HaxiTAG Deck: The Core Value and Implementation Pathway of Enterprise-Level LLM GenAI Applications

In the rapidly evolving landscape of generative AI (GenAI) and large language model (LLM) applications, enterprises face a critical challenge: how to deploy LLM applications efficiently and securely as part of their digital transformation strategy. HaxiTAG Deck provides a comprehensive architecture paradigm and supporting technical solutions for LLM and GenAI applications, aiming to address the key pain points in enterprise-level LLM development and expansion.

By integrating data pipelines, dynamic model routing, strategic and cost balancing, modular function design, centralized data processing and security governance, flexible tech stack adaptation, and plugin-based application extension, HaxiTAG Deck ensures that organizations can overcome the inherent complexity of LLM deployment while maximizing business value.

This paper explores HaxiTAG Deck from three dimensions: technological challenges, architectural design, and practical value, incorporating real-world use cases to assess its profound impact on enterprise AI strategies.

Challenges of Enterprise-Level LLM Applications and HaxiTAG Deck’s Response

Enterprises face three fundamental contradictions when deploying LLM applications:

1. Fragmented technologies vs. unified governance needs
2. Agile development vs. compliance risks
3. Cost control vs. performance optimization

For example, the diversity of LLM providers (such as OpenAI, Anthropic, and localized models) leads to a fragmented technology stack. Additionally, business scenarios have different requirements for model performance, cost, and latency, further increasing complexity.

HaxiTAG Deck LLM Adapter: The Philosophy of Decoupling for Flexibility and Control

1. Separation of the Service Layer and Application Layer

   • The HaxiTAG Deck LLM Adapter abstracts underlying LLM services through a unified API gateway, shielding application developers from the interface differences between providers.
   • Developers can seamlessly switch between models (e.g., GPT-4, Claude 3, DeepSeek API, Doubao API, or self-hosted LLM inference services) without being locked into a single vendor.

2. Dynamic Cost-Performance Optimization

   • Through centralized monitoring (e.g., HaxiTAG Deck LLM Adapter Usage Module), enterprises can quantify inference costs, response times, and output quality across different models.
   • Dynamic scheduling strategies allow prioritization based on business needs—e.g., customer service may use cost-efficient models, while legal contract analysis requires high-precision models.

3. Built-in Security and Compliance Mechanisms

   • Integrated PII detection and toxicity filtering ensure compliance with global regulations such as China’s Personal Information Protection Law (PIPL), GDPR, and the EU AI Act.
   • Centralized API key and access management mitigate data leakage risks.

HaxiTAG Deck LLM Adapter: Architectural Innovations and Key Components

Function and Object Repository

• Provides pre-built LLM function modules (e.g., text generation, entity recognition, image processing, multimodal reasoning, instruction transformation, and context builder engines).
• Reduces repetitive development costs and supports over 21 inference providers and 8 domestic API/open-source models for seamless integration.

Unified API Gateway & Access Control

• Standardized interfaces for data and algorithm orchestration
• Automates authentication, traffic control, and audit logging, significantly reducing operational complexity.

Dynamic Evaluation and Optimization Engine

• Multi-model benchmarking (e.g., HaxiTAG Prompt Button & HaxiTAG Prompt Context) enables parallel performance testing across LLMs.
• Visual dashboards compare cost and performance metrics, guiding model selection with data-driven insights.

Hybrid Deployment Strategy

• Balances privacy and performance:
  • Localized models (e.g., Llama 3) for highly sensitive data (e.g., medical diagnostics)
  • Cloud models (e.g., GPT-4o) for real-time, cost-effective solutions

HaxiTAG Instruction Transform & Context Builder Engine

• Trained on 100,000+ real-world enterprise AI interactions, dynamically optimizing instructions and context allocation.
• Supports integration with private enterprise data, industry knowledge bases, and open datasets.
• Context builder automates LLM inference pre-processing, handling structured/unstructured data, SQL queries, and enterprise IT logs for seamless adaptation.

Comprehensive Governance Framework

Compliance Engine

• Classifies AI risks based on use cases, triggering appropriate review workflows (e.g., human audits, explainability reports, factual verification).

Continuous Learning Pipeline

• Iteratively optimizes models through feedback loops (e.g., user ratings, error log analysis), preventing model drift and ensuring sustained performance.

Advanced Applications

• Private LLM training, fine-tuning, and SFT (Supervised Fine-Tuning) tasks
• End-to-end automation of data-to-model training pipelines

Practical Value: From Proof of Concept to Scalable Deployment

HaxiTAG’s real-world collaborations have demonstrated the scalability and efficiency of HaxiTAG Deck in enterprise AI adoption:

1. Agile Development

• A fintech company launched an AI chatbot in two weeks using HaxiTAG Deck, evaluating five different LLMs and ultimately selecting GLM-7B, reducing inference costs by 45%.

