLMs: Enhancing Decision-Making through Comprehensive Supplier Comparison Reports - GenAI USECASE
LLM and GenAI: The Product Manager's Innovation Companion - Success Stories and Application Techniques from Spotify to Slack - HaxiTAG
Using LLM and GenAI to Assist Product Managers in Formulating Growth Strategies - GenAI USECASE
Utilizing AI to Construct and Manage Affiliate Marketing Strategies: Applications of LLM and GenAI - GenAI USECASE
LLM and Generative AI-Driven Application Framework: Value Creation and Development Opportunities for Enterprise Partners - HaxiTAG
Leveraging LLM and GenAI Technologies to Establish Intelligent Enterprise Data Assets - HaxiTAG

Read more →

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.

Related Topic

Generative AI: Leading the Disruptive Force of the Future
HaxiTAG EiKM: The Revolutionary Platform for Enterprise Intelligent Knowledge Management and Search
From Technology to Value: The Innovative Journey of HaxiTAG Studio AI
HaxiTAG: Enhancing Enterprise Productivity with Intelligent Knowledge Management Solutions
HaxiTAG Studio: AI-Driven Future Prediction Tool
A Case Study:Innovation and Optimization of AI in Training Workflows
HaxiTAG Studio: The Intelligent Solution Revolutionizing Enterprise Automation
Exploring How People Use Generative AI and Its Applications
HaxiTAG Studio: Empowering SMEs with Industry-Specific AI Solutions
Maximizing Productivity and Insight with HaxiTAG EIKM System

Read more →

No Data Strategy, No AI Strategy

In today's rapidly evolving technological era, Artificial Intelligence (AI) has become a crucial force driving business innovation and social progress. However, many often overlook a key fact: without high-quality data, an AI strategy is impossible to formulate. Training AI models requires vast amounts of high-quality data, which is not just a technical issue but a strategic one. This article will explore the importance of data strategy in AI development and showcase some successful case studies.

Drawing from over 15 years of big data experience, the HaxiTAG team shares powerful insights on data asset value mining and technical applications, based on their work with banking and financial clients involving hundreds of billions in data volume and analyzing billions of daily transaction records.

The Importance of Data

Data is the "fuel" for AI. Whether training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Established companies possess large amounts of customer data, providing them with a unique advantage. They can leverage this data to train models and fine-tune algorithms, as long as they ensure data confidentiality and privacy.

For instance, Google's success in AI video largely depends on its vast YouTube library. Similarly, GitHub's advantage with Copilot stems from training AI on their extensive code base, which includes detailed human annotations. Companies with rich data resources can quickly achieve technological breakthroughs, while newcomers face significant challenges.

Customer Interaction and Data Strategy

AI strategies are built on the foundation of customer interactions. By deeply understanding customer behavior and needs, businesses can continuously optimize their data collection and utilization strategies, thereby enhancing AI model performance. Existing enterprises possess vast amounts of customer interaction data, providing them with valuable resources.

For example, Amazon can precisely recommend products by analyzing customer shopping behaviors and preferences, improving customer satisfaction and sales. Through continuous collection and analysis of customer data, Amazon has been able to constantly optimize its AI algorithms, maintaining its leading position in the e-commerce sector.

Challenges in Data Acquisition

Despite ongoing advancements in AI technology, the difficulty of obtaining large amounts of high-quality data is increasing. Companies that possess data have recognized its value and are often unwilling to share. This makes data a scarce resource, especially for new entrants and small businesses.

To address this challenge, enterprises need to formulate clear data strategies, including aspects such as data collection, management, analysis, and protection. This not only helps improve AI model performance but also ensures data security and privacy.

Without a data strategy, there can be no AI strategy. Data is the cornerstone of AI development, and high-quality data is key. By formulating effective data strategies, enterprises can fully utilize their data resources to drive continuous progress in AI technology. In this data-driven era, having a clear data strategy is not only crucial for business success but also a necessary condition for maintaining competitiveness.

Through this discussion, we hope to help readers better understand the importance of data strategy in AI development and inspire enterprises to focus on data collection, management, and utilization when formulating AI strategies. Only in this way can they maintain an undefeated position in fierce market competition.

Topic Related

Deep Application and Optimization of AI in Customer Journeys
How AI Can Improve Your Targeted Decision-Making
5 Ways HaxiTAG AI Drives Enterprise Digital Intelligence Transformation: From Data to Insight
How Artificial Intelligence is Revolutionizing Demand Generation for Marketers in Four Key Ways
HaxiTAG: Trusted Solutions for LLM and GenAI Applications
HaxiTAG Assists Businesses in Choosing the Perfect AI Market Research Tools
HaxiTAG Studio: AI-Driven Future Prediction Tool

Read more →