AI Transformation Excellence for Semiconductor Industry via Logical AI Collaborative Innovation

With the rapid development of generative AI technology, especially with the rise of services like OpenAI's ChatGPT, Microsoft's Bing, and Google's Bard, various industries are undergoing a critical period of AI transformation. This article focuses on exploring AI transformation excellence strategies across departments in the cutting-edge semiconductor industry, utilizing AI and Machine Learning (ML) technologies. These generative AI technologies not only possess a robust knowledge base and data processing capabilities but more importantly, enable efficient human-machine interaction models for market analysis, demand forecasting, supply chain optimization, and the development of manufacturing platform technologies.

The semiconductor industry is currently in a phase of rapid change and intense competition, with technological advancement being a significant characteristic. As technology continuously evolves, the performance of chips is constantly improving, but this also brings complexity in design and manufacturing. Additionally, since the COVID-19 pandemic and in the current post-pandemic era, the global supply chain has been severely distorted and impacted, posing a significant challenge for the semiconductor industry, which relies heavily on global supply chains. Furthermore, semiconductors being a critical part of the global economy and security, geopolitical tensions, especially between countries like the USA, China, and others, pose challenges to the stability of the industry chain.

The volatility of market demand requires companies to quickly adapt to changes to maintain competitiveness. At the same time, as technology advances to higher levels, R&D costs significantly increase, posing a considerable challenge for many companies, especially those without sufficient funds for long-term investments. Another important aspect is environmental impact; as focus on sustainable development increases, companies need to find more environmentally friendly manufacturing methods. Additionally, as technology advances, the demand for skilled engineers and scientists increases, but the shortage of qualified talent could limit further industry development.

Addressing the aforementioned points, the semiconductor industry faces many challenges while embracing the rapid advancement of technology. Various departments within companies, such as business planning, operations, marketing, and production, are actively undergoing AI transformation and need to plan and introduce technology platforms for disruptive innovations in generative AI and ML.

In the book "AI Power: Logical and Collaborative Innovation ," the author proposes a new model that combines logical thinking with AI collaboration, showing high-efficiency potential across various industry sectors. By integrating AI's powerful knowledge base and natural language processing capabilities with human intuition and logical thinking, it significantly enhances efficiency and productivity in solving complex business and technical problems across various industries.

# Prior Article Note on Industry AI Transformation

In the previously published four articles, AI transformation strategies for two industries were suggested. Readers interested in these topics can refer to the following articles:

AI Transformation in Heavy Manufacturing Industry：

• 《AI Business Planning for Heavy Manufacturing via Logical AI Collaboration 》
• 《AI Factory Capacity Planning via Logical AI Collaborative Innovation 》
• 《AI Factory Machinery Maintenance via Logical AI Collaborative Innovation 》

AI Transformation in Finance and Insurance Industry：

• 《Enhancing ESG Strategies via Logical Collaboration with AI 》

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In the process of researching AI transformation in the semiconductor industry, I specifically focused on the AI transformation recommendations for Taiwan Semiconductor Manufacturing Company (TSMC), which holds the leading global market share in wafer foundry. I collected publicly available data from the internet, such as annual reports, company websites, and news, to conduct an in-depth analysis of its business planning, process technology, and platform architecture. Through AI collaborative cooperation, targeted solutions were proposed. Although the research is based on TSMC, the conclusions drawn can be widely applied to other semiconductor industries.

In the first part of the article, I will focus on discussing TSMC's AI transformation excellence strategy. This includes exploring how to use the three-step intuitive AI problem-solving approach and the logical structure of the Minto Pyramid in collaboration with AI. Recommendations will be made on how to use AI and ML technologies for cross-departmental AI transformation in business planning, process technology, and platform architecture.

In the second part, the Minto Pyramid logical tool used in the first part will be introduced in depth. Discussions will include the pyramid's logical structure, logical relationships, and the SCQA narrative flow. The article also suggests Minto Pyramid AI prompts that can guide AI more precisely and efficiently according to the pyramid's answer format, enhancing collaborative innovation.

