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Wednesday, October 15, 2025

Enterprise Generative AI Investment Strategy and Evaluation Framework from HaxiTAG’s Perspective

October 15, 2025

In today’s rapidly evolving business environment, Artificial Intelligence (AI), particularly Generative AI, is reshaping industries at an unprecedented pace. As the CMO of HaxiTAG, we recognize both the opportunities and challenges enterprises face amidst the digital transformation wave. This report aims to provide an in-depth analysis of the necessity, scientific rationale, and foresight behind enterprise investments in Generative AI, drawing upon HaxiTAG’s practical experience and leading global research findings, to offer partners an actionable best-practice framework.

The Necessity of Generative AI Investment: A Strategic Imperative for a New Era

The global economy is undergoing a profound transformation driven by Generative AI. Enterprises are shifting their focus from asking “whether to adopt AI” to “how quickly it can be deployed.” This transition has become the core determinant of market competitiveness, reflecting not chance but the inevitability of systemic forces.

Reshaping Competitive Dimensions: Speed and Efficiency as Core Advantages

In the Generative AI era, competitiveness extends beyond traditional cost and quality toward speed and efficiency. A Google Cloud survey of 3,466 executives from 24 countries across companies with revenues over USD 10 million revealed that enterprises have moved from debating adoption to focusing on deployment velocity. Those capable of rapid experimentation and swift conversion of AI capabilities into productivity will seize significant first-mover advantages, while laggards risk obsolescence.

Generative AI Agents have emerged as the key enablers of this transformation. They not only achieve point-level automation but also orchestrate cross-system workflows and multi-role collaboration, reconstructing knowledge work and decision interfaces. As HaxiTAG’s enterprise AI transformation practice with Workday demonstrated, the introduction of the Agent System of Record (ASR)—which governs agent registration, permissions, costs, and performance—enabled enterprises to elevate productivity from tool-level automation to fully integrated role-based agents.

Shifting the Investment Focus: From Model Research to Productization and Operations

As Generative AI matures, investment priorities are shifting. Previously concentrated on model research, spending is now moving toward agent productization, operations, and integration. Google Cloud’s research shows that 13% of early adopters plan to allocate more than half of their AI budgets to agents. This signals that sustainable returns derive not from models alone, but from their transformation into products with service-level guarantees, continuous improvement, and compliance management.

HaxiTAG’s solutions, such as our Bot Factory, exemplify this shift. We enable enterprises to operationalize AI capabilities, supported by unified catalogs, observability, role and access management, budget control, and ROI tracking, ensuring effective deployment and governance of AI agents at scale.

The Advantage of Early Adopters: Success Is Beyond Technology

Google Cloud’s findings reveal that 88% of early adopters achieved ROI from at least one use case within a year, compared to an overall average of 74%. This highlights that AI success is not solely a technical challenge but the result of aligning use case selection, change execution, and governance. Early adopters succeed because they identify high-value use cases early, drive organizational change, and establish effective governance frameworks.

Walmart’s deployment of AI assistants such as Sparky and Ask Sam improved customer experiences and workforce productivity, while AI-enabled supply chain innovations—including drone delivery—delivered tangible business benefits. These cases underscore that AI investments succeed when technology is deeply integrated with business contexts and reinforced by execution discipline.

Acceleration of Deployment: Synergy of Technology and Organizational Experience

The time from AI ideation to production is shrinking. Google Cloud reports that 51% of organizations now achieve deployment within 3–6 months, compared to 47% in 2024. This acceleration is driven by maturing toolchains (pre-trained models, pipelines, low-code/agent frameworks) and accumulated organizational know-how, enabling faster validation of AI value and iterative optimization.

The Critical Role of C-Level Sponsorship: Executive Commitment as a Success Guarantee

The study found that 78% of organizations with active C-level sponsorship realized ROI from at least one Generative AI use case. Executive leadership is critical in removing cross-departmental barriers, securing budgets and data access, and ensuring organizational alignment. HaxiTAG emphasizes this by helping enterprises establish top-down AI strategies, anchored in C-level commitment.

In short, Generative AI investment is no longer optional—it is a strategic necessity for maintaining competitiveness and sustainable growth. HaxiTAG leverages its expertise in knowledge computation and AI agents to help partners seize this historic opportunity and accelerate transformation.

The Scientific and Forward-Looking Basis of Generative AI: The Engine of Future Business

Generative AI investment is not just a competitive necessity—it is grounded in strong scientific foundations and carries transformative implications for business models. Understanding its scientific underpinnings ensures accurate grasp of trends, while foresight reveals the blueprint for future growth.

