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Submit a ProposalArtificial Intelligence Enabled Businesses
 | How to Develop Strategies for Innovation Edited by Sweta Dixit, Mohit Maurya, Vishal Jain and Geetha Subramaniam Copyright: 2025 | Expected Pub Date:2024/12/06 ISBN: 9781394233977 | Hardcover | 542 pages Price: $225 USD  |
One Line Description
This book has a multidimensional perspective on AI solutions for business innovation and real-life case studies to achieve competitive advantage and drive growth in the evolving digital landscape.
Audience
This book is for marketing professionals, organizational leaders, and researchers to leverage AI and new technologies across various business functions. It also fits the needs of academics, students, and trainers, providing insights, case studies, and practical strategies for driving growth in the rapidly evolving digital landscape.
This book is for marketing professionals, organizational leaders, and researchers to leverage AI and new technologies across various business functions. It also fits the needs of academics, students, and trainers, providing insights, case studies, and practical strategies for driving growth in the rapidly evolving digital landscape.
Description
Artificial Intelligence-Enabled Businesses demonstrates how AI is a catalyst for change in business functional areas. Though still in the experimental phase, AI is instrumental in redefining the workforce, predicting consumer behavior, solving real-life marketing dynamics and modifications, recommending products and content, foreseeing demand, analyzing costs, strategizing, managing big data, enabling collaboration of cross-entities, and sparking new ethical, social and regulatory implications for business. Thus, AI can effectively guide the future of financial services, trading, mobile banking, last-mile delivery, logistics, and supply chain with a solution-oriented focus on discrete business problems. Furthermore, it is expected to educate leaders to act in an ever more accurate, complex, and sophisticated business environment with the combination of human and machine intelligence.
The book offers effective, efficient, and strategically competent suggestions for handling new challenges and responsibilities and is aimed at leaders who wish to be more innovative. It covers the early stages of AI adoption by organizations across their functional areas and provides insightful guidance for practitioners in the suitable and timely adoption of AI. This book will greatly help to scale up AI by leveraging interdisciplinary collaboration with cross-functional, skill-diverse teams and result in a competitive advantage.
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Artificial Intelligence-Enabled Businesses demonstrates how AI is a catalyst for change in business functional areas. Though still in the experimental phase, AI is instrumental in redefining the workforce, predicting consumer behavior, solving real-life marketing dynamics and modifications, recommending products and content, foreseeing demand, analyzing costs, strategizing, managing big data, enabling collaboration of cross-entities, and sparking new ethical, social and regulatory implications for business. Thus, AI can effectively guide the future of financial services, trading, mobile banking, last-mile delivery, logistics, and supply chain with a solution-oriented focus on discrete business problems. Furthermore, it is expected to educate leaders to act in an ever more accurate, complex, and sophisticated business environment with the combination of human and machine intelligence.
The book offers effective, efficient, and strategically competent suggestions for handling new challenges and responsibilities and is aimed at leaders who wish to be more innovative. It covers the early stages of AI adoption by organizations across their functional areas and provides insightful guidance for practitioners in the suitable and timely adoption of AI. This book will greatly help to scale up AI by leveraging interdisciplinary collaboration with cross-functional, skill-diverse teams and result in a competitive advantage.
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Author / Editor Details
Sweta Dixit, PhD, is an associate professor in the Sharda School of Business Studies, Sharda University, Greater Noida, India. She has authored one book and published research articles and case studies on emotional intelligence, global mobility, performance management, and organizational culture. Dixit also conducts sessions on emotional intelligence, self-awareness, leadership, and negotiation skills.
Sweta Dixit, PhD, is an associate professor in the Sharda School of Business Studies, Sharda University, Greater Noida, India. She has authored one book and published research articles and case studies on emotional intelligence, global mobility, performance management, and organizational culture. Dixit also conducts sessions on emotional intelligence, self-awareness, leadership, and negotiation skills.