In summary, this article aims to provide an in-depth perspective and analysis on how to combine the latest generative AI technology with logical thinking problem-solving approach to enhance AI transformation excellence planning in the semiconductor industry, such as TSMC. By using the structured logical thinking approach of the Minto Pyramid, we can assist the semiconductor industry in proposing its AI transformation excellence strategies with four directions of business planning, process technology, common AI platform, and operational strategies using generative AI and Machine Learning (ML).

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Part I: Collaborative AI Proposition of the Semiconductor Industry's AI Transformation Excellence Strategy

As mentioned above, in this research, I specifically focus on TSMC's situation, its advantages and challenges, and the final recommendations for its excellent AI transformation. The previous articles used a simple pyramid breakdown and structure. In this article, I use the three-layer logical structure model of the Minto Pyramid in collaboration with AI to propose solutions. Here are the three steps of the intuitive AI problem-solving method.

Initially, I collaborated with AI to explore TSMC's company profile as follows:

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AI Prompt:

Please tell me about Taiwan Semiconductor Manufacturing Company (TSMC) as a leading company in the semiconductor industry, including its highlights and business challenges, within 300 words.

ChatGPT

As TSMC (Taiwan Semiconductor Manufacturing Company) is one of the world's leading semiconductor manufacturing companies, headquartered in Taiwan. As a global semiconductor leader, TSMC is one of the largest and most significant semiconductor foundry manufacturers. The company specializes in manufacturing advanced semiconductor chips, providing process technology and production services to its clients.

Company Highlights:

• Advanced Process Technology: TSMC has consistently been at the forefront of semiconductor manufacturing technology, driving continuous industry development. The company offers a range of process technologies from 3 nanometers to 40 nanometers.
• Global Supply Chain: TSMC owns multiple advanced manufacturing plants worldwide, ensuring stable product production and delivery, supporting various applications including consumer electronics, communications, automotive electronics, and artificial intelligence.
• Environmental Responsibility: The company is committed to sustainable development, adopting eco-friendly measures to reduce energy consumption and carbon footprint while promoting green processes and the use of renewable energy.

Business Challenges:

• Technological Competition: The semiconductor industry is characterized by rapid technological changes and intense competition, requiring continual research and development and investment to maintain a technological edge.
• Supply Chain Uncertainty: Global supply chain issues and fluctuations in raw material prices can create uncertainties for company operations.
• International Trade Environment: International trade policies and geopolitical factors can impact the company's global business, necessitating proactive responses to changes.
• Talent Competition: The semiconductor industry requires highly specialized talent, and attracting and retaining skilled engineers and scientists is a significant challenge.

As an important player in the global semiconductor industry, TSMC is committed to continuous innovation, addressing business challenges, and playing a key role in semiconductor manufacturing. The company's global influence and technological leadership position it with an outstanding reputation in the technology and electronics sectors.

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From our analysis, we can identify two key directions that are crucial for a global leading semiconductor manufacturing company like TSMC.

Firstly, in business planning, TSMC shows precise market demand forecasting and strong supply chain management capabilities. As one of the world's largest semiconductor foundry manufacturers, TSMC not only excels in maintaining stable production and delivery but also effectively responds to the uncertainties and challenges of the global supply chain, ensuring its flexibility and responsiveness in a dynamic market.

Secondly, the lead in process technology is vital for maintaining a competitive edge in industry. TSMC's leading position in manufacturing technology, especially in advanced process technologies ranging from 3 nanometers to 40 nanometers, not only enhances the performance and reliability of its products but also drives the technological development and innovation of the entire semiconductor industry. This technological advantage allows TSMC to maintain a leading position amid increasing global competition.

These two key directions form the foundation for TSMC to continue playing a crucial role and maintain its technology leadership position in the global semiconductor industry. In this study, focusing on TSMC as the subject not only provides us with more targeted strategies but also reveals the success factors within the industry. Additionally, this article will continue using the intuitive AI problem-solving method introduced in previous articles and the Minto Pyramid framework to explore these strategies and solutions more deeply. Through this structured analysis method, we can better understand how TSMC addresses industry challenges and maintains its leadership in the global semiconductor market.

