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Thursday, July 31, 2025

Four Strategic Steps for AI-Driven Procurement Transformation: Maturity Assessment, Buy-or-Build Decision, Capability Enablement, and Value Capture

July 31, 2025

Four Strategic Steps for AI-Driven Procurement Transformation: Maturity Assessment, Buy-or-Build Decision, Capability Enablement, and Value Capture

Integrating Artificial Intelligence (AI) into procurement is not a one-off endeavor, but a structured journey that requires four critical stages. These are: conducting a comprehensive digital maturity assessment, making strategic decisions on whether to buy or build AI solutions, empowering teams with the necessary skills and change management, and continuously capturing financial value through improved data insights and supplier negotiations. This article draws from leading industry practices and the latest research to provide an in-depth analysis of each stage, offering procurement leaders a practical roadmap for advancing their AI transformation initiatives with confidence.

Digital Maturity Assessment

Before embarking on AI adoption, organizations must first evaluate their level of digital maturity to accurately identify current pain points and future opportunities. AI maturity models offer procurement leaders a strategic framework to map out their current state across technological infrastructure, team capabilities, and the digitization of procurement processes—thereby guiding the development of a realistic and actionable transformation roadmap.

According to McKinsey, a dual-track approach is essential: one track focuses on implementing high-impact, quick-win AI and analytics use cases, while the other builds a scalable data platform to support long-term innovation. Meanwhile, DNV’s AI maturity assessment methodology emphasizes aligning AI ambitions with organizational vision and industry benchmarks to ensure clear prioritization and avoid isolated, siloed technologies.

Buy vs. Build: Technology Decision-Making

A pivotal question facing many organizations is whether to purchase off-the-shelf AI solutions or develop customized systems in-house. Buying ready-made solutions often enables faster deployment, provides user-friendly interfaces, and requires minimal in-house AI expertise. However, such solutions may fall short in meeting the nuanced and specialized needs of procurement functions.

Conversely, organizations with higher AI ambitions may prefer to build tailored systems that deliver deeper visibility into spending, contract optimization, and ESG (Environmental, Social, and Governance) alignment. This route, however, demands strong internal capabilities in data engineering and algorithm development, and requires careful consideration of long-term maintenance costs versus strategic benefits.

As Forbes highlights, successful AI implementation depends not only on technology, but also on internal trust, ease of use, and alignment with long-term business strategy—factors often overlooked in the buy-vs.-build debate. Initial investment and ongoing iteration costs should also be factored in early to ensure sustainable returns.

Capability Enablement and Team Empowerment

AI not only accelerates existing procurement workflows but also redefines them. As such, empowering teams with new skills is crucial. According to BCG, only 10% of AI’s total value stems from algorithms themselves, while 20% comes from data and platforms—and a striking 70% is driven by people’s ability to adapt to and embrace new ways of working.

A report by Economist Impact reveals that 64% of enterprises already use AI tools in procurement. This shift demands that existing employees develop data analysis and decision support capabilities, while also incorporating new roles such as data scientists and AI engineers. Leadership must champion change management, foster open communication, and create a culture of experimentation and continuous learning to ensure skills development is embedded in daily operations.

Hackett Group emphasizes that the most critical future skills for procurement teams include advanced analytics, risk assessment, and cross-functional collaboration—essential for navigating complex negotiations and managing supplier relationships. Supply Chain Management Review also notes that AI empowers resource-constrained organizations to "learn by doing," accelerating hands-on mastery and fostering a mindset of continuous improvement.

Capturing Value from Suppliers

The ultimate goal of AI in procurement is to deliver measurable business value. This includes enhanced pre-negotiation insights through advanced data analytics, optimized contract terms, and even influencing suppliers to adopt generative AI (GenAI) technologies to reduce costs across the supply chain.

BCG’s research shows that organizations undertaking these four transformation steps can achieve cost savings of 15% to 45% in select product and service categories. Success hinges on deeply embedding AI into procurement workflows and delivering a compelling initial user experience to foster adoption and scale. Sustained value creation also requires strong executive sponsorship, with clear KPIs and continuous promotion of success stories to ensure AI becomes a core driver of long-term enterprise growth.