Mohit Maurya, PhD, is an associate professor in the Sharda School of Business Studies, Sharda University, Greater Noida, India. He has published several research papers in national and international journals and delivered lectures on hyper-localization, digital marketing, branding, business ethics, etc. He has authored 3 books. His scholarly work won the Emerald - AUC School of Business Cast Writing Competition in 2020.
Vishal Jain, PhD, is an associate professor at Sharda University, Greater Noida, India. His research interests focus on information retrieval, the semantic web, ontology engineering, data mining, etc. He has edited 50 books for a variety of publishers and authored more than 100 research papers for reputed conferences and journals. Jain has several awards, which include the 2012 Young Active Member Award and the 2019 Best Researcher Award.
Geetha Subramaniam, PhD, is a professor at the Faculty of Education, Languages, Psychology, and Music, SEGI University, Kuala Lumpur, Malaysia. Her research focuses on labor economics, sustainable development issues, teaching & learning, and educational management issues. She has published more than 100 journals, co-authored two economics textbooks, and is the managing editor of the Malaysian Journal of Qualitative Research.
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Table of Contents
Preface
1. Crafting Effective AI Adoption Strategies
Aarti Neema and Rashid Khan
1.1 Introduction
1.2 Understanding Business Objectives
1.2.1 Aligning AI with Business Goals
1.2.2 Defining Clear Objectives
1.2.3 Incorporating Stakeholder Input
1.3 Seller-Centric and Customer-Centric Approaches
1.3.1 Seller-Centric Approach
1.3.2 Customer-Centric Approach
1.4 Comparison of Seller-Centric Approach and Customer‑Centric Approach
1.5 Readiness Assessment
1.5.1 Evaluating Technological Maturity
1.5.2 Analyzing Data Infrastructure
1.5.3 Gauging Workforce AI Expertise
1.5.4 Defining Resources and Budget
1.5.5 Aligning with Business Objectives
1.6 Data as the Foundation
1.6.1 Data Quality and Accessibility: The Pillars of Trust
1.6.2 Data Utilization
1.6.3 Breaking Data Silos: Paving the Path for Holistic Insights
1.6.4 Ethical Data Usage: The Trust Imperative
1.6.4.1 Transparency and User Consent: The Pillars of Ethical Data Usage
1.6.4.2 Navigating the Ethics of AI-Driven Insights
1.6.4.3 Data Anonymization and De-Identification
1.6.4.4 Balancing Personalization and Privacy
1.6.4.5 Responsible Data Sharing and Third-Party Collaboration
1.6.5 Data Security: Fortifying the Data Fortress
1.6.6 Informed Decision-Making
1.7 Data Collection and Management
1.7.1 Data Collection Strategies
1.7.2 Data Quality Assurance
1.7.3 Data Storage and Accessibility
1.7.4 Data Governance and Compliance
1.8 Data Integration
1.8.1 Understanding Data Integration
1.8.2 ETL Processes: Extract, Transform, Load
1.8.3 Real-Time Data Integration
1.8.3.1 Benefits and Challenges
1.9 Building an AI Dream Team: Unleashing the Power of Expertise
1.9.1 The Composition of an AI Dream Team
1.9.2 Assembling the Right Expertise: Crafting a Multidisciplinary Team
1.9.3 Defining Roles and Responsibilities
1.9.4 Identifying Relevant Use Cases: The Dream Team’s Initial Focus
