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Friday, July 18, 2025

OpenAI’s Seven Key Lessons and Case Studies in Enterprise AI Adoption

July 18, 2025

AI is Transforming How Enterprises Work

OpenAI recently released a comprehensive guide on enterprise AI deployment, openai-ai-in-the-enterprise.pdf, based on firsthand experiences from its research, application, and deployment teams. It identified three core areas where AI is already delivering substantial and measurable improvements for organizations:

Enhancing Employee Performance: Empowering employees to deliver higher-quality output in less time
Automating Routine Operations: Freeing employees from repetitive tasks so they can focus on higher-value work
Enabling Product Innovation: Delivering more relevant and responsive customer experiences

However, AI implementation differs fundamentally from traditional software development or cloud deployment. The most successful organizations treat AI as a new paradigm, adopting an experimental and iterative approach that accelerates value creation and drives faster user and stakeholder adoption.

OpenAI’s integrated approach — combining foundational research, applied model development, and real-world deployment — follows a rapid iteration cycle. This means frequent updates, real-time feedback collection, and continuous improvements to performance and safety.

Seven Key Lessons for Enterprise AI Deployment

Lesson 1: Start with Rigorous Evaluation
Case: How Morgan Stanley Ensures Quality and Safety through Iteration

As a global leader in financial services, Morgan Stanley places relationships at the core of its business. Faced with the challenge of introducing AI into highly personalized and sensitive workflows, the company began with rigorous evaluations (evals) for every proposed use case.

Evaluation is a structured process that assesses model performance against benchmarks within specific applications. It also supports continuous process improvement, reinforced with expert feedback at each step.

In its early stages, Morgan Stanley focused on improving the efficiency and effectiveness of its financial advisors. The hypothesis was simple: if advisors could retrieve information faster and reduce time spent on repetitive tasks, they could provide more and better insights to clients.

Three initial evaluation tracks were launched:

Translation Accuracy: Measuring the quality of AI-generated translations
Summarization: Evaluating AI’s ability to condense information using metrics for accuracy, relevance, and coherence
Human Comparison: Comparing AI outputs to expert responses, scored on accuracy and relevance

Results: Today, 98% of Morgan Stanley advisors use OpenAI tools daily. Document access has increased from 20% to 80%, and search times have dropped dramatically. Advisors now spend more time on client relationships, supported by task automation and faster insights. Feedback has been overwhelmingly positive — tasks that once took days now take hours.

Lesson 2: Embed AI into Products
Case: How Indeed Humanized Job Matching

AI’s strength lies in handling vast datasets from multiple sources, enabling companies to automate repetitive work while making user experiences more relevant and personalized.

Indeed, the world’s largest job site, now uses GPT-4o mini to redefine job matching.

The “Why” Factor: Recommending good-fit jobs is just the beginning — it’s equally important to explain why a particular role is suggested.

By leveraging GPT-4o mini’s analytical and language capabilities, Indeed crafts natural-language explanations in its messages and emails to job seekers. Its popular "invite to apply" feature also explains how a candidate’s background makes them a great fit.

When tested against the prior matching engine, the GPT-powered version showed:

A 20% increase in job application starts
A 13% improvement in downstream hiring success

Given that Indeed sends over 20 million messages monthly and serves 350 million visits, these improvements translate to major business impact.

Scaling posed a challenge due to token usage. To improve efficiency, OpenAI and Indeed fine-tuned a smaller model that achieved similar results with 60% fewer tokens.

Helping candidates understand why they’re a fit for a role is a deeply human experience. With AI, Indeed is enabling more people to find the right job faster — a win for everyone.

Lesson 3: Start Early, Invest Ahead of Time
Case: Klarna’s Compounding Returns from AI Adoption

AI solutions rarely work out-of-the-box. Use cases grow in complexity and impact through iteration. Early adoption helps organizations realize compounding gains.

Klarna, a global payments and shopping platform, launched a new AI assistant to streamline customer service. Within months, the assistant handled two-thirds of all service chats — doing the work of hundreds of agents and reducing average resolution time from 11 to 2 minutes. It’s expected to drive $40 million in profit improvement, with customer satisfaction scores on par with human agents.

This wasn’t an overnight success. Klarna achieved these results through constant testing and iteration.

Today, 90% of Klarna’s employees use AI in their daily work, enabling faster internal launches and continuous customer experience improvements. By investing early and fostering broad adoption, Klarna is reaping ongoing returns across the organization.

Lesson 4: Customize and Fine-Tune Models
Case: How Lowe’s Improved Product Search

The most successful enterprises using AI are those that invest in customizing and fine-tuning models to fit their data and goals. OpenAI has invested heavily in making model customization easier — through both self-service tools and enterprise-grade support.

OpenAI partnered with Lowe’s, a Fortune 50 home improvement retailer, to improve e-commerce search accuracy and relevance. With thousands of suppliers, Lowe’s deals with inconsistent or incomplete product data.

Effective product search requires both accurate descriptions and an understanding of how shoppers search — which can vary by category. This is where fine-tuning makes a difference.

By fine-tuning OpenAI models, Lowe’s achieved:

A 20% improvement in labeling accuracy
A 60% increase in error detection

Fine-tuning allows organizations to train models on proprietary data such as product catalogs or internal FAQs, leading to:

Higher accuracy and relevance
Better understanding of domain-specific terms and user behavior
Consistent tone and voice, essential for brand experience or legal formatting
Faster output with less manual review

Lesson 5: Empower Domain Experts
Case: BBVA’s Expert-Led AI Adoption

Employees often know their problems best — making them ideal candidates to lead AI-driven solutions. Empowering domain experts can be more impactful than building generic tools.