Conclusion

In today’s fiercely competitive landscape, AI-powered procurement transformation is no longer optional—it is imperative. It serves as a vital lever for gaining future-ready advantages and building core competitive capabilities. Backed by structured maturity assessments, precise technology decisions, robust capability building, and sustainable value capture, the Hashitag team stands ready to support your procurement organization in navigating the digital tide and achieving intelligent transformation. We hope this four-step framework provides clarity and direction as your organization advances toward the next era of procurement excellence.

In-Depth Insights, Analysis, and Commentary on the Adoption Trends of Agentic AI in Enterprises

July 28, 2025

— A Professional Interpretation of KPMG’s “2025 Q2 AI Pulse” Report

KPMG’s newly released 2025 Q2 AI Pulse Report signals a pivotal inflection point in the enterprise adoption of Agentic AI. According to the report, 68% of large enterprises (with over 1,000 employees) have implemented agent-based AI in their operations, while 33% of all surveyed companies have adopted the technology. This trend illustrates a strategic shift from experimental exploration to operational deployment of generative AI, positioning intelligent agents as core enablers of operational efficiency and revenue growth.

Core Propositions and Key Trends

1. Accelerated Commercialization: From Pilots to Production-Grade Deployments

With 68% of large enterprises and 33% of all companies having deployed Agentic AI, it is evident that intelligent agents are transitioning from proof-of-concept trials to being deeply embedded in core business functions. No longer peripheral tools, agents are now integral to automation, customer interaction, operations, and analytics—serving as “intelligent engines” driving responsiveness and efficiency. This shift from “usable” to “in-use” marks the deepening of enterprise digital transformation.

2. Efficiency and Revenue as Dual Drivers: The Business Value of AI Agents

The report highlights that 46% of companies prioritize “efficiency gains and revenue growth” as primary objectives for adopting AI agents. This reflects the intense need to both reduce costs and drive new value amid complex market dynamics. Intelligent agents automate repetitive, rule-based tasks, freeing human capital for creative and strategic roles. Simultaneously, they deliver actionable insights, enhance decision-making, and enable personalized services—unlocking new revenue streams. The focus on tangible business outcomes is the primary accelerator of enterprise-wide adoption.

3. Digital Culture and Organizational Evolution: A New Human-Machine Paradigm

The deployment of Agentic AI extends beyond technology—it fundamentally reshapes organizational structures, data flows, access control, and employee roles. Nearly 90% of executives surveyed anticipate a transformation of performance metrics, and 87% recognize the need for upskilling. This underscores a growing consensus that human-AI collaboration will be the new norm. Enterprises must foster a digital culture centered on “co-work between humans and agents,” supported by initiatives such as prompt engineering training and sandbox-based agent simulations, to enable synergistic productivity rather than substitution.

Product and Use Case Insights: Lessons from HaxiTAG

As an enterprise GenAI solution provider, HaxiTAG has operationalized Agentic AI across industries, offering concrete examples of how agents act not just as tools, but as workflow re-shapers and decision assistants.

EiKM – Enterprise Intelligent Knowledge Management
EiKM leverages agents to automate knowledge curation and enable multi-role QA assistants, advancing traditional KM from “information automation” to “cognitive collaboration.” Through multimodal semantic parsing, contextual routing engines, and the AICMS middleware, agents are seamlessly integrated into enterprise systems—enhancing customer service responsiveness and internal learning outcomes.
ESGtank – ESG Intelligent Strategy System
While technical documentation is limited, ESGtank embeds policy-responsive agents that assist with real-time adaptation to regulatory changes and ESG disclosure recommendations. This reflects the potential of Agentic AI in complex compliance and strategy domains, facilitating closed-loop ESG management, reducing risk, and enhancing corporate reputation.
Yueli Knowledge Computation Engine
This engine automates end-to-end workflows from data ingestion to insight delivery. With advanced multimodal comprehension, the Yueli-KGM module, and a multi-model coordination framework, it enables intelligent orchestration of data flows via tasklets and visual pipelines. In finance and government domains, it empowers knowledge distillation and decision support from massive datasets.

Collectively, these cases underscore that agents are evolving into autonomous, context-aware actors that drive enterprise intelligence from data-driven processes to knowledge-centered systems.