1.9.5 Defining Success Metrics: Translating Vision into Measurable Outcomes
1.9.6 Deriving Actionable Insights: The Confluence of Expertise
1.9.7 Continuous Learning and Upskilling: Navigating an Evolving Landscape
1.9.8 Navigating Challenges and Complexities
1.10 Choosing the Right AI Solutions: Navigating the Sea of Possibilities
1.10.1 Defining Evaluation Criteria: The Roadmap to Selection
1.10.2 Piloting Projects: Testing the Waters
1.10.3 Considering AI Solution Types: From Off-the-Shelf to Custom-Built
1.10.4 Vendor Assessment: Beyond Features and into Partnership
1.10.5 Addressing Data Requirements: Fueling the AI Engine
1.11 Ethics and Transparency: Ensuring Moral Integrity in AI Adoption
1.11.1 Development of an Ethical Framework: Guiding Values in AI Adoption
1.11.2 Ensuring Algorithmic Transparency: Decoding the Black Box
1.11.3 Data Privacy and Informed Consent: Safeguarding Personal Information
1.11.4 Bias Mitigation and Fairness: The Quest for Equitable AI
1.11.5 Public Engagement and AI Literacy: Building Trust through Transparency
1.12 Managing Change and Resistance: Navigating the Human Dynamics of AI Adoption
1.12.1 Recognizing the Human Element: Cultivating an AI-Ready Culture
1.12.2 Communicating Vision and Benefits: Overcoming Fear with Clarity
1.12.3 Involving Employees: From Opposition to Ownership
1.12.4 Addressing Concerns: Creating a Safe Space for Feedback
1.12.5 Upskilling and Training: Empowering for a Tech-Driven Future
1.12.6 Building Resilience: Preparing for Challenges Ahead
1.13 Measuring Success and Iterative Improvement: Data-Driven Evolution of AI Initiatives
1.13.1 Defining Key Performance Indicators (KPIs): Guiding the Journey
1.13.2 Collecting and Analyzing Data: Insights for Iteration
1.13.3 Continuous Improvement Cycles: Optimizing AI Performance
1.13.4 Real-Time Decision-Making: Agility Through Insights
1.13.5 User Feedback and Adaptation: Aligning with User Needs
1.14 Conclusion: Navigating the AI-Enabled Future
References
2. Role of Artificial Intelligence in Management and Preservation of Old Text
Through New Tech
Veenus Jain, Pallavi Mohanan and Mkrtchyan Naira
2.1 Introduction
Conclusion
References
3. Deployment of AI and ML Techniques in the Form of Ontology for Improving
Business Management Perspectives
Aman Jandwani, Balraj Verma, Amit Mittal, Paras Jandwani and Gagandeep Singh Narula
3.1 Introduction
3.2 AI/ML Applications in Business and Marketing Management
3.3 Methodology Adopted
3.4 Discussion and Findings
3.4.1 Ontology Definitions
3.4.2 Types of Ontologies
3.4.3 Utilities of Ontologies
3.5 A Few Studies in Context of Innovation and Business Opportunity or Enterprise Ontologies
3.5.1 Development of Business-Related or Enterprise Ontology
3.5.2 Taxonomy Elucidation
3.5.2.1 Description of Features/Instances and Selection of Use Cases
3.5.3 Discussion and Findings
3.6 Conclusion and Future Scope
References
4. Blockchain in Supply Chain Management: Applications, Advantages and Challenges
Pijush Kanti Dutta Pramanik and Saurabh Pal
4.1 Introduction
4.2 Related Work
4.3 Importance of Efficient SCM in Business
4.3.1 Cost Reduction
4.3.2 Customer Satisfaction
4.3.3 Competitiveness
4.3.4 Innovation
4.3.5 Sustainability