BBVA, a global banking leader with over 125,000 employees, launched ChatGPT Enterprise across its operations. Employees were encouraged to explore their own use cases, supported by legal, compliance, and IT security teams to ensure responsible use.

“Traditionally, prototyping in companies like ours required engineering resources,” said Elena Alfaro, Global Head of AI Adoption at BBVA. “With custom GPTs, anyone can build tools to solve unique problems — getting started is easy.”

In just five months, BBVA staff created over 2,900 custom GPTs, leading to significant time savings and cross-departmental impact:

Credit risk teams: Faster, more accurate creditworthiness assessments
Legal teams: Handling 40,000+ annual policy and compliance queries
Customer service teams: Automating sentiment analysis of NPS surveys

The initiative is now expanding into marketing, risk, operations, and more — because AI was placed in the hands of people who know how to use it.

Lesson 6: Remove Developer Bottlenecks
Case: Mercado Libre Accelerates AI Development

In many organizations, developer resources are the primary bottleneck. When engineering teams are overwhelmed, innovation slows, and ideas remain stuck in backlogs.

Mercado Libre, Latin America's largest e-commerce and fintech company, partnered with OpenAI to build Verdi, a developer platform powered by GPT-4o and GPT-4o mini.

Verdi integrates language models, Python, and APIs into a scalable, unified platform where developers use natural language as the primary interface. This empowers 17,000 developers to build consistently high-quality AI applications quickly — without deep code dives. Guardrails and routing logic are built-in.

Key results include:

100x increase in cataloged products via automated listings using GPT-4o mini Vision
99% accuracy in fraud detection through daily evaluation of millions of product listings
Multilingual product descriptions adapted to regional dialects
Automated review summarization to help customers understand feedback at a glance
Personalized notifications that drive engagement and boost recommendations

Next up: using Verdi to enhance logistics, reduce delivery delays, and tackle more high-impact problems across the enterprise.

Lesson 7: Set Bold Automation Goals
Case: How OpenAI Automates Its Own Work

At OpenAI, we work alongside AI every day — constantly discovering new ways to automate our own tasks.

One challenge was our support team’s workflow: navigating systems, understanding context, crafting responses, and executing actions — all manually.

We built an internal automation platform that layers on top of existing tools, streamlining repetitive tasks and accelerating insight-to-action workflows.

First use case: Working on top of Gmail to compose responses and trigger actions. The platform pulls in relevant customer data and support knowledge, then embeds results into emails or takes actions like opening support tickets.

By integrating AI into daily workflows, the support team became more efficient, responsive, and customer-centric. The platform now handles hundreds of thousands of tasks per month — freeing teams to focus on higher-impact work.

It all began because we chose to set bold automation goals, not settle for inefficient processes.

Key Takeaways

As these OpenAI case studies show, every organization has untapped potential to use AI for better outcomes. Use cases may vary by industry, but the principles remain universal.

The Common Thread: AI deployment thrives on open, experimental thinking — grounded in rigorous evaluation and strong safety measures. The best-performing companies don’t rush to inject AI everywhere. Instead, they align on high-ROI, low-friction use cases, learn through iteration, and expand based on that learning.

The Result: Faster and more accurate workflows, more personalized customer experiences, and more meaningful work — as people focus on what humans do best.

We’re now seeing companies automate increasingly complex workflows — often with AI agents, tools, and resources working in concert to deliver impact at scale.

AI-Driven Software Development Transformation at Rakuten with Claude Code

June 30, 2025

Rakuten has achieved a transformative overhaul of its software development process by integrating Anthropic’s Claude Code, resulting in the following significant outcomes:

Claude Code demonstrated autonomous programming for up to seven continuous hours in complex open-source refactoring tasks, achieving 99.9% numerical accuracy;
New feature delivery time was reduced from an average of 24 working days to just 5 days, cutting time-to-market by 79%;
Developer productivity increased dramatically, enabling engineers to manage multiple tasks concurrently and significantly boost output.

Case Overview, Core Concepts, and Innovation Highlights

This transformation not only elevated development efficiency but also established a pioneering model for enterprise-grade AI-driven programming.

Application Scenarios and Effectiveness Analysis

1. Team Scale and Development Environment

Rakuten operates across more than 70 business units including e-commerce, fintech, and digital content, with thousands of developers serving millions of users. Claude Code effectively addresses challenges posed by multilingual, large-scale codebases, optimizing complex enterprise-grade development environments.

2. Workflow and Task Types

Workflows were restructured around Claude Code, encompassing unit testing, API simulation, component construction, bug fixing, and automated documentation generation. New engineers were able to onboard rapidly, reducing technology transition costs.

3. Performance and Productivity Outcomes

Development Speed: Feature delivery time dropped from 24 days to just 5, representing a breakthrough in efficiency;
Code Accuracy: Complex technical tasks were completed with up to 99.9% numerical precision;
Productivity Gains: Engineers managed concurrent task streams, enabling parallel development. Core tasks were prioritized by developers while Claude handled auxiliary workstreams.

4. Quality Assurance and Team Collaboration

AI-driven code review mechanisms provided real-time feedback, improving code quality. Automated test-driven development (TDD) workflows enhanced coding practices and enforced higher quality standards across the team.

Strategic Implications and AI Adoption Advancements

From Assistive Tool to Autonomous Producer: Claude Code has evolved from a tool requiring frequent human intervention to an autonomous “programming agent” capable of sustaining long-task executions, overcoming traditional AI attention span limitations.
Building AI-Native Organizational Capabilities: Even non-technical personnel can now contribute via terminal interfaces, fostering cross-functional integration and enhancing organizational “AI maturity” through new collaborative models.
Unleashing Innovation Potential: Rakuten has scaled AI utility from small development tasks to ambient agent-level automation, executing monorepo updates and other complex engineering tasks via multi-threaded conversational interfaces.
Value-Driven Deployment Strategy: Rakuten prioritizes AI tool adoption based on value delivery speed and ROI, exemplifying rational prioritization and assurance pathways in enterprise digital transformation.