2. Organizational Knowledge Collaboration

• HaxiTAG’s EiKM intelligent knowledge management system enables business teams to refine AI-driven services through real-time prompt tuning, while R&D and IT teams focus on security and infrastructure.
• Breaks down silos between AI development, IT, and business operations.

3. Sustainable Development & Expansion

• A multinational enterprise integrated HaxiTAG ESG reporting services with its ERP, supply chain, and OA systems, leveraging a hybrid RAG (retrieval-augmented generation) framework to dynamically model millions of documents and structured databases—all without complex coding.

4. Versatile Plugin Ecosystem

• 100+ validated AI solutions, including:
  • Multilingual, cross-jurisdictional contract review
  • Automated resume screening, JD drafting, candidate evaluation, and interview analytics
  • Market research and product analysis

Many lightweight applications are plug-and-play, requiring minimal customization.

Enterprise AI Strategy: Key Recommendations

1. Define Clear Objectives

• A common pitfall in AI implementation is lack of clarity—too many disconnected goals lead to fragmented execution.
• A structured roadmap prevents AI projects from becoming endless loops of debugging.

2. Leverage Best Practices in Your Domain

• Utilize industry-specific AI communities (e.g., HaxiTAG’s LLM application network) to find proven implementation models.
• Engage AI transformation consultants if needed.

3. Layered Model Selection Strategy

• Base models: GPT-4, Qwen2.5
• Domain-specific fine-tuned models: FinancialBERT, Granite
• Lightweight edge models: TinyLlama
• API-based inference services: OpenAI API, Doubao API

4. Adaptive Governance Model

• Implement real-time risk assessment for LLM outputs (e.g., copyright risks, bias propagation).
• Establish incident response mechanisms to mitigate uncontrollable algorithm risks.

5. Rigorous Output Evaluation

• Non-self-trained LLMs pose inherent risks due to unknown training data and biases.
• A continuous assessment framework ensures bad-case detection and mitigation.

Future Trends

With multimodal AI and intelligent agent technologies maturing, HaxiTAG Deck will evolve towards:

1. Cross-modal AI applications (e.g., Text-to-3D generation, inspired by Tsinghua’s LLaMA-Mesh project).
2. Automated AI execution agents for enterprise workflows (e.g., AI-powered content generation and intelligent learning assistants).

HaxiTAG Deck is not just a technical architecture—it is the operating system for enterprise AI strategy.

By standardizing, modularizing, and automating AI governance, HaxiTAG Deck transforms LLMs from experimental tools into core productivity drivers.

As AI regulatory frameworks mature and multimodal innovations emerge, HaxiTAG Deck will likely become a key benchmark for enterprise AI maturity.

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Analysis and Interpretation of OpenAI's Research Report "Identifying and Scaling AI Use Cases"

Since the advent of artificial intelligence (AI) technology in the public sphere, its applications have permeated every aspect of the business world. Research conducted by OpenAI in collaboration with leading industry players shows that AI is reshaping productivity dynamics in the workplace. Based on in-depth analysis of 300 successful case studies, 4,000 adoption surveys, and data from over 2 million business users, this report systematically outlines the key paths and strategies for AI application deployment. The study shows that early adopters have achieved 1.5 times faster revenue growth, 1.6 times higher shareholder returns, and 1.4 times better capital efficiency compared to industry averages. However, it is noteworthy that only 1% of companies believe their AI investments have reached full maturity, highlighting a significant gap between the depth of technological application and the realization of business value.

AI Generative AI Opportunity Identification Framework

Repetitive Low-Value Tasks

The research team found that knowledge workers spend an average of 12.7 hours per week on tasks such as document organization and data entry. For instance, at LaunchDarkly, the Chief Product Officer created an "Anti-To-Do List," delegating 17 routine tasks such as competitor tracking and KPI monitoring to AI, which resulted in a 40% increase in strategic decision-making time. This shift not only improved efficiency but also reshaped the value evaluation system for roles. For example, a financial services company used AI to automate 82% of its invoice verification work, enabling its finance team to focus on optimizing cash flow forecasting models, resulting in a 23% improvement in cash turnover efficiency.

Breaking Through Skill Bottlenecks

AI has demonstrated its unique bridging role in cross-departmental collaboration scenarios. A biotech company’s product team used natural language to generate prototype design documents, reducing the product requirement review cycle from an average of three weeks to five days. More notably, the use of AI tools for coding by non-technical personnel is becoming increasingly common. Surveys indicate that the proportion of marketing department employees using AI to write Python scripts jumped from 12% in 2023 to 47% in 2025, with 38% of automated reporting systems being independently developed by business staff.

Handling Ambiguity in Scenarios

When facing open-ended business challenges, AI's heuristic thinking demonstrates its unique value. A retail brand's marketing team used voice interaction to brainstorm advertising ideas, increasing quarterly marketing plan output by 2.3 times. In the strategic planning field, AI-assisted SWOT analysis tools helped a manufacturing company identify four potential blue ocean markets, two of which saw market share in the top three within six months.