Strategic Commentary and Recommendations

To harness Agentic AI as a sustainable competitive advantage, enterprises must align across four dimensions:

Embedded Deployment
Agents must be fully integrated into core business processes rather than isolated in sandbox environments. Only through end-to-end automation can their transformative potential be realized.
Explainability, Security, and Alignment with Governance
As agents assume greater decision-making authority, transparency, logic traceability, data security, and permission control are essential. A robust AI governance framework must ensure compliance with ethics, laws, and internal policies.
Human-Agent Collaborative Culture
Agents should empower, not replace. Enterprises must invest in training and change management to cultivate a workforce capable of co-creating with AI, thus fostering a virtuous cycle of learning and innovation.
From ROI to Organizational Intelligence Maturity
Traditional ROI metrics fail to capture the long-term strategic value of Agentic AI. A multidimensional maturity framework—spanning efficiency, innovation, risk control, employee engagement, and market positioning—should be adopted.

KPMG’s report provides a realistic blueprint for Agentic AI deployment, highlighting the shift from simple tools to autonomous collaborators, and from local process optimization to enterprise-wide synergy.

Conclusion

Driven by generative AI and intelligent agents, the next-generation enterprise will exhibit unprecedented capabilities in real-time coordination and adaptive intelligence. Forward-looking organizations must proactively establish agent-compatible processes, align business and governance models, and embrace human-AI synergy. This is not merely a response to disruption—but a foundational strategy to build lasting, future-ready competitiveness.

To build enterprise-grade AI agent systems and enable knowledge-driven workflow automation, HaxiTAG offers comprehensive solutions such as EiKM, ESGtank, Yueli Engine, and HaxiTAG BotFactory for scalable deployment and intelligent transformation.

The Core Logic of AI-Driven Digital-Intelligent Transformation Anchored in Business Problems

July 21, 2025

As enterprises transition from digitalization to intelligence, the value of data and AI has moved beyond technical capabilities alone—it now hinges on whether they can effectively identify and resolve real-world business challenges. In this context, formulating the right problem has become the first principle of AI empowerment.

From “Owning Data” to “Problem Orientation”: An Evolution in Strategic Thinking

Traditional views often fall into the trap of “the more data, the better.” However, from the perspective of intelligent operations, the true value of data lies in its relevance to the problem at hand. HaxiTAG’s Yueli Knowledge Computing Engine embraces a “task-oriented data flow” design, where data assets and knowledge services are automatically orchestrated around specific business tasks and scenarios, ensuring precise alignment with enterprise needs. When formulating a data strategy, companies must first build a comprehensive business problem repository, and then backtrack to determine the necessary data and model capabilities—thus avoiding the pitfalls of data bloat and inefficient analysis.

Intelligent Application of Data Scenarios: From Static Assets to Dynamic Agents

Four key scenarios—asset management, energy management, spatial analytics, and tenant prediction—have already demonstrated tangible outcomes through HaxiTAG’s ESGtank system and enterprise intelligent IoT platform. For example:

In energy management, IoT devices and AI models collaborate to monitor energy consumption, automatically optimizing consumption curves based on building behavior patterns.
In tenant analytics, HaxiTAG integrates geographic mobility data, surrounding facilities, and historical lease behavior into a composite feature graph, significantly improving the F1-score of tenant retention prediction models.

All of these point toward a key shift: data should serve as perceptive input for intelligent agents—not just static content in reports.

Building Data Platforms and Intelligent Foundations: Integration as Cognitive Advancement

To continually unlock the value of data, enterprises must develop integrated, standardized, and intelligent data infrastructures. HaxiTAG’s AI middleware platform enables multi-modal data ingestion and unified semantic modeling, facilitating seamless transformation from raw physical data to semantic knowledge graphs. It also provides intelligent Agents and CoPilots to assist business users with question-answering and decision support—an embodiment of “platform as capability augmentation.”

Furthermore, the convergence of “data + knowledge” is becoming a foundational principle in future platform architecture. By integrating a knowledge middle platform with data lakehouse architecture, enterprises can significantly enhance the accuracy and interpretability of AI algorithms, thereby building more trustworthy intelligent systems.

Driving Organizational Synergy and Cultural Renewal: Intelligent Talent Reconfiguration

AI projects are not solely the domain of technical teams. At the organizational level, HaxiTAG has implemented “business-data-tech triangle teams” across multiple large-scale deployments, enabling business goals to directly guide data engineering tasks. These are supported by the EiKM enterprise knowledge management system, which fosters knowledge collaboration and task transparency—ensuring cross-functional communication and knowledge retention.