The Outlook for Intelligent Evolution

By adopting Claude Code, Rakuten has not only achieved a leap in development efficiency but also validated AI’s progression from a supportive technology to a core component of process architecture. This case highlights several strategic insights:

AI autonomy is foundational to driving both efficiency and innovation;
Process reengineering is the key to unlocking organizational potential with AI;
Cross-role collaboration fosters a new ecosystem, breaking down technical silos and making innovation velocity a sustainable competitive edge.

This case offers a replicable blueprint for enterprises across industries: by building AI-centric capability frameworks and embedding AI across processes, roles, and architectures, organizations can accumulate sustained performance advantages, experiential assets, and cultural transformation — ultimately elevating both organizational capability and business value in tandem.

Leveraging o1 Pro Mode for Strategic Market Entry: A Stepwise Deep Reasoning Framework for Complex Business Decisions

April 29, 2025

Below is a comprehensive, practice-oriented guide for using the o1 Pro Mode to construct a stepwise market strategy through deep reasoning, especially suitable for complex business decision-making. It integrates best practices, operational guidelines, and a simulated case to demonstrate effective use, while also accounting for imperfections in ASR and spoken inputs.

Context & Strategic Value of o1 Pro Mode

In high-stakes business scenarios characterized by multi-variable complexity, long reasoning chains, and high uncertainty, conventional AI often falls short due to its preference for speed over depth. The o1 Pro Mode is purpose-built for these conditions. It excels in:

Deep logical reasoning (Chain-of-Thought)
Multistep planning
Structured strategic decomposition

Use cases include:

Market entry feasibility studies
Product roadmap & portfolio optimization
Competitive intelligence
Cross-functional strategy synthesis (marketing, operations, legal, etc.)

Unlike fast-response models (e.g., GPT-4.0, 4.5), o1 Pro emphasizes rigorous reasoning over quick intuition, enabling it to function more like a “strategic analyst” than a conversational bot.

Step-by-Step Operational Guide

Step 1: Input Structuring to Avoid ASR and Spoken Language Pitfalls

Goal: Transform raw or spoken-language queries (which may be ambiguous or disjointed) into clearly structured, interrelated analytical questions.

Recommended approach:

Define a primary strategic objective
e.g., “Assess the feasibility of entering the Japanese athletic footwear market.”
Decompose into sub-questions:
- Market size, CAGR, segmentation
- Consumer behavior and cultural factors
- Competitive landscape and pricing benchmarks
- Local legal & regulatory challenges
- Go-to-market and branding strategy

✅ Best Practice: Number each question and provide context-rich framing. For example:
"1. Market Size: What is the total addressable market for athletic shoes in Japan over the next 5 years?"

Step 2: Triggering Chain-of-Thought Reasoning in o1 Pro

o1 Pro Mode processes tasks in logical stages, such as:

Identifying problem variables
Cross-referencing knowledge domains
Sequentially generating intermediate insights
Synthesizing a coherent strategic output

Prompting Tips:

Explicitly request “step-by-step reasoning” or “display your thought chain.”
Ask for outputs using business frameworks, such as:
- SWOT Analysis
- Porter’s Five Forces
- PESTEL
- Ansoff Matrix
- Customer Journey Mapping

Step 3: First Draft Strategy Generation & Human Feedback Loop

After o1 Pro generates the initial strategy, implement a structured verification process:

Dimension	Validation Focus	Prompt Example
Logical Consistency	Are insights connected and arguments sound?	“Review consistency between conclusions.”
Data Reasonability	Are claims backed by evidence or logical inference?	“List data sources or assumptions used.”
Local Relevance	Does it reflect cultural and behavioral nuances?	“Consider localization and cultural factors.”
Strategic Coherence	Does the plan span market entry, growth, risks?	“Generate a GTM roadmap by stage.”

Step 4: Action Plan Decomposition & Operationalization

Goal: Convert insights into a realistic, trackable implementation roadmap.

Recommended Outputs:

Execution timeline: 0–3 months, 3–6 months, 6–12 months
RACI matrix: Assign roles and responsibilities
KPI dashboard: Track strategic progress and validate assumptions

Prompts:

“Convert the strategy into a 6-month execution plan with milestones.”
“Create a KPI framework to measure strategy effectiveness.”
“List resources needed and risk mitigation strategies.”

✅ Deliverables may include: Gantt charts, OKR tables, implementation matrices.

Example: Sneaker Company Entering Japan

Scenario: A mid-sized sneaker brand is evaluating expansion into Japan.

Phase	Activity
1	Input 12 structured questions into o1 Pro (market, competitors, culture, etc.)
2	Model takes 3 minutes to produce a stepwise reasoning path & structured report
3	Outputs include market sizing, consumer segments, regulatory insights
4	Strategy synthesized into SWOT, Five Forces, and GTM roadmap
5	Output refined with human expert feedback and used for board review

Error Prevention & Optimization Strategies

Common Pitfall	Remediation Strategy
ASR/Spoken language flaws	Manually refine transcribed input into structured form
Contextual disconnection	Reiterate background context in prompt
Over-simplified answers	Require explicit reasoning chain and framework output
Outdated data	Request public data references or citation of assumptions
Execution gap	Ask for KPI tracking, resource list, and risk controls

Conclusion: Strategic Value of o1 Pro

o1 Pro Mode is not just a smarter assistant—it is a scalable strategic reasoning tool. It reduces the time, complexity, and manpower traditionally required for high-quality business strategy development. By turning ambiguous spoken questions into structured, multistep insights and executable action plans, o1 Pro empowers individuals and small teams to operate at strategic consulting levels.