Scientific Foundations: Emergent Intelligence from Data and Algorithms

Generative AI exhibits emergent capabilities through large-scale data training and advanced algorithmic models. These capabilities transcend automation, enabling reasoning, planning, and content creation. Core principles include:

Deep Learning and Large Models: Built on Transformer-based LLMs and Diffusion Models, trained on vast datasets to generate high-quality outputs. Walmart’s domain-specific “Wallaby” model exemplifies how verticalized AI enhances accuracy in retail scenarios.
Agentic AI: Agents simulate cognitive processes—perception, planning, action, reflection—becoming “digital colleagues” capable of complex, autonomous tasks. HaxiTAG’s Bot Factory operationalizes this by integrating registration, permissions, cost, and performance management into a unified platform.
Data-Driven Optimization: AI models enhance decision-making by identifying trends and correlations. Walmart’s Wally assistant, for example, analyzes sales data and forecasts inventory to optimize supply chain efficiency.

Forward-Looking Impact: Reshaping Business Models and Organizations

Generative AI will fundamentally reshape future enterprises, driving transformation in:

From Apps to Role-Based Agents: Human–AI interaction will evolve toward contextual, role-aware agents rather than application-driven workflows.
Digital Workforce Governance: AI agents will be managed as digital employees, integrated into budget, compliance, and performance frameworks.
Ecosystem Interoperability: Open agent ecosystems will enable cross-system and cross-organization collaboration through gateways and marketplaces.
Hyper-Personalization: Retail innovations such as AI-powered shopping agents will redefine customer engagement through personalized automation.
Organizational Culture: Enterprises must redesign roles, upskill employees, and foster AI collaboration to sustain transformation.

Notably, while global enterprises invested USD 30–40 billion in Generative AI, MIT reports that 95% have yet to realize commercial returns—underscoring that success depends not merely on model quality but on implementation and learning capacity. This validates HaxiTAG’s focus on agent governance and adaptive platforms as critical success enablers.

HaxiTAG’s Best-Practice Framework for Generative AI Investment

Drawing on global research and HaxiTAG’s enterprise service practice, we propose a comprehensive framework for enterprises:

Strategy First: Secure C-level sponsorship, define budgets and KPIs, and prioritize 2–3 high-value pilot use cases with measurable ROI within 3–6 months.
Platform as Foundation: Build an AI Agent platform with agent registration, observability, cost tracking, and orchestration capabilities.
Data as Core: Establish unified knowledge bases, real-time data pipelines, and robust governance.
Organization as Enabler: Redesign roles, train employees, and implement change management to ensure adoption.
Vendor Strategy: Adopt hybrid models balancing cost, latency, and compliance; prioritize providers offering explainability and operational toolchains.
Risk and Optimization: Manage cost overruns, ensure reliability, mitigate organizational resistance, and institutionalize performance measurement.

By following this framework, enterprises can scientifically and strategically invest in Generative AI, converting its potential into tangible business value. HaxiTAG is committed to partnering with organizations to pioneer this next chapter of intelligent transformation.

Conclusion

The Generative AI wave is irreversible. It represents not only a technological breakthrough but also a strategic opportunity for enterprises to achieve leapfrog growth. Research from Google Cloud and practices from HaxiTAG both demonstrate that agentification must become central to enterprise product and business transformation. This requires strong executive sponsorship, rapid use-case validation, scalable agent platforms, and integrated governance. Short-term goals should focus on pilot ROI within months, while medium-term goals involve scaling successful patterns into productized, operationalized agent ecosystems.

HaxiTAG will continue to advance the frontier of Generative AI, providing cutting-edge technology and professional solutions to help partners navigate the challenges and seize the opportunities of the intelligent era.

Core Challenges and Decision Models for Enterprise LLM Applications: Maximizing AI Potential

October 26, 2024

In today's rapidly advancing era of artificial intelligence, enterprise applications of large language models (LLMs) have become a hot topic. As an expert in decision-making models for enterprise LLM applications, I will provide you with an in-depth analysis of how to choose the best LLM solution for your enterprise to fully harness the potential of AI.

Core Challenges of Enterprise LLM Applications

The primary challenge enterprises face when applying LLMs is ensuring that the model understands and utilizes the enterprise's unique knowledge base. While general-purpose LLMs like ChatGPT are powerful, they are not trained on internal enterprise data. Directly using the enterprise knowledge base as context input is also not feasible, as most LLMs have token limitations that cannot accommodate a vast enterprise knowledge base.