Crucially, strategic leadership involvement is essential. Senior executives must align on the value of “data as a core asset,” as this shared conviction lays the groundwork for organizational transformation and cultural evolution.

From “No-Regret Moves” to Continuous Intelligence Optimization

Digital-intelligent transformation should not aim for instant overhaul. Enterprises should begin with measurable, quick-win initiatives. For instance, a HaxiTAG client in the real estate sector first achieved ROI breakthroughs through tenant churn prediction, before expanding to energy optimization and asset inventory management—gradually constructing a closed-loop intelligent operations system.

Ongoing feedback and model iteration, driven by real-time behavioral data, are the only sustainable ways to align data strategies with business dynamics.

Conclusion

The journey toward AI-powered intelligent operations is not about whether a company “has AI,” but whether it is anchoring its transformation in real business problems—building an intelligent system powered jointly by data, knowledge, and organizational capabilities. Only through this approach can enterprises truly evolve from “data availability” to “actionable intelligence”, and ultimately maximize business value.

Four Core Steps to AI-Powered Procurement Transformation: Maturity Assessment, Build-or-Buy Decisions, Capability Enablement, and Value Capture

July 16, 2025

Applying artificial intelligence (AI) in procurement is not an overnight endeavor—it requires a systematic approach through four core steps. First, organizations must assess their digital maturity to identify current pain points and opportunities. Second, they must make informed decisions between buying off-the-shelf solutions and building custom systems. Third, targeted upskilling and change management are essential to equip teams to embrace new technologies. Finally, AI should be used to capture sustained financial value through improved data analytics and negotiation strategies. This article draws on industry-leading practices and cutting-edge research to unpack each step, helping procurement leaders navigate their AI transformation journey with confidence.

Digital Maturity Assessment

Before embarking on AI adoption, companies must conduct a comprehensive evaluation of their digital maturity to accurately locate both challenges and opportunities. AI maturity models provide a strategic roadmap for procurement leaders by assessing the current state of technological infrastructure, team capabilities, and process digitalization. These insights help define a realistic evolution path based on gaps and readiness.

McKinsey recommends a dual-track approach—rapidly deploying AI and analytics use cases that generate quick wins, while simultaneously building a scalable data platform to support long-term needs. Similarly, DNV’s AI maturity framework emphasizes benchmarking organizational vision against industry standards to help companies set priorities from a holistic perspective and avoid becoming isolated “technology islands.”

Technology: Buy or Build?

One of the most strategic decisions in implementing AI is choosing between purchasing ready-made solutions or building custom systems. Off-the-shelf solutions offer faster time-to-value, mature interfaces, and lower technical entry barriers—but they often fall short in addressing the unique nuances of procurement functions.

Conversely, organizations with greater AI ambitions may opt to build proprietary systems to achieve deeper control over spend transparency, contract optimization, and ESG goal alignment. However, this approach demands significant in-house capabilities in data engineering and algorithm development, along with careful consideration of long-term maintenance costs versus strategic benefits.

Forbes emphasizes that AI success hinges not only on the technology itself but also on factors such as user trust, ease of adoption, and alignment with long-term strategy—key dimensions that are frequently overlooked in the build-vs-buy debate. Additionally, the initial cost and future iteration expenses of AI solutions must be factored into decision-making to prevent unmanageable ROI gaps later on.

Upskilling the Team

AI doesn't just accelerate existing procurement processes—it redefines them. As such, upskilling procurement teams is paramount. According to BCG, only 10% of AI’s value comes from algorithms, 20% from data and platforms, and a staggering 70% from people adapting to new ways of working and being motivated to learn.

Economist Impact reports that 64% of enterprises have already adopted AI tools in procurement. This transformation requires current employees to gain proficiency in data analytics and decision support, while also bringing in new roles such as data scientists and AI engineers. Leaders must foster a culture of experimentation and continuous learning through robust change management and transparent communication to ensure skill development is fully realized.