Six Core Application Paradigms

The Content Creation Revolution

AI-generated content has surpassed simple text reproduction. In Promega's case, by uploading five of its best blog posts to train a custom model, the company increased email open rates by 19% and reduced content production cycles by 67%. Another noteworthy innovation is style transfer technology—financial institutions have developed models trained on historical report data that automatically maintain consistency in technical terminology, improving compliance review pass rates by 31%.

Empowering Deep Research

The new agentic research system can autonomously complete multi-step information processing. A consulting company used AI's deep research functionality to analyze trends in the healthcare industry. The system completed the analysis of 3,000 annual reports within 72 hours and generated a cross-verified industry map, achieving 15% greater accuracy than manual analysis. This capability is particularly outstanding in competitive intelligence—one technology company leveraged AI to monitor 23 technical forums in real-time, improving product iteration response times by 40%.

Democratization of Coding Capabilities

Tinder's engineering team revealed how AI reshapes development workflows. In Bash script writing scenarios, AI assistance reduced unconventional syntax errors by 82% and increased code review pass rates by 56%. Non-technical departments are also significantly adopting coding applications—at a retail company, the marketing department independently developed a customer segmentation model that increased promotion conversion rates by 28%, with a development cycle that was only one-fifth of the traditional method.

The Transformation of Data Analysis

Traditional data analysis processes are undergoing fundamental changes. After uploading quarterly sales data, an e-commerce platform's AI not only generated visual charts but also identified three previously unnoticed inventory turnover anomalies, preventing potential losses of $1.2 million after verification. In the finance field, AI-driven data coordination systems shortened the monthly closing cycle from nine days to three days, with an anomaly detection accuracy rate of 99.7%.

Workflow Automation

Intelligent automation has evolved from simple rule execution to a cognitive level. A logistics company integrated AI with IoT devices to create a dynamic route planning system, reducing transportation costs by 18% and increasing on-time delivery rates to 99.4%. In customer service, a bank deployed an intelligent ticketing system that autonomously handled 89% of common issues, routing the remaining cases to the appropriate experts, leading to a 22% increase in customer satisfaction.

Evolution of Strategic Thinking

AI is changing the methodology for strategic formulation. A pharmaceutical company used generative models to simulate clinical trial plans, speeding up R&D pipeline decision-making by 40% and reducing resource misallocation risks by 35%. In merger and acquisition assessments, a private equity firm leveraged AI for in-depth data penetration analysis of target companies, identifying three financial anomalies and avoiding potential investment losses of $450 million.

Implementation Path and Risk Warnings

The research found that successful companies generally adopt a "three-layer advancement" strategy: leadership sets strategic direction, middle management establishes cross-departmental collaboration mechanisms, and grassroots innovation is stimulated through hackathons. A multinational group demonstrated that setting up an "AI Ambassador" system could increase the efficiency of use case discovery by three times. However, caution is needed regarding the "technology romanticism" trap—one retail company overly pursued complex models, leading to 50% of AI projects being discontinued due to insufficient ROI.

HaxiTAG’s team, after reading OpenAI's research report openai-identifying-and-scaling-ai-use-cases.pdf, analyzed its implementation value and conflicts. The report emphasizes the need for leadership-driven initiatives, with generative AI enterprise applications as a future investment. Although 92% of effective use cases come from grassroots practices, balancing top-down design with bottom-up innovation requires more detailed contingency strategies. Additionally, while the research emphasizes data-driven decision-making, the lack of a specific discussion on data governance systems in the case studies may affect the implementation effectiveness. It is recommended that a dynamic evaluation mechanism be established during implementation to match technological maturity with organizational readiness, ensuring a clear and measurable value realization path.

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Key Challenges and Strategic Solutions for Enterprise AI Adoption: Deep Insights and Practices from HaxiTAG

With the rapid advancement of artificial intelligence (AI), enterprises are increasingly recognizing its immense potential in enhancing productivity and optimizing business processes. However, translating AI into sustainable productivity presents multiple challenges, ranging from defining high-ROI use cases to addressing data security concerns, managing technical implementation complexity, and achieving large-scale deployment.

Leveraging its deep industry expertise and cutting-edge technological innovations, HaxiTAG offers innovative solutions to these challenges. This article provides an in-depth analysis of the key hurdles in enterprise AI adoption, supported by real-world HaxiTAG case studies, and outlines differentiated strategies and future development trends.

Key Challenges in Enterprise AI Adoption

1. Ambiguous Value Proposition: Difficulty in Identifying High-ROI Use Cases

While most enterprises acknowledge AI’s potential, they often lack a clear roadmap for implementation in core departments such as finance, human resources, market research, customer service, and support. This results in unclear investment priorities and an uncertain AI adoption strategy.

