12 The intelligent implications of artificial intelligence-driven decision-making in business management
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S. Revathy
Abstract
The rapid rate at which technology has progressed in the last few decades has resulted in emerging technological solutions such as artificial intelligence (AI). AI is increasingly being used in business management decision-making processes to make more informed, better-informed, and more intelligent decisions. It is important to consider the intelligent implications of AI-driven decision-making in business management in order to ensure that the decisions made are not only effective but are also ethical, responsible, and beneficial to all stakeholders involved. AI has enabled decision makers to access and analyze large amounts of data quickly and accurately. This can aid decision-makers in making better-informed decisions as well as reducing costs and saving time. AI can also assist decision-makers in identifying important patterns or trends within datasets and in finding solutions to problems that might have otherwise been overlooked. Additionally, AI can also help to identify potential opportunities to improve the efficiency of existing processes and systems.
Abstract
The rapid rate at which technology has progressed in the last few decades has resulted in emerging technological solutions such as artificial intelligence (AI). AI is increasingly being used in business management decision-making processes to make more informed, better-informed, and more intelligent decisions. It is important to consider the intelligent implications of AI-driven decision-making in business management in order to ensure that the decisions made are not only effective but are also ethical, responsible, and beneficial to all stakeholders involved. AI has enabled decision makers to access and analyze large amounts of data quickly and accurately. This can aid decision-makers in making better-informed decisions as well as reducing costs and saving time. AI can also assist decision-makers in identifying important patterns or trends within datasets and in finding solutions to problems that might have otherwise been overlooked. Additionally, AI can also help to identify potential opportunities to improve the efficiency of existing processes and systems.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