4.4 Components of SCM
4.5 Issues in Traditional SCM Systems
4.6 Advancement in SCM
4.7 Use of Blockchain in SCM
4.8 Advantages of Blockchain for SCM
4.9 Challenges in Implementing Blockchain in SCM
4.10 A General Framework of Blockchain-Based SCM
4.11 Considerations for Implementing Blockchain in SCM
4.12 Case Studies of Blockchain Adoption in SCM
4.13 Conclusions
References
5. Artificial Intelligence for Supply Chain Optimization: Benefits, Challenges,
and Potential Solutions
Dhruv Kishore Bole and Narasimha Rao Vajjhala
5.1 Introduction
5.2 AI in Organizational Supply Chain: Benefits
5.2.1 Better Demand and Sales Forecasting
5.2.2 Improved Supplier Management
5.2.3 Enhanced Logistical Efficiency
5.2.4 Improved Product Return Efficiency
5.3 AI in Organizational Supply Chain: Implementation Challenges
5.3.1 High Initial Investment and Implementation Cost
5.3.2 Lack of Access to High-Quality Data and Scalable Data Pipelines
5.3.3 Employee-Related Challenges
5.3.4 Data Privacy and Security Issues
5.3.5 System Integration Challenges
5.4 Conclusion
5.5 Future Work
References
6. Fusing New Age Technologies with Marketing Management: Navigating the Digital Frontier
Ankitha K., Jayapadmini Kanchan, Harish Kunder, Shwetha S. Shetty, Ganaraj K. and Madhura Hegde
6.1 Introduction
6.2 The Evolution of Marketing Management in the Digital Era
6.3 Understanding New Age Technologies in Marketing
6.3.1 Artificial Intelligence (AI) and Machine Learning
6.3.2 Big Data Analytics
6.3.3 Virtual and Augmented Reality (VR/AR)
6.3.4 Internet of Things (IoT)
6.3.5 Blockchain Technology
6.4 Leveraging Data-Driven Insights for Targeted Marketing
6.5 Enhancing Customer Engagement Through Immersive Experiences
6.6 The Power of Social Media and Influencer Marketing
6.7 Blockchain for Transparent and Trustworthy Marketing
6.8 The Challenge of Data Privacy and Ethics
6.9 Overcoming Barriers and Implementing New Age Technologies
6.10 Conclusion
References
7. Nth Floor at Accenture—Next-Gen Onboarding Using Metaverse
Rachna Bansal Jora, Ansh Agrawal, Edoardo Mazzetto, Sweta Dixit and Samson Lallawmkipa Darlong
7.1 Introduction
7.1.1 About the Company
7.1.2 Accenture—A Pioneer in Technology
7.1.3 Accenture Rankings
7.2 Concept of Metaverse
7.2.1 Metaverse and the Future of Work
7.2.2 Concept of VR Training
7.3 Nth Floor at Accenture
7.3.1 VR Onboarding Experience at Accenture
7.3.2 Future of VR and Challenges
7.4 Conclusions
References
8. Smart HR with Smart Technologies
Arnav Malhotra, Alka Maurya and Balasundram Maniam
8.1 Introduction
8.2 Technology Integration for Effective Human Resource Management
8.2.1 Recruitment
8.2.2 Selection
8.2.3 Training and Development
8.2.4 Performance Appraisal System
8.2.5 Compensation Management
8.3 Adoption of Latest Technologies for Effective HRM
8.3.1 Artificial Intelligence
8.3.1.1 Career Path
8.3.1.2 Recruitment
8.3.2 Big Data
8.3.2.1 Pay for Performance
8.3.2.2 Talent Assessment
8.3.3 Blockchain
8.3.3.1 Background Checks
8.3.3.2 Data Security
8.3.4 Augmented Reality
8.3.4.1 Communication
8.3.5 Virtual Reality
8.3.5.1 Training and Development
8.3.5.2 Employee Safety
8.4 Conclusion
References
9. Securing Business Transactions Using Merkle Tree
Ambika N.