For full-scale deployment, organizations can template this workflow for verticals such as:

Consumer goods internationalization
Fintech regulatory strategy
ESG and compliance market planning
Tech product market fit and roadmap design

Let me know if you’d like a custom prompt set or reusable template for your team.

Performance of Multi-Trial Models and LLMs: A Direct Showdown between AI and Human Engineers

December 01, 2024

With the rapid development of generative AI, particularly Large Language Models (LLMs), the capabilities of AI in code reasoning and problem-solving have significantly improved. In some cases, after multiple trials, certain models even outperform human engineers on specific tasks. This article delves into the performance trends of different AI models and explores the potential and limitations of AI when compared to human engineers.

Performance Trends of Multi-Trial Models

In code reasoning tasks, models like O1-preview and O1-mini have consistently shown outstanding performance across 1-shot, 3-shot, and 5-shot tests. Particularly in the 3-shot scenario, both models achieved a score of 0.91, with solution rates of 87% and 83%, respectively. This suggests that as the number of prompts increases, these models can effectively improve their comprehension and problem-solving abilities. Furthermore, these two models demonstrated exceptional resilience in the 5-shot scenario, maintaining high solution rates, highlighting their strong adaptability to complex tasks.

In contrast, models such as Claude-3.5-sonnet and GPT-4.0 performed slightly lower in the 3-shot scenario, with scores of 0.61 and 0.60, respectively. While they showed some improvement with fewer prompts, their potential for further improvement in more complex, multi-step reasoning tasks was limited. Gemini series models (such as Gemini-1.5-flash and Gemini-1.5-pro), on the other hand, underperformed, with solution rates hovering between 0.13 and 0.38, indicating limited improvement after multiple attempts and difficulty handling complex code reasoning problems.

The Impact of Multiple Prompts

Overall, the trend indicates that as the number of prompts increases from 1-shot to 3-shot, most models experience a significant boost in score and problem-solving capability, particularly O1 series and Claude-3.5-sonnet. However, for some underperforming models, such as Gemini-flash, even with additional prompts, there was no substantial improvement. In some cases, especially in the 5-shot scenario, the model's performance became erratic, showing unstable fluctuations.

These performance differences highlight the advantages of certain high-performance models in handling multiple prompts, particularly in their ability to adapt to complex tasks and multi-step reasoning. For example, O1-preview and O1-mini not only displayed excellent problem-solving ability in the 3-shot scenario but also maintained a high level of stability in the 5-shot case. In contrast, other models, such as those in the Gemini series, struggled to cope with the complexity of multiple prompts, exhibiting clear limitations.

Comparing LLMs to Human Engineers

When comparing the average performance of human engineers, O1-preview and O1-mini in the 3-shot scenario approached or even surpassed the performance of some human engineers. This demonstrates that leading AI models can improve through multiple prompts to rival top human engineers. Particularly in specific code reasoning tasks, AI models can enhance their efficiency through self-learning and prompts, opening up broad possibilities for their application in software development.

However, not all models can reach this level of performance. For instance, GPT-3.5-turbo and Gemini-flash, even after 3-shot attempts, scored significantly lower than the human average. This indicates that these models still need further optimization to better handle complex code reasoning and multi-step problem-solving tasks.

Strengths and Weaknesses of Human Engineers

AI models excel in their rapid responsiveness and ability to improve after multiple trials. For specific tasks, AI can quickly enhance its problem-solving ability through multiple iterations, particularly in the 3-shot and 5-shot scenarios. In contrast, human engineers are often constrained by time and resources, making it difficult for them to iterate at such scale or speed.

However, human engineers still possess unparalleled creativity and flexibility when it comes to complex tasks. When dealing with problems that require cross-disciplinary knowledge or creative solutions, human experience and intuition remain invaluable. Especially when AI models face uncertainty and edge cases, human engineers can adapt flexibly, while AI may struggle with significant limitations in these situations.

Future Outlook: The Collaborative Potential of AI and Humans

While AI models have shown strong potential for performance improvement with multiple prompts, the creativity and unique intuition of human engineers remain crucial for solving complex problems. The future will likely see increased collaboration between AI and human engineers, particularly through AI-Assisted Frameworks (AIACF), where AI serves as a supporting tool in human-led engineering projects, enhancing development efficiency and providing additional insights.

As AI technology continues to advance, businesses will be able to fully leverage AI's computational power in software development processes, while preserving the critical role of human engineers in tasks requiring complexity and creativity. This combination will provide greater flexibility, efficiency, and innovation potential for future software development processes.

Conclusion

The comparison of multi-trial models and LLMs highlights both the significant advancements and the challenges AI faces in the coding domain. While AI performs exceptionally well in certain tasks, particularly after multiple prompts, top models can surpass some human engineers. However, in scenarios requiring creativity and complex problem-solving, human engineers still maintain an edge. Future success will rely on the collaborative efforts of AI and human engineers, leveraging each other's strengths to drive innovation and transformation in the software development field.

Navigating the AI Landscape: Ensuring Infrastructure, Privacy, and Security in Business Transformation

November 30, 2024

In today's rapidly evolving digital era, businesses are embracing artificial intelligence (AI) at an unprecedented pace. This trend is not only transforming the way companies operate but also reshaping industry standards and technical protocols. However, the success of AI implementation goes far beyond technical innovation in model development. The underlying infrastructure, along with data security and privacy protection, is a decisive factor in whether companies can stand out in this competitive race.