2. Data Control and Security: Balancing Regulation and Trust

• Complex data integration and access management: The intricate logic of data governance makes permission control a challenge.
• Stringent regulatory compliance: Highly regulated industries such as finance and healthcare impose strict data privacy requirements, making AI deployment difficult. Enterprises must ensure data remains within their firewalls to comply with regulations.

3. Complexity of AI Implementation: Development Barriers vs. Resource Constraints

• High dependency on centralized AI PaaS and SaaS services: Limited flexibility makes it difficult for SMEs to bear the high costs of building their own solutions.
• Rapid iterations of AI models and computing platforms: Enterprises struggle to decide between in-house development and external partnerships.

4. Scaling AI from Experimentation to Production: The Trust Gap

Transitioning AI solutions from proof of concept (PoC) to production-grade deployment (such as AI agents) involves substantial technical, resource, and risk barriers.

HaxiTAG’s Strategic AI Implementation Approach

1. Data Connectivity and Enablement

• Direct System Integration: HaxiTAG seamlessly integrates AI models with enterprise ERP and CRM systems. By leveraging real-time transformation engines and automated data pipelines, enterprises can gain instant access to financial and supply chain data. Case studies demonstrate how non-technical teams successfully retrieve and utilize internal data to execute complex tasks.
• Private Data Loops: AI solutions are deployed on-premises or via private cloud, ensuring compliance with global privacy regulations such as China’s Personal Information Protection Law, the Cybersecurity Law, GDPR (EU), and HIPAA (US).

2. Security-First AI Architecture

• Zero-Trust Design: Incorporates encryption, tiered access controls, and audit mechanisms at both data flow and compute levels.
• Industry-Specific Compliance: Pre-built regulatory compliance modules for sectors such as healthcare and finance streamline AI deployment while ensuring adherence to industry regulations.

3. Transitioning from "Chat-Based AI" to "Production-Grade AI Agents"

• Task Automation: Specialized AI agents handle repetitive tasks, such as financial report generation and customer service ticket categorization.
• End-to-End AI Solutions: HaxiTAG integrates data ingestion, workflow automation, and feedback optimization into comprehensive toolchains, such as HaxiTAG Studio.

4. Lowering Implementation Barriers

• Fine-Tuned Pre-Trained Models: AI models are adapted using proprietary enterprise data, reducing deployment costs.
• Low-Code/No-Code Interfaces: Business teams can configure AI agents via visual tools without relying on data scientists.

Key Insights from Real-World Implementations

1. AI Agent Scalability

By 2025, core enterprise functions such as finance, HR, marketing, and customer service are expected to adopt custom AI agents, automating over 80% of rule-based and repetitive tasks.

2. Increased Preference for Private AI Deployments

Organizations will favor on-premise AI deployment to balance innovation with data sovereignty, especially in the financial sector.

3. Shift from "Model Competition" to "Scenario-Driven AI"

Enterprises will focus on vertically integrated AI solutions tailored for specific business use cases, rather than merely competing on model size or capabilities.

4. Human-AI Collaboration Paradigm Shift

AI will evolve from simple question-answer interactions to co-intelligence execution. AI agents will handle data collection, while humans will focus on decision analysis and validation of key nodes and outcomes.

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HaxiTAG’s Differentiated Approach

Challenges with Traditional AI Software Solutions

• Data silos hinder integration
• LLMs and GenAI models are black-box systems, lacking transparency in reasoning and decision-making
• General-purpose AI models struggle with real-world business needs, reducing reliability in specific domains
• Balancing security and efficiency remains a challenge
• High development costs for adapting AI to production-level solutions

HaxiTAG’s Solutions

✅ Direct Integration with Enterprise Databases, SaaS Platforms, and Industry Data
✅ Provides explainable AI logs and human-in-the-loop intervention
✅ Supports private data fine-tuning and industry-specific terminology embedding
✅ Offers hybrid deployment models for offline or cloud-based processing with dynamic access control
✅ Delivers turnkey, end-to-end AI solutions

Enterprise AI Adoption Recommendations

1. Choose AI Providers That Prioritize Control and Compliance

• Opt for vendors that support on-premise deployment, data sovereignty, and regulatory compliance.

2. Start with Small-Scale Pilots

• Begin AI adoption with low-risk use cases such as financial reconciliation and customer service ticket categorization before scaling.

3. Establish an AI Enablement Center

• Implement AI-driven workflow optimization to enhance organizational intelligence.
• Train business teams to use low-code tools for developing AI agents, reducing dependence on IT departments.

Conclusion

Successful enterprise AI adoption goes beyond technological advancements—it requires secure and agile architectures that transform internal data into intelligent AI agents.

HaxiTAG’s real-world implementations highlight the strategic importance of private AI deployment, security-first design, and scenario-driven solutions.

As AI adoption matures, competition will shift from model capability to enterprise-grade usability, emphasizing data pipelines, toolchains, and privacy-centric AI ecosystems.