9.1 Introduction
Background
Merkle Tree
Literature Survey
Previous Study
Analysis of the Work
Future Scope
Conclusion
References
10. InvestoAI-Tailored Investment Recommendation
Niomi Samani, Tejas Uttare, Rama M. Maliya and Kedar Semani
10.1 Introduction
10.2 Literature Survey
10.3 Methodology
10.4 Conclusion and Future Scope of Work
References
11. Using AI Technology to Enhance Data-Driven Decision-Making in the Financial Sector
Meng Wu, Geetha Subramaniam, Zeyu Li and Xiuchun Gao
11.1 Introduction
11.2 An Overview of AI Technology in Financial Analysis
11.2.1 Machine Learning
11.2.2 Deep Learning and Natural Language Processing (NLP)
11.2.3 Predictive Analytics and Predictive Algorithms
11.3 Case Studies of AI Technology in Financial Analysis
11.3.1 Yield Prediction of Agricultural Products Based on Remote Sensing Images
11.3.1.1 Research Workflow
11.3.1.2 Data Collection
11.3.1.3 Data Preprocessing
11.3.1.4 Model Training
11.3.1.5 Findings and Discussions
11.3.2 Futures Price Forecasting Using AI Algorithms
11.3.2.1 Research Process
11.3.2.2 Showcase of Industrial Silicon Futures
11.3.3 Commodity Supply Chain Financial Big Data Intelligent Analysis Platform
11.3.3.1 Platform Architecture
11.3.3.2 Platform Showcase—Financial AI Analysis Result
11.4 Conclusion, Challenges, and Outlook
11.4.1 Existing Challenges
11.4.2 Outlook of the Financial Industry
11.4.3 Conclusion
References
12. The Role of Artificial Intelligence (AI) in the Transformation of Smalland
Medium-Sized Businesses: Challenges and Opportunities
Arjita Jain, Kiran Shrimant Kakade and Swati Amit Vispute
12.1 Introduction
AI and Business Transformation in SMEs
Growth of Artificial Intelligence Structures
Implications of AI Systems for SMEs
Implications for the Business Environment
AI Diffusion and Challenges for SMEs
Contribution of Artificial Intelligence (AI) in Transforming Businesses
Overcoming Barriers and Ensuring Inclusivity
Future Directions and Policy Interventions
Future Research
Conclusion
References
13. Applications of Artificial Intelligence and Machine Learning-Enabled Businesses: A SWOT Analysis for Human Society
Santanu Koley, Shatadru Sengupta, Bipasha Biswas, Kankana Datta, Manasi Jana and Apratim Mitra
13.1 Introduction
13.2 Artificial Intelligence
13.3 Machine Learning
13.4 Deep Learning
13.5 Applications of AI, ML and DL in Various Types of Businesses with SWOT Analysis
13.5.1 Biometric Recognition
13.5.1.1 Fingerprint Recognition
13.5.1.2 Facial Recognition
13.5.1.3 Recognition of Other Biometric Features
13.5.2 Recommendation Systems
13.5.2.1 Why Recommendation System?
13.5.2.2 Definition of Recommendation System
13.5.2.3 Various Applications of Recommendation System
13.5.2.4 Types of Recommendation System
13.5.2.5 Advantages of Recommendation System
13.5.2.6 Challenges Involved with Recommendation System
13.5.3 Digital Healthcare
13.5.3.1 Why Digital Healthcare is Needed?
13.5.3.2 Need for AI/ML-Enabled Automation in the Healthcare Industry of India
13.5.3.3 Disease Detection and Prophylaxis Through AI
13.5.3.4 Advantages of AI-Enabled Healthcare System
13.5.3.5 Digital Biomarker
13.5.3.6 The Disadvantages of AI
13.5.4 Sentiment Analysis
13.5.5 Logistics Management
13.6 Conclusion
13.7 Future Work
References
14. Gamified Learning Environments for Higher Education Sustainability in Delhi Metropolitan Region
Sonia Yadav, Sweta Dixit, Rachna Bansal and Canh Van Ta
14.1 Introduction
14.2 “Gamification” Online and Education for Sustainability
14.3 Gamification and Theory of Self Determination
14.4 In-Class Instruction in an Online Gamification EfS Learning Exercise