The Regulatory Challenge of AI Implementation

When introducing AI applications, businesses face not only technical challenges but also the constantly evolving regulatory requirements and industry standards. With the widespread use of generative AI and large language models, issues of data privacy and security have become increasingly critical. The vast amount of data required for AI model training serves as both the "fuel" for these models and the core asset of the enterprise. Misuse or leakage of such data can lead to legal and regulatory risks and may erode the company's competitive edge. Therefore, businesses must strictly adhere to data compliance standards while using AI technologies and optimize their infrastructure to ensure that privacy and security are maintained during model inference.

Optimizing AI Infrastructure for Successful Inference

AI infrastructure is the cornerstone of successful model inference. Companies developing AI models must prioritize the data infrastructure that supports them. The efficiency of AI inference depends on real-time, large-scale data processing and storage capabilities. However, latency during inference and bandwidth limitations in data flow are major bottlenecks in today's AI infrastructure. As model sizes and data demands grow, these bottlenecks become even more pronounced. Thus, optimizing the infrastructure to support large-scale model inference and reduce latency is a key technical challenge that businesses must address.

Opportunities and Challenges Presented by Generative AI

The rise of generative AI brings both new opportunities and challenges to companies undergoing digital transformation. Generative AI has the potential to greatly enhance data prediction, automated decision-making, and risk management, particularly in areas like DevOps and security operations, where its application holds immense promise. However, generative AI also amplifies the risks of data privacy breaches, as proprietary data used in model training becomes a prime target for attacks. To mitigate this risk, companies must establish robust security and privacy frameworks to ensure that sensitive information is not exposed during model inference. This requires not only stronger defense mechanisms at the technical level but also strategic compliance with the highest industry standards and regulatory requirements regarding data usage.

Learning from Experience: The Importance of Data Management

Past experiences reveal that the early stages of AI model data collection have paved the way for future technological breakthroughs, particularly in the management of proprietary data. A company's success may hinge on how well it safeguards these valuable assets, preventing competitors from indirectly gaining access to confidential information through AI models. AI model competitiveness lies not only in technical superiority but also in the data backing and security assurance. As such, businesses need to build hybrid cloud technologies and distributed computing architectures to optimize their data infrastructure, enabling them to meet the demands of future large-scale AI model inference.

The Future Role of AI in Security and Efficiency

Looking ahead, AI will not only serve as a tool for automation and efficiency improvement but also play a pivotal role in data privacy and security defense. As the attack surface expands, AI tools themselves may become a crucial part of the automation in security defenses. By leveraging generative AI to optimize detection and prediction, companies will be better positioned to prevent potential security threats and enhance their competitive advantage.

Conclusion

The successful application of AI hinges not only on cutting-edge technological innovation but also on sustained investments in data infrastructure, privacy protection, and security compliance. Companies that can effectively utilize generative AI to optimize business processes while protecting core data through comprehensive privacy and security frameworks will lead the charge in this wave of digital transformation.

HaxiTAG's Solutions

HaxiTAG offers a comprehensive suite of generative AI solutions, achieving efficient human-computer interaction through its data intelligence component, automatic data accuracy checks, and multiple functionalities. These solutions significantly enhance management efficiency, decision-making quality, and productivity. HaxiTAG's offerings include LLM and GenAI applications, private AI, and applied robotic automation, helping enterprise partners leverage their data knowledge assets, integrate heterogeneous multimodal information, and combine advanced AI capabilities to support fintech and enterprise application scenarios, creating value and growth opportunities.

Driven by LLM and GenAI, HaxiTAG Studio organizes bot sequences, creates feature bots, feature bot factories, and adapter hubs to connect external systems and databases for any function. These innovations not only enhance enterprise competitiveness but also open up more development opportunities for enterprise application scenarios.

Deep Analysis of Large Language Model (LLM) Application Development: Tactics and Operations

October 18, 2024

With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become one of the most prominent technologies today. LLMs not only demonstrate exceptional capabilities in natural language processing but also play an increasingly significant role in real-world applications across various industries. This article delves deeply into the core strategies and best practices of LLM application development from both tactical and operational perspectives, providing developers with comprehensive guidance.

Key Tactics

The Art of Prompt Engineering

Prompt engineering is one of the most crucial skills in LLM application development. Well-crafted prompts can significantly enhance the quality and relevance of the model’s output. In practice, we recommend the following strategies:

Precision in Task Description: Clearly and specifically describe task requirements to avoid ambiguity.
Diversified Examples (n-shot prompting): Provide at least five diverse examples to help the model better understand the task requirements.
Iterative Optimization: Continuously adjust prompts based on model output to find the optimal form.

Application of Retrieval-Augmented Generation (RAG) Technology

RAG technology effectively extends the knowledge boundaries of LLMs by integrating external knowledge bases, while also improving the accuracy and reliability of outputs. When implementing RAG, consider the following:

Real-Time Integration of Knowledge Bases: Ensure the model can access the most up-to-date and relevant external information during inference.
Standardization of Input Format: Standardize input formats to enhance the model’s understanding and processing efficiency.
Design of Output Structure: Create a structured output format that facilitates seamless integration with downstream systems.

Comprehensive Process Design and Evaluation Strategies

A successful LLM application requires not only a powerful model but also meticulous process design and evaluation mechanisms. We recommend:

Constructing an End-to-End Application Process: Carefully plan each stage, from data input and model processing to result verification.
Establishing a Real-Time Monitoring System: Quickly identify and resolve issues within the application to ensure system stability.
Introducing a User Feedback Mechanism: Continuously optimize the model and process based on real-world usage to improve user experience.