Organizations that embrace scenario-specific AI deployment, prioritize security, and optimize AI-human collaboration will emerge as leaders in the next phase of enterprise intelligence transformation.

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The Evolution of Enterprise AI Applications: Organizational Restructuring and Value Realization

— An In-Depth Analysis Based on McKinsey’s The State of AI: How Organizations Are Rewiring to Capture Value (March 12, 2025) and HaxiTAG’s Industry Applications

The Structural Shift in Enterprise AI Applications

By 2025, artificial intelligence (AI) has entered a phase of systemic integration within enterprises. Organizations are moving beyond isolated innovations and instead restructuring their operations to unlock AI’s full-scale value. McKinsey’s The State of AI report provides a comprehensive analysis of how companies are reshaping governance structures, optimizing workflows, and mitigating AI-related risks to maximize the potential of generative AI (Gen AI). HaxiTAG’s extensive work in enterprise decision intelligence, knowledge computation, and ESG (Environmental, Social, and Governance) intelligence reinforces a clear trend: AI’s true value lies not only in technological breakthroughs but in the reinvention of organizational intelligence.

From AI Algorithms and Technological Breakthroughs to Enterprise Value Realization

The report highlights that the fundamental challenge in enterprise AI adoption is not the technology itself, but how organizations can transform their structures to capture AI-driven profitability. HaxiTAG’s industry experience confirms this insight—delivering substantial Gen AI value requires strategic action across several key dimensions:

1. The Core Logic of AI Governance: Shifting from Technical Decision-Making to Executive Leadership

• McKinsey’s Insights: Research shows that enterprises where the CEO directly oversees AI governance report the highest impact of AI on EBIT (Earnings Before Interest and Taxes). This underscores the need to position AI as a top-level strategic imperative, rather than an isolated initiative within technical departments.
• HaxiTAG’s Practice: In deploying the ESGtank ESG Intelligence Platform and YueLi Knowledge Computation Engine, HaxiTAG has adopted a joint governance model involving the CIO, business executives, and AI experts to ensure that AI is seamlessly embedded into business operations, enabling large-scale industry intelligence.

2. Workflow Redesign: How Gen AI Reshapes Enterprise Operations

• McKinsey’s Data: 21% of enterprises have fundamentally restructured certain workflows, indicating that Gen AI is not just a tool upgrade—it is a disruptor of business models.
• HaxiTAG’s Cases:
  • Intelligent Knowledge Management: In the EiKM Enterprise Knowledge Management System, HaxiTAG has developed an automated knowledge flow framework powered by Gen AI, allowing organizations to build real-time knowledge repositories from multi-source data, thereby enhancing market research and compliance analysis.
  • AI-Optimized Supply Chain Finance: HaxiTAG’s intelligent credit assessment engine, leveraging multimodal AI analysis, enables dynamic risk evaluation and financing optimization, significantly improving enterprises’ capital turnover efficiency.

3. AI Talent and Capability Building: Addressing the Skills Gap

• McKinsey’s Observations: Over the next three years, enterprises will intensify efforts to train AI-related talent, particularly data scientists, AI ethics and compliance specialists, and AI product managers.
• HaxiTAG’s Initiatives:
  • Implementing an embedded AI learning model, where the YueLi Knowledge Computation Engine features an intelligent training system that enables employees to acquire AI skills in real business contexts.
  • Combining AI-driven mentoring with expert knowledge graphs, ensuring seamless integration of enterprise knowledge and AI competencies, facilitating the transition from skill gaps to AI empowerment.

Risk Governance and Trustworthy AI Frameworks in AI Applications

1. Trustworthiness and Risk Control in Generative AI

• McKinsey’s Data: The top concerns surrounding Gen AI adoption include inaccuracy, intellectual property infringement, data security, and decision-making transparency.
• HaxiTAG’s Response:
  • Deploying a multi-tiered knowledge computation and causal inference model to enhance explainability and accuracy of AI-generated content.
  • Integrating YueLi Knowledge Computation Engine (KGM) to combine symbolic logic with deep learning, reducing AI hallucinations and improving factual consistency.
  • Establishing a "Trustworthy AI + ESG Compliance Framework" in ESGtank’s ESG data analytics solutions to ensure regulatory compliance in sustainability assessments.

2. AI Governance Architectures: Centralized vs. Decentralized Models

• McKinsey’s Data: Key AI governance elements, such as risk management and data governance, are predominantly centralized, while AI talent and operational deployment follow a hybrid model.
• HaxiTAG’s Implementation:
  • ESGtank adopts a centralized AI ethics governance model (establishing an AI Ethics Committee) while embedding decentralized AI capability units within enterprises, allowing independent innovation while ensuring alignment with overarching compliance frameworks.
  • The HaxiTAG AI Middleware uses an API + microservices architecture, ensuring that various enterprise modules can efficiently utilize AI capabilities without falling into fragmented, siloed deployments.

