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

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.

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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:

1. 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.

2. Platform as Foundation: Build an AI Agent platform with agent registration, observability, cost tracking, and orchestration capabilities.

3. Data as Core: Establish unified knowledge bases, real-time data pipelines, and robust governance.

4. Organization as Enabler: Redesign roles, train employees, and implement change management to ensure adoption.

5. Vendor Strategy: Adopt hybrid models balancing cost, latency, and compliance; prioritize providers offering explainability and operational toolchains.

6. 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.

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Evaluation of LLMs: Systematic Thinking and Methodology

With the rapid development of Generative AI (GenAI), large language models (LLMs) like GPT-4 and GPT-3.5 have become increasingly prevalent in text generation and summarization tasks. However, evaluating the output quality of these models, particularly their summarizations, has become a crucial issue. This article explores the systematic thinking and methodology behind evaluating LLMs, using GenAI summarization tasks as an example. It aims to help readers better understand the core concepts and future potential of this field.

Key Points and Themes

Evaluating LLMs is not just a technical issue; it involves comprehensive considerations including ethics, user experience, and application scenarios. The primary goal of evaluation is to ensure that the summaries produced by the models meet the expected standards of relevance, coherence, consistency, and fluency to satisfy user needs and practical applications.

Importance of Evaluation

Evaluating the quality of LLMs helps to:

• Enhance reliability and interpretability: Through evaluation, we can identify and correct the model's errors and biases, thereby increasing user trust in the model.
• Optimize user experience: High-quality evaluation ensures that the generated content aligns more closely with user needs, enhancing user satisfaction.
• Drive technological advancement: Evaluation results provide feedback to researchers, promoting improvements in models and algorithms across the field.

Methodology and Research Framework

Evaluation Methods

Evaluating LLM quality requires a combination of automated tools and human review.

1 Automated Evaluation Tools

• ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Assesses the similarity of summaries to reference answers based on lexical and syntactic overlap. Suitable for evaluating the extractive quality of summaries.
• BERTScore: Based on word embeddings, it evaluates the semantic similarity of generated content, particularly useful for semantic-level evaluations.
• G-Eval: Uses LLMs themselves to evaluate content on aspects such as relevance, coherence, consistency, and fluency, providing a more nuanced evaluation.

2 Human Review

While automated tools can provide quick evaluation results, human review is indispensable for understanding context and capturing subtle differences. Human evaluators can calibrate the results from automated evaluations, offering more precise feedback.

Building Evaluation Datasets

High-quality evaluation datasets are the foundation of accurate evaluations. An ideal dataset should have the following characteristics:

• Reference answers: Facilitates comparison and assessment of model outputs.
• High quality and practical relevance: Ensures that the content in the dataset is representative and closely related to practical application scenarios.

Case Study: GenAI Summarization Tasks

In GenAI summarization tasks, the choice of different models and methods directly impacts the quality of the final summaries. The following are common summarization methods and their evaluations:

1 Summarization Methods

• Stuff: Uses a large context window to process all content, suitable for short, information-dense texts.
• Map Reduce: Segments large documents for processing, then merges summaries, suitable for complex long documents.
• Refine: Summarizes each part progressively, then merges, suitable for content requiring detailed analysis and refinement.

2 Application of Evaluation Methods

• Vicuna Model: Evaluates by scoring two model outputs on a scale of 1-10, useful for detailed comparison.
• AlpacaEval Leaderboard: Uses simple prompts with GPT-4-Turbo for evaluation, inclined towards user preference-oriented assessments.
• G-Eval: Adopts the AutoCoT strategy, generating evaluation steps and scores, improving evaluation accuracy.

Insights and Future Prospects

LLM evaluation plays a critical role in ensuring content quality and user experience. Future research should further refine evaluation methods, particularly in identifying human preferences and specialized evaluation prompts. As LLM technology advances, the precision and customization capabilities of models will significantly improve, bringing more possibilities for various industries.

Future Research Directions

• Diversified evaluation metrics: Beyond traditional metrics like ROUGE and BERTScore, explore more dimensions of evaluation, such as sentiment analysis and cultural adaptability.
• Cross-domain application evaluations: Evaluation methods must cater to the specific needs of different fields, such as law and medicine.
• User experience-oriented evaluations: Continuously optimize model outputs based on user feedback, enhancing user satisfaction.

Conclusion

Evaluating LLMs is a complex and multi-faceted task, encompassing technical, ethical, and user experience considerations. By employing systematic evaluation methods and a comprehensive research framework, we can better understand and improve the quality of LLM outputs, providing high-quality content generation services to a wide audience. In the future, as technology continues to advance, LLM evaluation methods will become more refined and professional, offering more innovation and development opportunities across various sectors.

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