14.5 The Idea of Gamification in Online Education
14.6 Sustainability and Gamification in Recent Times
14.7 Ecological Online Education
14.8 Objectives
14.9 Research Methodology
14.10 Data Analysis
14.11 Conclusion
References
15. Exploring the Impact of AI on Management and Healthcare for Streamlining
Operations and Decision-Making
Pranshu Mathur and Ajay Kumar
15.1 Introduction
15.2 Conceptual Framework
15.2.1 Artificial Intelligent Need for Business
15.2.2 Current Uses of AI
15.3 Research Methodology
15.4 AI Applications in Management
15.4.1 AI in Finance
15.4.2 AI in Healthcare
15.4.3 AI in Human Resource (HR)
15.4.4 AI in Marketing
15.5 Conclusion
15.6 Future Research Direction
References
16. Empowering Defense: Harnessing AI for Next-Generation Warfare
Nikki Rani, Komal Jindal, Rita Chikkara and Nidhi Malik
16.1 Introduction
16.2 AI Complies with Acquisition Procedures and Defense Behaviors
16.2.1 Artificial Intelligence Methods That Might Be Used for Active Cyber Defense
16.2.2 Misconceptions
16.3 Intelligent DDoS Mitigation System
16.3.1 Automation Degree of DDoS Attack
16.3.2 Exploited Vulnerability of DDoS Attack
16.3.3 The DDoS Attack’s Dynamic Rate
16.3.4 Impact of DDoS Attack
16.4 Advancing Defense: Nanotechnology and Natural Pathogen Defense in Fish
16.5 Emerging Technologies and Defense: Exploring the Intersection of AI, Robotics, Swarm Drones, and India’s Defense Preparedness
16.5.1 Swarm Drones and India’s Defense Preparedness
16.5.2 Collaboration: Partnership Between Boeing and AIESL for Key Indian Defense Platforms’ Maintenance, Repair, and Overhaul
16.5.3 AI and Robotics: Reshaping Indian Industries in Industry 4.0
16.6 AI Projects in Defense: Impressive Achievements by the Indian Government
16.7 Conclusion and Future Scope
References
17. Industry Augmented Reality Along with Artificial Intelligence: Developments, Resources, and Possible Concerns
Sarabjeet Singh Sethi and Priyanka Sharma
17.1 Introduction
17.2 The Frameworks and Platforms for Augmented Reality
17.2.1 ARBlocks
17.2.2 ARCore
17.2.3 ARKit
17.2.4 AR-Media
17.2.5 ARToolKit
17.2.6 ARWin
17.2.7 Bright
17.2.8 CoVAR
17.2.9 DUIRA
17.2.10 KITE
17.2.11 Nexus
17.2.12 WARP
17.3 Artificial Intelligence with Augmented Reality in Enterprise
17.3.1 Significance
17.3.2 AR in Manufacturing
17.3.3 AI-Aided Manufacturing
17.3.4 AI and AR in Business and Academia
17.3.4.1 Education
17.3.4.2 Construction Business
17.3.4.3 Manufacturing Business
17.3.4.4 Healthcare
17.3.4.5 Gaming
17.4 Challenges
17.5 Conclusion
References
18. Transformative Effects of Smarter Chatbots: Unravelling the Vision, Challenges, and Capabilities of ChatGPT-Conversational AI
C. Kishor Kumar Reddy, Patlolla Sathvika Reddy, Ashritha Pilly and Srinath Doss
18.1 Introduction
18.2 ChatGPT Summary Compilation
18.2.1 Background of ChatGPT
18.3 Architecture of ChatGPT
18.4 Training ChatGPT
18.4.1 Data Sources Used in Training ChatGPT
18.5 Applications of ChatGPT
18.5.1 Advantages
18.5.2 Disadvantages
18.6 Conclusion
References
19. Application of Artificial Intelligence in Business Management for Prudent
Decision Making
Syed Tabassum Sultana and T. Venkat Narayana Rao
19.1 Introduction
19.2 Review of Literature
19.3 Merits and Cons in Business Decisions with AI Involvement
19.4 Applications of AI Tools in Business Models
19.5 Artificial Intelligence-Based Data-Driven Insights
19.5.1 Predictive Analytics Models
19.5.2 Supply Chain Predictive Analytics
19.5.3 Recommendation Systems
19.5.4 Natural Language Processing (NLP) Models
19.5.5 Computer Vision Models
19.6 How AI Can Transform the Industry