Operational Guidelines

Formation of a Professional Team

The success of LLM application development hinges on an efficient, cross-disciplinary team. When assembling a team, consider the following:

Diverse Talent Composition: Combine professionals from various backgrounds, such as data scientists, machine learning engineers, product managers, and system architects. Alternatively, consider partnering with professional services like HaxiTAG, an enterprise-level LLM application solution provider.
Fostering Team Collaboration: Establish effective communication mechanisms to encourage knowledge sharing and the collision of innovative ideas.
Continuous Learning and Development: Provide ongoing training opportunities for team members to maintain technological acumen.

Flexible Deployment Strategies

In the early stages of LLM application, adopting flexible deployment strategies can effectively control costs while validating product-market fit:

Prioritize Cloud Resources: During product validation, consider using cloud services or leasing hardware to reduce initial investment.
Phased Expansion: Gradually consider purchasing dedicated hardware as the product matures and user demand grows.
Focus on System Scalability: Design with future expansion needs in mind, laying the groundwork for long-term development.

Importance of System Design and Optimization

Compared to mere model optimization, system-level design and optimization are more critical to the success of LLM applications:

Modular Architecture: Adopt a modular design to enhance system flexibility and maintainability.
Redundancy Design: Implement appropriate redundancy mechanisms to improve system fault tolerance and stability.
Continuous Optimization: Optimize system performance through real-time monitoring and regular evaluations to enhance user experience.

Conclusion

Developing applications for large language models is a complex and challenging field that requires developers to possess deep insights and execution capabilities at both tactical and operational levels. Through precise prompt engineering, advanced RAG technology application, comprehensive process design, and the support of professional teams, flexible deployment strategies, and excellent system design, we can fully leverage the potential of LLMs to create truly valuable applications.

However, it is also essential to recognize that LLM application development is a continuous and evolving process. Rapid technological advancements, changing market demands, and the importance of ethical considerations require developers to maintain an open and learning mindset, continuously adjusting and optimizing their strategies. Only in this way can we achieve long-term success in this opportunity-rich and challenging field.

AI Revolutionizes Retail: Walmart’s Path to Enhanced Productivity

October 10, 2024

As a global retail giant, Walmart is reshaping its business model through artificial intelligence (AI) technology, leading industry transformation. This article delves into how Walmart utilizes AI, particularly Generative AI (GenAI), to enhance productivity, optimize customer experience, and drive global business innovation.

1. Generative AI: The Core Engine of Efficiency

Walmart has made breakthrough progress in applying Generative AI. According to CEO Doug McMillon’s report, GenAI enables the company to update 850 million product catalog entries at 100 times the speed of traditional methods. This achievement showcases the immense potential of AI in data processing and content generation:

Automated Data Updates: GenAI significantly reduces manual operations and error rates.
Cost Efficiency: Automation of processes has markedly lowered data management costs.
Real-Time Response: The rapid update capability allows Walmart to promptly adjust product information, enhancing market responsiveness.

2. AI-Driven Personalized Customer Experience

Walmart has introduced AI-based search and shopping assistants, revolutionizing its e-commerce platform:

Smart Recommendations: AI algorithms analyze user behavior to provide precise, personalized product suggestions.
Enhanced Search Functionality: AI assistants improve the search experience, increasing product discoverability.
Increased Customer Satisfaction: Personalized services greatly boost customer satisfaction and loyalty.

3. Market Innovation: AI-Powered New Retail Models

Walmart is piloting AI-driven seller experiences in the U.S. market, highlighting the company’s forward-thinking approach to retail innovation:

Optimized Seller Operations: AI technology is expected to enhance seller operational efficiency and sales performance.
Enhanced Platform Ecosystem: Improving seller experiences through AI helps attract more high-quality merchants.
Competitive Advantage: This innovative initiative aids Walmart in maintaining its leading position in the competitive e-commerce landscape.

4. Global AI Strategy: Pursuing Efficiency and Consistency

Walmart plans to extend AI technology across its global operations, a grand vision that underscores the company’s globalization strategy:

Standardized Operations: AI technology facilitates standardized business processes across different regions.
Cross-Border Collaboration: Global AI applications will enhance information sharing and collaboration across regions.
Scale Efficiency: Deploying AI globally maximizes returns on technological investments.

5. Human-AI Collaboration: A New Paradigm for Future Work

With the widespread application of AI, Walmart faces new challenges in human resource management:

Skill Upgradation: The company needs to invest in employee training to adapt to an AI-driven work environment.
Redefinition of Jobs: Some traditional roles may be automated, but new job opportunities will also be created.
Human-AI Collaboration: Optimizing the collaboration between human employees and AI systems to leverage their respective strengths.

Conclusion

By strategically applying AI technology, especially Generative AI, Walmart has achieved significant advancements in productivity, customer experience, and business innovation. This not only solidifies Walmart’s leadership in the retail sector but also sets a benchmark for the industry’s digital transformation. However, with the rapid advancement of technology, Walmart must continue to innovate to address market changes and competitive pressures. In the future, finding a balance between technological innovation and human resource management will be a key issue for Walmart and other retail giants. Through ongoing investment in AI technology, fostering a culture of innovation, and focusing on employee development, Walmart is poised to continue leading the industry in the AI-driven retail era, delivering superior and convenient shopping experiences for consumers.