AI-Driven Business Model Transformation

1. AI-Driven Revenue Growth: Unlocking Monetization Opportunities

• McKinsey’s Data: 47% of enterprises reported direct revenue growth from AI adoption in marketing and sales.
• HaxiTAG’s Cases:
  • Gen AI-Powered Smart Marketing: HaxiTAG has developed an A/B testing and multimodal content generation system, optimizing advertising performance and maximizing marketing ROI.
  • AI-Driven Financial Risk Solutions: In supply chain finance, HaxiTAG’s intelligent risk control models have increased SME financing success rates by 30%.

2. AI-Enabled Cost Reduction and Automation

• McKinsey’s Insights: In the second half of 2024, most enterprises reduced costs in IT, knowledge management, and HR through AI.
• HaxiTAG’s Implementations:
  • In AI-powered customer service, the AI knowledge management + human-AI collaboration model has reduced operational costs by 30% while enhancing customer satisfaction.
  • In ESG compliance, automated regulatory interpretation and report generation have cut compliance costs while improving audit quality.

Future Outlook: AI-Enabled Enterprise Transformation

1. AI Agents (Agentic AI): The Next Frontier of AI Innovation

McKinsey predicts that AI agents (Agentic AI) will emerge as the next major breakthrough in enterprise AI adoption by 2025. HaxiTAG’s strategic initiatives in this area include:

• Intelligent Knowledge Agents: The YueLi Knowledge Computation Engine is embedding AI agents leveraging LLMs + knowledge graphs to dynamically optimize enterprise knowledge assets.
• Automated Intelligent Decision-Making Systems: In supply chain finance and ESG analytics, AI agents autonomously analyze, infer, and execute complex tasks, advancing enterprises toward fully automated operations.
• HaxiTAG Bot Factory: A low-code editing platform for creating and running intelligent agent collaboration for enterprises based on private data and models, significantly reducing the threshold for enterprises' intelligent transformation.

2. The Ultimate Form of Industrial Intelligence

The ultimate goal of enterprise intelligence is not merely AI technology adoption, but the deep integration of AI as a cognitive engine that transforms organizational structures and decision-making processes. In the future, AI will evolve from being a mere execution tool to becoming a strategic partner, intelligent decision-maker, and value creator.

AI Inside: The Organizational Reinvention of the Era

McKinsey’s report emphasizes that AI’s true value lies in "rewiring organizations, not merely replacing human labor." HaxiTAG’s experience further validates this by highlighting four key enablers for AI-driven enterprise transformation:

1. Executive leadership in AI governance, ensuring AI is integral to corporate strategy.
2. Workflow reengineering, embedding AI deeply into operational frameworks.
3. Risk governance and trustworthy AI, securing AI’s reliability and regulatory compliance.
4. Business model innovation, leveraging AI to drive revenue growth and cost optimization.

In this era of digital transformation, only organizations that undertake comprehensive structural reinvention will unlock AI’s full potential.

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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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2025 Productivity Transformation Report

A study by Grammarly involving 1,032 knowledge workers and 254 business leaders revealed that professionals spend over 28 hours per week on written and tool-based communication, marking a 13.2% increase from the previous year. Notably, 60% of professionals struggle with constant notifications, leading to reduced focus. Despite increased communication frequency, actual productivity has not improved, resulting in a disconnect between "performative productivity" and real efficiency.

The report further highlights that AI-fluent users—those who effectively leverage AI tools—save significantly more time and experience greater productivity and job satisfaction. On average, AI-fluent users save 11.4 hours per week, compared to just 6.3 hours for users merely familiar with AI.

These findings align with HaxiTAG’s observations in digital transformation practices for enterprises. Excessive meetings and redundant tasks often stem from misaligned information and status updates. By integrating HaxiTAG’s intelligent digital solutions—built upon data, case studies, and digitized best practices—organizations can establish a human-AI symbiotic ecosystem. This approach systematically enhances productivity and competitiveness, making it a key pathway for digital transformation.

Background and Problem Diagnosis

1. Communication Overload: The Invisible Productivity Killer

• Time and Cost Waste
  Knowledge workers lose approximately 13 hours per week to inefficient communication and performative tasks. In a company with 1,000 employees, this translates to an annual hidden cost of $25.6 million.

• Employee Well-being and Retention Risks
  Over 80% of employees report additional stress due to ineffective communication, and nearly two-thirds consider leaving their jobs. The impact is particularly severe for multilingual and neurodiverse employees.

• Business and Customer Impact
  Nearly 80% of business leaders say declining communication efficiency affects customer satisfaction, with 40% of companies facing transaction losses.

2. Disparity in AI Adoption: Fluent Users vs. Avoiders

• Significant Advantages of AI-Fluent Users
  Only 13% of employees and 30% of business leaders are classified as AI-fluent, yet their productivity gains reach 96%. They save an average of 11.4 hours per week and report enhanced customer relationships.