The Hackett Group further notes that the most critical future skills for procurement professionals include advanced analytics, risk assessment, and cross-functional collaboration. These competencies will empower teams to excel in complex negotiations and supplier management. Supply Chain Management Review highlights that AI also democratizes learning for budget-constrained companies, enabling them to adopt and refine new technologies through hands-on experience.

Capturing Value from Suppliers

The ultimate goal of AI adoption in procurement is to translate technical capabilities into measurable business value—generating negotiation insights through advanced analytics, optimizing contract terms, and even encouraging suppliers to adopt generative AI to reduce total supply chain costs.

BCG’s research shows that a successful AI transformation can yield cost savings of 15% to 45% across select categories of products and services. The key lies in seamlessly integrating AI into procurement workflows and delivering an exceptional initial user experience to drive ongoing adoption and scalability. Sustained value capture also depends on strong executive commitment, regular KPI evaluation, and active promotion of success stories—ensuring that AI transformation becomes an enduring engine of enterprise growth.

Conclusion

In today’s hypercompetitive market landscape, AI-driven procurement transformation is no longer optional—it is essential. It offers a vital pathway to securing future competitive advantages and building core capabilities. At Hashitag, we are committed to guiding procurement teams through every stage of the transformation journey, from maturity assessment and technology decisions to workforce enablement and continuous value realization. We hope this four-step framework provides a clear roadmap for organizations to unlock the full potential of intelligent procurement and thrive in the digital era.

AI Automation: A Strategic Pathway to Enterprise Intelligence in the Era of Task Reconfiguration

July 13, 2025

With the rapid advancement of generative AI and task-level automation, the impact of AI on the labor market has gone far beyond the simplistic notion of "job replacement." It has entered a deeper paradigm of task reconfiguration and value redistribution. This transformation not only reshapes job design but also profoundly reconstructs organizational structures, capability boundaries, and competitive strategies. For enterprises seeking intelligent transformation and enhanced service and competitiveness, understanding and proactively embracing this change is no longer optional—it is a strategic imperative.

The "Dual Pathways" of AI Automation: Structural Transformation of Jobs and Skills

AI automation is reshaping workforce structures along two main pathways:

Routine Automation (e.g., customer service responses, schedule planning, data entry): By replacing predictable, rule-based tasks, automation significantly reduces labor demand and improves operational efficiency. A clear outcome is the decline in job quantity and the rise in skill thresholds. For instance, British Telecom’s plan to cut 40% of its workforce and Amazon’s robot fleet surpassing its human workforce exemplify enterprises adjusting the human-machine ratio to meet cost and service response imperatives.
Complex Task Automation (e.g., roles involving analysis, judgment, or interaction): Automation decomposes knowledge-intensive tasks into standardized, modular components, expanding employment access while lowering average wages. Job roles like telephone operators or rideshare drivers are emblematic of this "commoditization of skills." Research by MIT reveals that a one standard deviation drop in task specialization correlates with an 18% wage decrease—even as employment in such roles doubles, illustrating the tension between scaling and value compression.

For enterprises, this necessitates a shift from role-centric to task-centric job design, and a comprehensive recalibration of workforce value assessment and incentive systems.

Task Reconfiguration as the Engine of Organizational Intelligence: Not Replacement, but Reinvention

When implementing AI automation, businesses must discard the narrow view of “human replacement” and adopt a systems approach to task reengineering. The core question is not who will be replaced, but rather:

Which tasks can be automated?
Which tasks require human oversight?
Which tasks demand collaborative human-AI execution?

By clearly classifying task types and redistributing responsibilities accordingly, enterprises can evolve into truly human-machine complementary organizations. This facilitates the emergence of a barbell-shaped workforce structure: on one end, highly skilled "super-individuals" with AI mastery and problem-solving capabilities; on the other, low-barrier task performers organized via platform-based models (e.g., AI operators, data labelers, model validators).

Strategic Recommendations:

Accelerate automation of procedural roles to enhance service responsiveness and cost control.
Reconstruct complex roles through AI-augmented collaboration, freeing up human creativity and judgment.
Shift organizational design upstream, reshaping job archetypes and career development around “task reengineering + capability migration.”

Redistribution of Competitive Advantage: Platform and Infrastructure Players Reshape the Value Chain

AI automation is not just restructuring internal operations—it is redefining the industry value chain.