Automation and Artificial Intelligence: An Innovative Approach to New Product Data Processing on E-Commerce Platforms

October 08, 2024

In the e-commerce sector, the process of listing new products often involves extensive data input and organization. Traditionally, these tasks required significant manual labor, including product names, descriptions, categorization, and image processing. However, with advancements in artificial intelligence (AI) and automation technologies, these cumbersome tasks can now be addressed more efficiently. Recently, an e-commerce platform launched 450 new products, but only had product photos available with no descriptions or metadata. In response, the development of a custom AI automation tool to extract and generate complete product information has emerged as an innovative solution.

How the Automation Tool Works

We have developed an advanced automation system that analyzes each product image to extract all possible information and generate product drafts. These drafts include product names, stock keeping units (SKUs), brief and detailed descriptions, SEO meta titles and descriptions, features, attributes, categories, image links, and alternative text for images. The core of the system lies in its precise image analysis capabilities, which rely on finely tuned prompts to ensure that every piece of information extracted from the image is as accurate and detailed as possible.

Technical Challenges and Solutions

One of the most challenging aspects of creating this automation system was optimizing the prompts to extract key information from images. Image data is inherently unstructured, meaning that extracting information requires in-depth analysis of the images combined with advanced machine learning algorithms. For example, OpenAI Vision, as the core technology for image analysis, can identify specific objects in images and convert them into structured data. To ensure the security and accessibility of this data, the results are saved in JSON format and stored in Google Sheets.

Setting up this system took two days, but once completed, it processed all 450 products in just four hours. In comparison, manual processing would have required 15 to 20 minutes per product, totaling approximately 110 to 150 hours of labor. Thus, this automation method significantly enhanced production efficiency, reduced human errors, and saved substantial time and costs.

Customer Needs and Industry Transformation

The client's understanding of AI and automation has been crucial in driving this innovation. Recognizing the limitations of traditional methods, the client actively sought technological solutions to address these issues. This demand led us to explore and implement this AI-based automation approach. While traditional automation can improve productivity, its combination with AI further transforms the industry landscape. AI not only enhances the accuracy of automation but also demonstrates unparalleled efficiency in handling complex and large-scale data.

Implementation and Tools

In implementing this automation process, we used several tools to ensure a smooth workflow. Initially, image data was retrieved from a directory in Google Drive and analyzed using OpenAI Vision. The analysis results were provided in JSON format and securely stored in Google Sheets. Finally, products were created using the WooCommerce module, and product IDs were updated back into Google Sheets. This series of steps not only accelerated data processing but also ensured the accuracy and integrity of the data.

Future Outlook

This AI-based automation tool showcases the tremendous potential of artificial intelligence technology in e-commerce data processing. As technology continues to advance and optimize, such tools will become even smarter and more efficient. They will help businesses save costs and time while enhancing data processing accuracy and consistency. With the ongoing progress in AI technology, it is anticipated that this innovative automation solution will become a standard fixture in the e-commerce industry, driving the sector towards greater efficiency and intelligence.

In conclusion, the integration of AI and automation provides an unprecedented solution for new product data processing on e-commerce platforms. Through this technology, businesses can significantly improve operational efficiency, reduce labor costs, and deliver higher quality services to customers. This innovation not only demonstrates the power of technology but also sets a new benchmark for the future development of e-commerce.

Overview of JPMorgan Chase's LLM Suite Generative AI Assistant

October 06, 2024

JPMorgan Chase has recently launched its new generative AI assistant, LLM Suite, marking a significant breakthrough in the banking sector's digital transformation. Utilizing advanced language models from OpenAI, LLM Suite aims to enhance employee productivity and work efficiency. This move not only reflects JPMorgan Chase's gradual adoption of artificial intelligence technologies but also hints at future developments in information processing and task automation within the banking industry.

Key Insights and Addressed Issues

Productivity Enhancement

One of LLM Suite’s primary goals is to significantly boost employee productivity. By automating repetitive tasks such as email drafting, document summarization, and creative generation, LLM Suite reduces the time employees spend on these routine activities, allowing them to focus more on strategic work. This shift not only optimizes workflows but also enhances overall work efficiency.

Information Processing Optimization

In areas such as marketing, customer itinerary management, and meeting summaries, LLM Suite helps employees process large volumes of information more quickly and accurately. The AI tool ensures accurate transmission and effective utilization of information through intelligent data analysis and automated content generation. This optimization not only speeds up information processing but also improves data analysis accuracy.

Solutions and Core Methods

Automated Email Drafting

Method

LLM Suite uses language models to analyze the context of email content and generate appropriate responses or drafts.

Steps

Input Collection: Employees input email content and relevant background information into the system.
Content Analysis: The AI model analyzes the email’s subject and intent.
Response Generation: The system generates contextually appropriate responses or drafts.
Optimization and Adjustment: The system provides editing suggestions, which employees can adjust according to their needs.

Document Summarization

Method

The AI generates concise document summaries by extracting key content.

Steps

Document Input: Employees upload the documents that need summarizing.
Model Analysis: The AI model extracts the main points and key information from the documents.
Summary Generation: A clear and concise document summary is produced.
Manual Review: Employees check the accuracy and completeness of the summary.

Creative Generation

Method

Generative models provide inspiration and creative suggestions for marketing campaigns and proposals.

Steps

Input Requirements: Employees provide creative needs or themes.
Creative Generation: The model generates related creative ideas and suggestions based on the input.
Evaluation and Selection: Employees evaluate multiple creative options and select the most suitable one.

Customer Itinerary and Meeting Summaries

Method

Automatically organize and summarize customer itineraries and meeting content.

Steps

Information Collection: The system retrieves meeting records and customer itinerary information.
Information Extraction: The model extracts key decision points and action items.
Summary Generation: Easy-to-read summaries of meetings or itineraries are produced.