• Risks of AI Avoidance
  About 22% of employees avoid AI due to fear of job displacement or lack of tool support, preventing businesses from fully leveraging AI’s potential.

Four-Step AI-Powered Strategy for Productivity Enhancement

To address communication overload and AI adoption disparities, we propose a structured four-step strategy:

1. Reshaping Employee Mindset: From Fear to Empowerment

• Leadership Demonstration and Role Modeling
  Executives should actively use and promote AI tools, demonstrating that AI serves as an assistant rather than a replacement, thereby fostering trust.

• Transparent Communication and AI Literacy Training
  Internal case studies and customized training programs should clarify AI’s benefits, improving employees’ recognition of AI’s supportive role—similar to the 92% AI acceptance rate observed among fluent users in the study.

2. Phased AI Literacy Development

• Basic Onboarding
  For beginners, training should focus on fundamental tools such as translation and writing assistants, leveraging LLMs like Deepseek, Doubao, and ChatGPT for batch processing and creative content generation.

• Intermediate Applications
  Mid-level users should be trained in content creation, data analysis, and task automation (e.g., AI-generated meeting summaries) to enhance efficiency.

• Advanced Fluency
  Experienced users should explore AI-driven agency tasks, such as automated project report generation and strategic communication support, positioning them as internal AI experts.

• Targeted Support
  Multilingual and neurodiverse employees should receive customized tools (e.g., real-time translation and structured information retrieval) to ensure inclusivity.

3. Workflow Optimization: Shifting from Performative to Outcome-Driven Work

• Communication Streamlining and Integration
  Implement unified collaboration platforms (e.g., Feishu, DingTalk, WeCom, Notion, Slack) with AI-driven classification and filtering to reduce communication fragmentation.

• Automation of Repetitive Tasks
  AI should handle routine tasks such as ad copy generation, meeting transcription, and code review, allowing employees to focus on high-value work.

4. Tool and Ecosystem Development: Data-Driven Continuous Optimization

• Enterprise-Grade Security and Tool Selection
  Deploy AI tools with robust data intelligence capabilities, including multimodal data pipelines and Microsoft Copilot, ensuring security compliance.

• Performance Monitoring and Iteration
  Establish AI utilization monitoring systems, tracking key metrics like weekly time savings and error reduction rates to refine tool selection and workflows.

Targeted AI Strategies for Different Teams

Team Type	Core Challenges	AI Application Focus	Expected Benefits
Marketing	High-frequency content creation (41.7 hours/week)	AI-generated ad copy, automated social media content	91% increase in creative efficiency, doubled output speed
Customer Service	High-pressure real-time communication (70% of time)	AI-powered FAQs, sentiment analysis for optimized responses	15% improvement in customer satisfaction, 40% faster response time
Sales	Information overload delaying decisions	AI-driven customer insights, personalized email generation	12% increase in conversion rates, 30% faster communication
IT Team	Complex technical communication (41.5 hours/week)	AI-assisted code generation, automated documentation	20% reduction in development cycles, 35% lower error rates

By implementing customized AI strategies, teams can not only address specific pain points but also enhance overall collaboration and operational efficiency.

Leadership Action Guide: Driving Strategy Implementation and Cultural Transformation

Executives play a pivotal role in digital transformation. Recommended actions include:

• Setting Strategic Priorities
  Positioning AI-powered communication and collaboration as top priorities to ensure organizational alignment.

• Investing in Employee Development
  Establishing AI mentorship programs to encourage knowledge-sharing and skill-building across teams.

• Quantifying Outcomes and Implementing Incentives
  Incorporating AI usage metrics into KPI evaluations, rewarding teams based on productivity improvements.

Future Outlook: From Efficiency Gains to Innovation-Driven Growth

Digital transformation extends beyond efficiency optimization—it serves as a strategic lever for long-term innovation and resilience:

• Unleashing Employee Creativity
  By resolving communication overload, employees can focus on strategic thinking and innovation, while multilingual employees can leverage AI to participate in global projects.

• Building a Human-AI Symbiotic Ecosystem
  AI acts as an amplifier of human capabilities, fostering high-performance collaboration and driving intelligent productivity.

• Creating Agile and Resilient Organizations
  AI enables real-time communication, data-driven decision-making, and automated workflows, helping businesses adapt swiftly to market changes.

Empowering Partners for Collaborative Success

HaxiTAG is committed to helping enterprises overcome communication overload, enhance workforce productivity, and strengthen competitive advantage. Our solution is:

• Data-Driven and Case-Supported
  Integrating insights from the 2025 Productivity Transformation Report to provide evidence-based transformation strategies.

• Comprehensive and Multi-Dimensional
  Covering mindset shifts, technical implementation, team-specific support, and leadership enablement.

• A Catalyst for Innovation and Resilience
  Establishing a "human-AI symbiosis" model to drive both immediate efficiency gains and long-term innovation.

Join our community to explore AI-powered productivity solutions and access over 400 AI application research reports. Click here to contact us.