Platform enterprises (e.g., recruitment or remote service platforms) have inherent advantages in standardizing tasks and matching supply with demand, giving them control over resource allocation.
AI infrastructure providers (e.g., model developers, compute platforms) build strategic moats in algorithms, data, and ecosystems, exerting capability lock-in effects downstream.

To remain competitive, enterprises must actively embed themselves within the AI ecosystem, establishing an integrated “technology–business–talent” feedback loop. The future of competition lies not between individual companies, but among ecosystems.

Societal and Ethical Considerations: A New Dimension of Corporate Responsibility

AI automation exacerbates skill stratification and income inequality, particularly in low-skill labor markets, where “new structural unemployment” is emerging. Enterprises that benefit from AI efficiency gains must also fulfill corresponding responsibilities:

Support workforce skill transition through internal learning platforms and dual-capability development (“AI literacy + domain expertise”).
Participate in public governance by collaborating with governments and educational institutions to promote lifelong learning and career retraining systems.
Advance AI ethics governance to ensure fairness, transparency, and accountability in deployment, mitigating hidden risks such as algorithmic bias and data discrimination.

AI Is Not Destiny, but a Matter of Strategic Choice

As one industry mentor aptly stated, “AI is not fate—it is choice.” How a company defines which tasks are delegated to AI essentially determines its service model, organizational form, and value positioning. The future will not be defined by “AI replacing humans,” but rather by “humans redefining themselves through AI.”

Only by proactively adapting and continuously evolving can enterprises secure their strategic advantage in this era of intelligent reconfiguration.

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

April 08, 2025

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:

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

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

March 28, 2025

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.

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

March 19, 2025

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

News Information Retrieval: Users seek AI-powered search tools to quickly access news reports, original article links, and key insights.
Decision Support: Businesses and individuals utilize LLMs for market research, industry trend analysis, and forecasting.
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.
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.
Chatbot & Virtual Assistant: Improve the relevance of your chatbot and virtual assistant answers, and make your user experience personalized and content-rich dialogue.
Internal knowledge management: Empower employees through personalized and accurate answers based on enterprise knowledge, reduce search time and improve productivity.
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:

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.
Introducing Explainability and Refusal Mechanisms: Implementing transparent models that enable LLMs to reject uncertain queries rather than generating misleading responses.
Optimizing Generative Search Citation Management: Refining LLM strategies for URL and citation generation to prevent invalid links and fabricated content, improving traceability.
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.
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.
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.

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

January 10, 2025

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

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.
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.
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.
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.
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

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.
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.
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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Thursday, July 31, 2025

Digital Maturity Assessment

Buy vs. Build: Technology Decision-Making

Capability Enablement and Team Empowerment

Capturing Value from Suppliers

Conclusion

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3. Digital Culture and Organizational Evolution: A New Human-Machine Paradigm

Product and Use Case Insights: Lessons from HaxiTAG

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From “No-Regret Moves” to Continuous Intelligence Optimization

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Digital Maturity Assessment

Technology: Buy or Build?

Upskilling the Team

Capturing Value from Suppliers

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1. Strategic Impacts

2. Enhanced Operational Intelligence

3. Data Security and Privacy Protection

4. Workforce Transformation

5. Broad Adoption of AI Tools

6. HashTag EiKM's Innovative Practices

Future Challenges and Considerations

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Friday, March 28, 2025

Integrating Data, AI, and Large Models

1. Intelligent Data Analysis

2. Enhancing Large Models with Data

3. Developing Data-Driven Intelligent Applications

Core Application Scenarios of AI and Large Models

1. Intelligent Decision Support

2. Smart Marketing & Customer Intelligence

3. Intelligent Supply Chain Management

4. Enterprise Process Automation

5. Data-Driven Product Innovation

How AI and Large Models Enable Intelligent Decision-Making

1. Data-Driven Intelligent Recommendations

2. Enhancing Business Intelligence (BI) with Large Models

3. AI-Driven Risk Management & Forecasting

Business Automation & Intelligence

How AI, Data, and Large Models Drive Enterprise Innovation

1. Establishing AI Experimentation Platforms

2. Industry-Specific Large Models

3. Building AI + Data Ecosystems

Challenges and Risks

1. Data Security & Privacy Compliance

2. Data Quality & Model Bias

3. Technical Complexity & Deployment Challenges

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