Artificial Intelligence (AI) Quick-Reference Guide (Excel XLSX)

BENEFITS OF THIS EXCEL DOCUMENT

1. Provides a framework for understanding and applying 150+ AI concepts across business, data, and technology.
2. Enables leaders and teams to implement AI initiatives with clarity, structure, and measurable impact.
3. Serves as a comprehensive reference for aligning AI strategy, tools, and governance with organizational goals.

ARTIFICIAL INTELLIGENCE EXCEL DESCRIPTION

This product (Artificial Intelligence [AI] Quick-Reference Guide) is an Excel template (XLSX), which you can download immediately upon purchase.

Curated by McKinsey-trained Executives


🚀 The Ultimate AI Quick Reference Guide – 100+ Key Elements for Mastering Artificial Intelligence, Machine Learning, Data, and Business Strategy

Unlock the complete power of Artificial Intelligence with the AI Quick Reference Guide – a fully structured, expert-designed resource featuring 150 essential AI elements across concepts, data, infrastructure, strategy, ethics, and execution.

Whether you're a business leader, AI strategist, data scientist, or innovation consultant, this guide delivers a comprehensive, actionable framework for designing, implementing, and scaling AI initiatives that drive measurable business results.

Every key element includes:
Overview | Core Activities | Tools & Systems | Roles & Responsibilities | KPIs | Best Practices & Standards | Risks & Controls | Integration Points | Quick Tips & FAQs

Perfect for executives, analysts, developers, and strategists, this AI Quick Reference Guide simplifies complex ideas into practical insights and frameworks that help you build, govern, and optimize AI-driven enterprises.

🧠 1. Core AI Concepts
1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Neural Networks
5. Natural Language Processing
6. Computer Vision
7. Reinforcement Learning
8. Generative AI
9. Transfer Learning
10. Explainable AI


💾 2. Data Foundations
11. Data Strategy
12. Data Collection
13. Data Labeling
14. Data Quality Management
15. Data Governance
16. Data Integration
17. Data Warehousing
18. Data Pipelines
19. Synthetic Data
20. Metadata Management


⚙️ 3. AI Infrastructure
21. Cloud AI Platforms
22. Edge AI
23. High-Performance Computing
24. Model Deployment
25. Model Monitoring
26. Containerization
27. Microservices Architecture
28. APIs and AI Integration
29. AI Operations (MLOps)
30. Scalability and Optimization


🧩 4. Machine Learning Lifecycle
31. Problem Definition
32. Feature Engineering
33. Model Selection
34. Model Training
35. Hyperparameter Tuning
36. Model Validation
37. Model Evaluation
38. Model Versioning
39. Continuous Learning
40. Model Drift Management


🧰 5. AI Tools & Frameworks
41. TensorFlow
42. PyTorch
43. Scikit-learn
44. Keras
45. Hugging Face Transformers
46. OpenAI API
47. LangChain
48. MLflow
49. Kubeflow
50. AutoML Platforms


🏢 6. Business Strategy & Leadership
51. AI Vision and Strategy
52. AI Maturity Model
53. Executive Alignment
54. AI Roadmapping
55. ROI and Value Realization
56. Change Management
57. Talent Strategy
58. AI Center of Excellence
59. Cross-Functional Collaboration
60. AI Governance Framework


💼 7. AI in Business Functions
61. Marketing Automation
62. Sales Enablement
63. Customer Service AI
64. Supply Chain Optimization
65. Financial Forecasting
66. Risk Management
67. Human Resources Analytics
68. Product Innovation
69. Operations Optimization
70. Pricing and Revenue Management


🎨 8. Generative AI Applications
71. Text Generation
72. Image Generation
73. Video Synthesis
74. Speech Synthesis
75. Code Generation
76. Content Personalization
77. Chatbots and Virtual Agents
78. Automated Report Writing
79. Knowledge Summarization
80. Digital Twin Simulation


📊 9. AI Analytics & Insights
81. Predictive Analytics
82. Prescriptive Analytics
83. Real-Time Analytics
84. Behavioral Analytics
85. Sentiment Analysis
86. Forecasting Models
87. KPI Dashboards
88. Decision Support Systems
89. Scenario Modeling
90. Root Cause Analysis


⚖️ 10. AI Ethics & Responsibility
91. Bias and Fairness
92. Transparency
93. Accountability
94. Privacy and Data Protection
95. Ethical AI Frameworks
96. Regulatory Compliance
97. Human Oversight
98. Algorithmic Trust
99. Responsible AI Design
100. Impact Assessment


🔒 11. Risk & Security
101. Data Security
102. Model Security
103. Adversarial Attacks
104. Threat Detection
105. AI System Vulnerabilities
106. Cyber Risk Assessment
107. AI Compliance Audits
108. Access Control
109. Incident Response
110. Secure AI Development


⚙️ 12. Productivity & Automation
111. Robotic Process Automation (RPA)
112. Intelligent Process Automation
113. Workflow Optimization
114. Smart Assistants
115. AI-Powered Search
116. Knowledge Management
117. Process Mining
118. Task Automation
119. Self-Service Analytics
120. AI-Augmented Decision-Making


🧭 13. AI Project Execution & Management
121. Project Scoping
122. Business Case Development
123. Stakeholder Engagement
124. Cross-Functional Teams
125. Agile AI Development
126. Project Milestones and Deliverables
127. Risk Mitigation Planning
128. Vendor and Partner Management
129. Pilot-to-Production Transition
130. Post-Implementation Review


⚡ 14. Performance & Optimization
131. Model Compression
132. Quantization
133. Parallel Processing
134. Resource Optimization
135. Cost Management
136. Latency Reduction
137. GPU Utilization
138. Cloud Cost Efficiency
139. A/B Testing
140. Continuous Optimization


📈 15. AI Measurement, Scaling & Continuous Improvement
141. Key AI Performance Indicators
142. Business Impact Measurement
143. Adoption Metrics
144. Feedback Loops
145. Model Retraining Strategy
146. Continuous Experimentation
147. AI Portfolio Management
148. Scaling AI Use Cases
149. Best Practice Sharing
150. Continuous Improvement Culture


🧩 Why You Need This AI Quick Reference Guide
•  Instant access to 150 critical AI concepts and business applications
•  Built-in structure for AI governance, MLOps, and transformation
•  Perfect for teams and executives leading AI initiatives
•  Optimized for decision-making, learning, and implementation
•  Includes integration, risk, and KPI frameworks across every AI dimension


💡 Transform Complexity into Clarity
This AI Quick Reference Guide is your one-stop intelligence hub for mastering the language, logic, and leadership of AI – across data, infrastructure, strategy, ethics, and operations.

Turn confusion into confidence.
Turn ambition into execution.
Turn AI into ROI.


📘 Get Your Copy of the AI Quick Reference Guide Today – and Build the Future, Smarter.




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Source: Best Practices in Artificial Intelligence Excel: Artificial Intelligence (AI) Quick-Reference Guide Excel (XLSX) Spreadsheet, SB Consulting