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1. Intuitive Experience Decomposition - For AI Transformation Excellence of Semiconductor Industry

• 2-4 Items and Others: In our in-depth analysis of TSMC, we utilized an AI collaborative cooperation model to extract key information, identifying two main aspects: business planning and manufacturing process technology. Based on these insights provided by AI, we further considered TSMC's strategic deployment. Combining my professional experience, I believe it is necessary to add a third aspect — the common AI technology platform. This aspect emphasizes the importance of the shared AI platform in the modern semiconductor manufacturing industry, especially in terms of fostering innovation, improving efficiency, and maintaining competitiveness.

• Minto Pyramid SCQA Narrative Flow:

o?? Scenario: TSMC is a leading global semiconductor wafer manufacturing company, as introduced on the company's website, in its planning, and in its annual financial reports.

o?? Complication: The imbalance of the supply chain in the post-pandemic era, the expansion of regional conflicts, and demands for localization of production and technical cooperation due to regional protectionism have led to unpredictability in business planning and global manufacturing.

o?? Question: How can TSMC utilize generative AI and Machine Learning (ML) to enhance its competitiveness? Please consider three aspects including business planning, manufacturing process technology, and the common AI platform. Also, suggest any other directions that could be considered.

o?? Answer: Please provide an AI collaborative response.

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2. AI Interaction and Collaboration - For AI Transformation Excellence of Semiconductor Industry

• First Step Content: TSMC is a leading global semiconductor wafer manufacturing company, as introduced on the company's website, in its planning, and in its annual financial reports. The imbalance of the supply chain in the post-pandemic era, the expansion of regional conflicts, and demands for localization of production and technical cooperation due to regional protectionism have led to unpredictability in business planning and global manufacturing. How can TSMC utilize generative AI and Machine Learning (ML) to enhance its competitiveness? Please consider three aspects including business planning, manufacturing process technology, and the common AI platform. Also, suggest any other directions that could be considered.
• Required Output Format: Use the specified Minto Pyramid structure to answer the question (for the format explanation, please refer to the introduction of the Minto Pyramid in Part II).

Here is the interaction with ChatGPT:

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AI Prompt:

?TSMC is a leading global semiconductor wafer manufacturing company, as introduced on the company's website, in its planning, and in its annual financial reports. The imbalance of the supply chain in the post-pandemic era, the expansion of regional conflicts, and demands for localization of production and technical cooperation due to regional protectionism have led to unpredictability in business planning and global manufacturing. How can TSMC utilize generative AI and Machine Learning (ML) to enhance its competitiveness? Please consider three aspects including business planning, manufacturing process technology, and the common AI platform. Also, suggest any other directions that could be considered. Use the specified Minto Pyramid structure to answer the question.

ChatGPT

Pyramid Level 1 - Primary Objective One: Optimization and Adaptability of Business Planning

Pyramid Level 2: Market Analysis and Forecasting

• Method 1: Dynamic Data Analysis - Utilizing advanced AI and ML tools for real-time analysis of extensive market data, capturing subtle market trends, and predicting future demand patterns.
• Method 2: Supply Chain Model Optimization - Applying machine learning techniques to optimize supply chain decisions, such as predicting logistics delays and fluctuations in raw material supplies, thereby more effectively managing inventory and production plans.
• Method 3: Risk Prediction - Using AI models to analyze and predict market, policy, and technological risks, providing comprehensive risk assessment and management strategies for businesses.
• Method 4: Customer Segmentation and Customization - Analyzing customer behavior data through deep learning algorithms to achieve precise market segmentation and provide more personalized products and services for different customer groups.

Pyramid Level 2: Risk Management and Response Strategies

• Method 1: Geopolitical Risk Analysis - Employing AI tools for in-depth analysis of the global political-economic environment, predicting potential impacts on business, and developing corresponding measures.
• Method 2: Supply Chain Redundancy Design - Optimizing supply chain design through machine learning to establish stronger redundancy mechanisms, reducing dependence on specific suppliers or regions.
• Method 3: Agile Response Mechanism - Building a rapid response mechanism with AI to quickly adjust operational strategies in the face of market fluctuations or supply chain disruptions.
• Method 4: Crisis Simulation Drills - Using AI for various crisis scenario simulations to assess potential impacts and develop proactive strategies, enhancing the company's ability to respond to emergencies.