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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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Practical Testing and Selection of Enterprise LLMs: The Importance of Model Inference Quality, Performance, and Fine-Tuning

In the course of modern enterprises' digital transformation, adopting large language models (LLMs) as the infrastructure for natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) applications has become a prevailing trend. However, choosing the right LLM model to meet enterprise needs, especially testing and optimizing these models in real-world applications, has become a critical issue that every decision-maker must carefully consider. This article delves into several key aspects that enterprises need to focus on when selecting LLM models, helping readers understand the significance and key challenges in practical applications.

NLP Model Training Based on Enterprise Data and Data Security

When choosing an LLM, enterprises must first consider whether the model can be effectively generated and trained based on their own data. This not only relates to the model's customization capability but also directly impacts the enterprise's performance in specific application scenarios. For instance, whether an enterprise's proprietary data can successfully integrate with the model training data to generate more targeted semantic understanding models is crucial for the effectiveness and efficiency of business process automation.

Meanwhile, data security and privacy cannot be overlooked in this process. Enterprises often handle sensitive information, so during the model training and fine-tuning process, it is essential to ensure that this data is never leaked or misused under any circumstances. This requires the chosen LLM model to excel in data encryption, access control, and data management, thereby ensuring compliance with data protection regulations while meeting business needs.

Comprehensive Evaluation of Model Inference Quality and Performance

Enterprises impose stringent requirements on the inference quality and performance of LLM models, which directly determines the model's effectiveness in real-world applications. Enterprises typically establish a comprehensive testing framework that simulates interactions between hundreds of thousands of end-users and their systems to conduct extensive stress tests on the model's inference quality and scalability. In this process, low-latency and high-response models are particularly critical, as they directly impact the quality of the user experience.

In terms of inference quality, enterprises often employ the GSB (Good, Same, Bad) quality assessment method to evaluate the model's output quality. This assessment method not only considers whether the model's generated responses are accurate but also emphasizes feedback perception and the score on problem-solving relevance to ensure the model truly addresses user issues rather than merely generating seemingly reasonable responses. This detailed quality assessment helps enterprises make more informed decisions in the selection and optimization of models.

Fine-Tuning and Hallucination Control: The Value of Proprietary Data

To further enhance the performance of LLM models in specific enterprise scenarios, fine-tuning is an indispensable step. By using proprietary data to fine-tune the model, enterprises can significantly improve the model's accuracy and reliability in specific domains. However, a common issue during fine-tuning is "hallucinations" (i.e., the model generating incorrect or fictitious information). Therefore, enterprises need to assess the hallucination level in each given response and set confidence scores, applying these scores to the rest of the toolchain to minimize the number of hallucinations in the system.

This strategy not only improves the credibility of the model's output but also builds greater trust during user interactions, giving enterprises a competitive edge in the market.

Conclusion

Choosing and optimizing LLM models is a complex challenge that enterprises must face in their digital transformation journey. By considering NLP model training based on enterprise data and security, comprehensively evaluating inference quality and performance, and controlling hallucinations through fine-tuning, enterprises can achieve high-performing and highly customized LLM models while ensuring data security. This process not only enhances the enterprise's automation capabilities but also lays a solid foundation for success in a competitive market.

Through this discussion, it is hoped that readers will gain a clearer understanding of the key factors enterprises need to focus on when selecting and testing LLM models, enabling them to make more informed decisions in real-world applications.

HaxiTAG Studio is an enterprise-level LLM GenAl solution that integrates AIGC Workflow and privatization data fine-tuning.

Through a highly scalable Tasklets pipeline framework, flexible Al hub components, adpter, and KGM component, HaxiTAG Studio enables flexible setup, orchestration, rapid debugging, and realization of product POC. Additionally, HaxiTAG Studio is embedded with RAG technology solution and training data annotation tool system, assisting partners in achieving low-cost and rapid POC validation, LLM application, and GenAl integration into enterprise applications for quick verification and implementation.

As a trusted LLM and GenAl industry application solution, HaxiTAG provides enterprise partners with LLM and GenAl application solutions, private Al, and applied robotic automation to boost efficiency and productivity in applications and production systems. It helps partners leverage their data knowledge assets, integrate heterogeneous multi-modal information, and combine advanced Al capabilities to support fintech and enterprise application scenarios, creating value and growth opportunities.

HaxiTAG Studio, driven by LLM and GenAl, arranges bot sequences, creates feature bots, feature bot factories, and adapter hubs to connect external systems and databases for any function. HaxiTAG is a trusted solution for LLM and GenAl industry applications, designed to supply enterprise partners with LLM and GenAl application solutions, private Al, and robotic process automation to enhance efficiency and productivity. It helps partners leverage their data knowledge assets, relate and produce heterogeneous multimodal information, and amalgamate cutting-edge Al capabilities with enterprise application scenarios, creating value and development opportunities.

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