Two Mainstream Solutions

To address this challenge, the industry primarily employs two methods:

(1) Fine-tuning Open Source LLMs This method involves fine-tuning open-source LLMs, such as Llama2, on the enterprise's corpus. The fine-tuned model can internalize and understand domain-specific knowledge of the enterprise, enabling it to answer questions without additional context. However, it's important to note that many enterprises' corpora are limited in size and may contain grammatical errors, which can pose challenges for fine-tuning.

(2) Retrieval-Augmented Generation (RAG) The RAG method involves chunking data, storing it in a vector database, and then retrieving relevant chunks based on the query to pass them to the LLM for answering questions. This method, which combines LLMs, vector storage, and orchestration frameworks, has been widely adopted in the industry.

Key Factors in RAG Solutions

The performance of RAG solutions depends on several factors:

Document Chunk Size: Smaller chunks may fail to answer questions requiring information from multiple paragraphs, while larger chunks quickly exhaust context length.
Adjacent Chunk Overlap: Proper overlap ensures that information is not abruptly cut off during chunking.
Embedding Technology: The algorithm used to convert chunks into vectors determines the relevance of retrieval.
Document Retriever: The database used to store embeddings and retrieve them with minimal latency.
LLM Selection: Different LLMs perform differently across datasets and scenarios.
Number of Chunks: Some questions may require information from different parts of a document or across documents.

Innovative Approaches by autoML

To address the above challenges, autoML has proposed an innovative automated approach:

Automated Iteration: Finds the best combination of parameters, including LLM fine-tuning, to fit specific use cases.
Evaluation Dataset: Requires only an evaluation dataset with questions and handcrafted answers.
Multi-dimensional Evaluation: Uses various metrics, such as BLEU, METEOR, BERT Score, and ROUGE Score, to assess performance.

Enterprise Decision Model

Based on the above analysis, I recommend the following decision model for enterprises when selecting and implementing LLM solutions:

(1) Requirement Definition: Clearly define the specific scenarios and goals for applying LLMs in the enterprise. (2) Data Assessment: Review the size, quality, and characteristics of the enterprise knowledge base. (3) Technology Selection:

For enterprises with small but high-quality datasets, consider fine-tuning open-source LLMs.
For enterprises with large or varied-quality datasets, the RAG method may be more suitable.
When feasible, combining fine-tuned LLMs and RAG may yield the best results. (4) Solution Testing: Use tools like autoML for automated testing and comparing the performance of different parameter combinations. (5) Continuous Optimization: Continuously adjust and optimize model parameters based on actual application outcomes.

Collaboration and Innovation

Implementing LLM solutions is not just a technical issue but requires cross-departmental collaboration:

IT Department: Responsible for technical implementation and system integration.
Business Department: Provides domain knowledge and defines specific application scenarios.
Legal and Compliance: Ensures data usage complies with privacy and security regulations.
Senior Management: Provides strategic guidance to ensure AI projects align with enterprise goals.

Through this comprehensive collaboration, enterprises can fully leverage the potential of LLMs to achieve true AI-driven innovation.

Enterprise LLM applications are a complex yet promising field. By deeply understanding the technical principles, adopting a scientific decision model, and promoting cross-departmental collaboration, enterprises can maintain a competitive edge in the AI era. We believe that as technology continues to advance and practical experience accumulates, LLMs will bring more innovative opportunities and value creation to enterprises.

HaxiTAG Studio is an enterprise-level LLM GenAI solution that integrates AIGC Workflow and privatization data fine-tuning. Through a highly scalable Tasklets pipeline framework, flexible AI 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 GenAI integration into enterprise applications for quick verification and implementation.

As a trusted LLM and GenAI industry application solution, HaxiTAG provides enterprise partners with LLM and GenAI application solutions, private AI, 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 AI capabilities to support fintech and enterprise application scenarios, creating value and growth opportunities.

HaxiTAG Studio, driven by LLM and GenAI, 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 GenAI industry applications, designed to supply enterprise partners with LLM and GenAI application solutions, private AI, 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 AI capabilities with enterprise application scenarios, creating value and development opportunities.

Developing LLM-based GenAI Applications: Addressing Four Key Challenges to Overcome Limitations
Analysis of AI Applications in the Financial Services Industry
Application of HaxiTAG AI in Anti-Money Laundering (AML)
Analysis of HaxiTAG Studio's KYT Technical Solution
Strategies and Challenges in AI and ESG Reporting for Enterprises: A Case Study of HaxiTAG
HaxiTAG ESG Solutions: Best Practices Guide for ESG Reporting
Impact of Data Privacy and Compliance on HaxiTAG ESG System

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