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Pyramid Level 1 - Primary Objective Two: Innovation in Manufacturing Process Technology

Pyramid Level 2: Production Efficiency Enhancement

• Method 1: Smart Manufacturing Optimization - Applying AI and ML technologies in production processes for real-time monitoring and automatic adjustment of production parameters, improving efficiency and reducing costs.
• Method 2: Predictive Maintenance - Using machine learning to analyze production equipment data, identifying potential faults in advance, reducing unplanned downtime and extending equipment life.
• Method 3: Resource Optimization Allocation - Optimizing resource allocation on production lines with AI algorithms, including raw materials, energy, and human resources, to enhance efficiency and reduce waste.
• Method 4: Process Optimization - Continuously optimizing the manufacturing process with deep learning technology, improving product quality, reducing defects, and increasing output.

Pyramid Level 2: Quality Control and Improvement

• Method 1: Automated Inspection - Deploying AI-based automated inspection systems to enhance the speed and accuracy of product testing, ensuring product quality.
• Method 2: Defect Analysis and Improvement - Applying ML to deeply analyze production data, quickly identifying and addressing defects and problems in the production process.
• Method 3: Real-Time Monitoring and Feedback - Using AI for real-time production monitoring, instant data collection and analysis, and timely improvement suggestions.
• Method 4: Quality Prediction Model - Developing AI-based quality prediction models to identify potential quality issues in advance, reducing rework and waste.

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Pyramid Level 1 - Primary Objective Three: Construction and Application of Common AI Platform

Pyramid Level 2: Platform Construction and Integration

• Method 1: Data Integration - Establishing a robust data integration platform, aggregating data from various business and production departments, providing comprehensive data support for AI analysis.
• Method 2: AI Algorithm Optimization - Continuously developing and optimizing AI algorithms tailored for company needs, enhancing the efficiency and accuracy of data processing and analysis.
• Method 3: User Interface Friendliness - Designing user-friendly interfaces, ensuring easy access and utilization of the AI platform by employees from all departments.
• Method 4: Security and Privacy Protection - Strictly considering data security and privacy protection measures in platform construction, ensuring compliance with relevant regulations and establishing user trust.

Pyramid Level 2: AI Application Expansion

• Method 1: Intelligent Decision Support - Utilizing the AI platform to provide efficient decision-support tools, helping management make data-based decisions quickly.
• Method 2: New Business Exploration - Using AI to analyze market and technology trends, identifying new business opportunities and market potential.
• Method 3: Customer Service Automation - Deploying AI technology to automate customer service processes, improving response speed and increasing customer satisfaction.
• Method 4: Product Design Optimization - Using AI to optimize product design stages, shortening design cycles, enhancing product functionality, and market competitiveness.

Other Recommended Directions

• Environmental Sustainability: Using AI to optimize the use of energy and materials, reducing environmental impact during production, aiding in achieving more sustainable manufacturing.
• Global Strategy Adjustment: Using AI to analyze global market and policy trends, optimizing the company's global layout and strategy.
• Talent Cultivation and Management: Increasing efforts in training and recruiting talent in the AI and ML technology fields, building a strong technical team, enhancing the core competitiveness of the enterprise.

Action Methods

• Action Method One: For optimizing business planning, use AI and ML for market analysis and risk management, improving the company's market adaptability and foresight.
• Action Method Two: In manufacturing technology, innovate through smart transformation and quality control, enhancing production efficiency and product quality.
• Action Method Three: Construct and apply AI technology platforms, promoting decision-making efficiency and new business development, while focusing on environmental sustainability and global strategic adjustment.

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3. Logically Reassembling Proposal into 3-layer Pyramid - For AI Transformation Excellence of Semiconductor Industry

• Determining the Main Logic Line of Pyramid: Regarding the collaborative responses provided by AI, I will follow the original AI hierarchical classification without reordering them. As for other aspects suggested by AI that need attention, I will incorporate them into the category of "Operational Strategy Planning," making them an important component of future planning and implementation.
• Solution Suggestion in 3-Layer Minto Pyramid Structure: Based on the above reorganization analysis, we can derive the following three-layer pyramid scheme with four priority aspects.