Practical Usage Feedback and Workflow

Employee Feedback

Positive Feedback: Many employees report that LLM Suite has significantly reduced the time spent on repetitive tasks, enhancing work efficiency. The automation features of the AI tool help them quickly complete tasks such as handling numerous emails and documents, allowing more focus on strategic work.
Improvement Suggestions: Some employees noted that AI-generated content sometimes lacks personalization and contextual relevance, requiring manual adjustments. Additionally, employees would like the model to better understand industry-specific and internal jargon to improve content accuracy.

Workflow Description

Initiation: Employees log into the system and select the type of task to process (e.g., email, document summarization).
Input: Based on the task type, employees upload or input relevant information or documents.
Processing: LLM Suite uses OpenAI’s model for content analysis, generation, or summarization.
Review: Generated content is presented to employees for review and necessary editing.
Output: The finalized content is saved or sent, completing the task.

Practical Experience Guidelines

Clearly Define Requirements: Clearly define task requirements and expected outcomes to help the model generate more appropriate content.
Regularly Assess Effectiveness: Regularly review the quality of generated content and make necessary adjustments and optimizations.
User Training: Provide training to employees to ensure they can effectively use the AI tool and improve work efficiency.
Feedback Mechanism: Establish a feedback mechanism to continuously gather user experiences and improvement suggestions for ongoing tool performance and user experience optimization.

Limitations and Constraints

Data Privacy and Security: Ensure data privacy and security when handling sensitive information, adhering to relevant regulations and company policies.
Content Accuracy: Although AI can generate high-quality content, there may still be errors, necessitating manual review and adjustments.
Model Dependence: Relying on a single generative model may lead to content uniformity and limitations; multiple tools and strategies should be used to address the model’s shortcomings.

The launch of LLM Suite represents a significant advancement for JPMorgan Chase in the application of AI technology. By automating and optimizing routine tasks, LLM Suite not only boosts employee efficiency but also improves the speed and accuracy of information processing. However, attention must be paid to data privacy, content accuracy, and model dependence. Employee feedback indicates that while AI tools greatly enhance efficiency, manual review of generated content remains crucial for ensuring quality and relevance. With ongoing optimization and adjustments, LLM Suite is poised to further advance JPMorgan Chase’s and other financial institutions’ digital transformation success.

Decline in ESG Fund Launches: Reflections and Prospects Amid Market Transition

September 10, 2024

Recently, there has been a significant slowdown in the issuance of ESG funds by some of the world's leading asset management companies. According to data provided by Morningstar Direct, companies such as BlackRock, Deutsche Bank's DWS Group, Invesco, and UBS have seen a sharp reduction in the number of new ESG fund launches this year. This trend reflects a cooling attitude towards the ESG label in financial markets, influenced by changes in the global political and economic landscape affecting ESG fund performance.

Current Status Analysis

Sharp Decline in Issuance Numbers

As of the end of May 2024, only about 100 ESG funds have been launched globally, compared to 566 for the entire year of 2023 and 993 in 2022. In May of this year alone, only 16 new ESG funds were issued, marking the lowest monthly issuance since early 2020. This data indicates a significant slowdown in the pace of ESG fund issuance.

Multiple Influencing Factors

Political and Regulatory Pressure: In the United States, ESG is under political attack from the Republican Party, with bans and lawsuit threats being frequent. In Europe, stricter ESG fund naming rules have forced some passively managed portfolios to drop the ESG label.
Poor Market Performance: High inflation, high interest rates, and a slump in clean energy stocks have led to poor performance of ESG funds. Those that perform well are often heavily weighted in tech stocks, which have questionable ESG attributes.
Changes in Product Design and Market Demand: Due to poor product design and more specific market demand for ESG funds, many investors are no longer interested in broad ESG themes but are instead looking for specific climate solutions or funds focusing on particular themes such as net zero or biodiversity.

Corporate Strategy Adjustments

Facing these challenges, some asset management companies have chosen to reduce the issuance of ESG funds. BlackRock has launched only four ESG funds this year, compared to 36 in 2022 and 23 last year. DWS has issued three ESG funds this year, down from 25 in 2023. Invesco and UBS have also seen significant reductions in ESG fund launches.

However, some companies view this trend as a sign of market maturity. Christoph Zschaetzsch, head of product development at DWS Group, stated that the current "white space" for ESG products has reduced, and the market is entering a "normalization" phase. This means the focus of ESG fund issuance will shift to fine-tuning and adjusting existing products.

Investors' Lessons

Huw van Steenis, partner and vice chair at Oliver Wyman, pointed out that the sharp decline in ESG fund launches is due to poor market performance, poor product design, and political factors. He emphasized that investors have once again learned that allocating capital based on acronyms is not a sustainable strategy.

Prospects

Despite the challenges, the prospects for ESG funds are not entirely bleak. Some U.S.-based ESG ETFs have posted returns of over 20% this year, outperforming the 18.8% rise of the S&P 500. Additionally, French asset manager Amundi continues its previous pace, having launched 14 responsible investment funds in 2024, and plans to expand its range of net-zero strategies and ESG ETFs, demonstrating a long-term commitment and confidence in ESG.

The sharp decline in ESG fund issuance reflects market transition and adjustment. Despite facing multiple challenges such as political, economic, and market performance issues, the long-term prospects for ESG funds remain. In the future, asset management companies need to more precisely meet specific investor demands and innovate in product design and market strategy to adapt to the ever-changing market environment.

TAGS:

ESG fund issuance decline, ESG investment trends 2024, political impact on ESG funds, ESG fund performance analysis, ESG fund market maturity, ESG product design challenges, regulatory pressure on ESG funds, ESG ETF performance 2024, sustainable investment prospects, ESG fund market adaptation

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