Part I summary: Through collaborative work with AI, using a three-step intuitive problem-solving approach and the logical tool of the Minto Pyramid, we propose how TSMC and semiconductor industry in general can enhance its competitive edge through an AI transformation excellence strategy. This strategy can be planned and implemented in four areas using generative AI and ML:

• Optimization and Adaptability of Business Planning: In market analysis and forecasting, real-time market data analysis, supply chain model optimization, risk prediction, and customer segmentation and customization services are all enhanced by AI and ML. Additionally, the importance of geopolitical risk analysis, supply chain redundancy design, agile response mechanisms, and crisis simulation exercises are emphasized in risk management and response strategies.
• Innovation in Manufacturing Process Technology: Focus on improving production efficiency through smart manufacturing optimization, predictive maintenance, resource optimization allocation, and process optimization. Strategies for quality control and improvement include automated detection, defect analysis and improvement, real-time monitoring and feedback, and the development of quality prediction models to ensure product quality.
• Construction and Application of common AI Platform: Key points in platform construction and integration include data integration, AI algorithm optimization, user interface friendliness, and security and privacy protection. Expansion of AI applications involves smart decision support, new business exploration, customer service automation, and product design optimization, to strengthen the company’s competitiveness and market adaptability.
• Operational Strategy Planning: Includes environmental sustainability through AI-optimized energy and waste reduction, global strategic adjustments by analyzing global market and policy changes with AI and strengthening talent cultivation and management in AI and ML to build a strong technical team.

By implementing these strategies, TSMC and semiconductor vendors can leverage AI and ML for an excellent AI transformation strategy and improve its global competitiveness.

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Part II: Logical Thinking Method for Problem Solving - The Minto Pyramid

As used in Part I, the Minto Pyramid’s hierarchical classification, combined with AI, was used to deconstruct, and reconstruct into a three-layer pyramid. In Part II, I delve into the structured problem-solving approach of the pyramid, focusing mainly on the hierarchical structure of the Minto Pyramid, the SCQA narrative flow, and principles and techniques related to the pyramid structure.

Pyramid Structure:

• Basic Structure

o?? Top-to-Bottom Style: The Minto Pyramid organizes information starting from the top, where the main conclusion or proposition is presented first. Supporting arguments are then provided within the respective groups.

o?? Start with Conclusion/Proposal/Question at first: At the top of the pyramid, the author or analyst begins by presenting the main conclusion, recommendation, or question, which helps readers quickly grasp the key points.

o?? Provide Supporting Points within Groups: Following the conclusion/question, lower levels of the pyramid contain various supporting arguments, evidence, or question breakdowns, which are grouped and associated with specific conclusions/propositions.

o?? Group Related Elements Together: Similar information or elements are grouped together because they share common attributes or relationships.

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• Advantages of the Structure

o?? Nature of Brain Behavior - Grouping: The Minto Pyramid leverages the way the human brain processes information by structuring it through grouping related information.

o?? Enhanced Comprehension: This structured organization makes information easier to understand because it follows a logical and orderly manner.

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• Narrative Flows:

o?? Direct Reader's Focus Points: The goal of narrative flows is to quickly guide the reader's points of focus and address questions that may arise during the reading.

o?? Validate Reader's Questions by Investigating the Root of the Issue: Narrative flows validate potential reader questions by delving into the origin of the problem to enhance the credibility of the analysis.

• Systematic Storytelling - SCQA:

o?? Situation: Describe the current situation or issue, providing background information.

o?? Complication: Introduce the complexity or challenge of the problem, explaining why the situation has become difficult or complicated.

o?? Question: Pose one or more questions that need to be addressed or answered.

o?? Answer: Provide the answers or solutions to the questions.

Building an effective pyramid structure involves several key structural relationships that ensure the pyramid is clear and organized for better communication and understanding of information. Structured principles emphasize the importance of organizing information logically. In a pyramid structure, different viewpoints or pieces of information should be organized following a logical sequence. This means that information should be arranged in an orderly manner to ensure a clear and coherent flow of information.

Structured hierarchical classification can have the following five logical structures:

• Importance: This principle emphasizes organizing information in order of importance. Information can be organized from the most important (on the left) to less important (on the right) in the pyramid. This ensures that key information is presented to the reader first, with less critical information following. This helps readers quickly grasp the core of the issue. In my personal experience, about 80% of analysis uses importance as the logical thread.
• Spatial: This principle focuses on the spatial aspect of organizing information. Information can be organized from inside to outside, from close to distant, or from many to few. This approach helps readers better understand the relationships and hierarchical structure of information.
• Temporal: The temporal principle organizes information in chronological order. It can be organized from goal to present or from present to goal, among other temporal arrangements. This method is particularly suitable for describing the development of events or historical evolution.
• Relational: This principle focuses on the relationships between pieces of information. Information can be organized from top to bottom, from high to low, or from broad to deep. This helps readers understand the hierarchical relationships and interactions between pieces of information.
• Argumentative: Finally, the argumentative principle emphasizes reasoning and argumentation in organizing information. Information can be organized deductively (including major premises, minor premises, and conclusions) or inductively. This helps establish a clear logical chain, enabling readers to better understand the reasoning and conclusions of the information.

Based on the three-layer Minto Pyramid structure, I will guide AI to answer questions according to this framework:

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AI Prompt:

For the questions mentioned later, please instruct ChatGPT?to use the following three-layer Minto pyramid?structure to answer the questions. Each layer should include two to four items listed in order of importance.

Pyramid Tier 1 - Primary Goal 1: Title

Pyramid Tier 2

First Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Second Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Third Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Pyramid Tier 1 - Primary Goal 2: Title

Pyramid Tier 2

First Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Second Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Third Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Pyramid Tier 1 - Primary Goal 3: Title

Pyramid Tier 2

First Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Second Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

Third Item: Title

Method 1: Description 1

Method 2: Description 2

Method 3: Description 3

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Please also suggest other Primary Goals to be considered.

Action Method 1 is for actions related to Primary Goal 1.

Action Method 2 is for actions related to Primary Goal 2.

Action Method 3 is for actions related to Primary Goal 3.

Action Method 4 is for actions related to other Primary Goals.

ChatGPT

Understood your request, and I will answer the question following the three-layer Minto pyramid?structure you provided:

Pyramid Tier 1 - Primary Goal 1:? Title

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We have explored how ChatGPT can utilize its transient memory capacity to provide three-layer structured responses according to the Minto Pyramid structure. This approach is particularly effective in handling complex issues, such as exploring TSMC's AI transformation excellence strategy, where ChatGPT can provide a layered, structured, comprehensive analysis based on the logical structure of the pyramid. This not only makes the responses clearer and more organized but also helps readers better understand and digest the information.

In Part II, I first introduce the basic structure of the Minto Pyramid, including how it helps organize and convey complex information. The design of the Minto Pyramid is intended to help people organize their thoughts methodically when facing complex issues, thereby presenting clear and logical responses. This structure is particularly suitable for solving strategic business issues, such as TSMC's AI transformation excellence. In future articles, I will further explore how to specifically use this tool and demonstrate its practical application through examples, thereby helping readers understand and master this powerful analytical tool more deeply.

In summary, the article discusses the AI transformation excellence strategy of the semiconductor industry facing rapid technological advancements and globalization challenges, focusing specifically on TSMC, a global leader in semiconductor manufacturing. Using the intuitive AI collaborative approach combined with the three-layer logical structure of the Minto Pyramid, the article proposes AI transformation excellence strategies for the semiconductor industry. These strategies include not only optimizing business planning, process technology, and common AI platforms but also addressing challenges such as supply chain uncertainty, technological competition, and talent competition.

The article emphasizes that by combining structured thinking methods with AI technology, the efficiency of solving complex problems can be effectively improved, providing practical strategies and insights for the AI transformation excellence of the semiconductor industry like TSMC. This approach is not only applicable to TSMC but can also be extended to the entire semiconductor industry, helping other companies formulate and implement their AI transformation strategies.

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For further details, please refer to the publication of “AI Power: Logical and Collaborative Innovation ”.

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#SemiconductorIndustry #BusinessPlanning #ProcessTechnology #AIPlatform