19.7 Process Optimization Empowered by AI
19.7.1 Automation and Robotic Process Automation (RPA)
19.7.2 Personalization Models
19.7.3 Personalized Marketing
19.7.4 E-Commerce Personalization
19.7.5 Supply Chain Optimization Models
19.7.6 Healthcare Models
19.7.7 Human Resources Models
19.7.8 Financial Models
19.7.9 Energy Efficiency Models
19.8 Risk Mitigation Using AI
19.8.1 Anomaly Detection Model
19.8.2 Threats Analysis and Management
19.8.3 Risk Minimization
19.8.4 Fraud Discovery
19.8.5 How to Employ Artificial Intelligence in Risk Management Strategy
19.8.6 Ideation
19.8.7 Data Classification Data Sourcing
19.8.8 Model Development
19.8.9 Monitoring
19.9 Applications of AI in Business Processes
19.10 Advantages of AI
19.11 Conclusion
References
20. Technology-Driven Business Ethics: A Philosophical Discourse
Sooraj Kumar Maurya, Amarbahadur Yadav and Rajiv Nayan
20.1 Introduction
20.2 Research Methodology
20.3 Business Ethics and Technology
20.4 Evaluating the Applicability of Ethical Theories in Tech‑Infused Business
Landscapes
20.4.1 Utilitarianism
20.4.2 Deontology
20.4.3 Virtue Ethics
20.4.4 Social Contract Theory
20.4.5 The Changing Landscape of Normativity
20.5 Hurdles Encountered in Maintaining Ethical Standards Within Technology-Driven Business Environments
20.5.1 Rapid Technological Advancements
20.5.2 Data Privacy Concerns
20.5.3 Algorithmic Bias and Discrimination
20.5.4 Ethical Dilemmas in Automation
20.5.5 Leveraging Technology for Ethical Advancements in Business
20.5.6 Transparency and Accountability
20.5.7 Ethical Design
20.5.8 Ethical Decision-Support Systems
20.5.9 Stakeholder Engagement
20.6 Conclusion
References
21. Harnessing the Power of Artificial Intelligence for Sustainable Development
Ayan Harsh Sinha, Alka Maurya and J. Mark Munoz
21.1 Introduction
21.1.1 Waste Management
21.1.2 Saving Water and Valuable Resources
21.1.3 Improving Renewable Energy
21.1.4 Team Up with Firms for Goal Alignment
21.1.5 Reduced Environmental Impact
21.1.6 Energy-Efficient Algorithms
21.2 Conclusion
References
22. University Students’ Perception of Artificial Intelligence (AI) for Entrepreneurship Development in Selected Asian Countries of China, India, Indonesia, and Malaysia
Doris Padmini Selvaratnam, Jaheer Mukhtar K.P., Evi Gravitiani and Wen Meiting
22.1 Introduction
22.1.1 Importance of Artificial Intelligence (AI)
22.2 Student Entrepreneurship
22.3 Sustainability Livelihood
22.4 Theoretical and Conceptual Framework
22.4.1 Technological Acceptance and Perceptions of AI
22.4.2 Cultural and Societal Factors
22.4.3 Educational Ecosystem and Innovation
22.4.4 Entrepreneurship Education and Intentions
22.5 Methodology
22.6 Results and Discussion (ALL)
22.6.1 Knowledge of Artificial Intelligence (AI)
22.6.2 Practice of Artificial Intelligence (AI)
22.6.3 Perception of Artificial Intelligence (AI)
22.7 Future of Entrepreneurship With The Advancement of Artificial Intelligence (AI)
22.8 Policy Implication
22.9 Conclusion
References
23. Clubhouse Unleashed: Harnessing the Power of Voice for Robust Social
Networking and Business Growth
Pooja Darda, Shailesh Pandey, Om Jee Gupta, Manpreet Kaurand Sanjaya Singh Gaur
23.1 Introduction
23.2 The Rise of Clubhouse
23.3 The Clubhouse is the Next Major Thing
23.3.1 Audio-Chat Social Networking Apps: Market Overview
23.4 Clubhouse’s Unique Appeal
23.4.1 Social Media Apps Benefits for Business
23.4.2 Grow Your Business
23.4.3 Networking
23.4.4 Personal Branding
23.4.5 Coaching
23.4.6 Create an Engaged Community
23.4.7 Focus on Adding Value
23.4.8 Organizing Virtual Events and Conferences
23.4.9 Find (or Become) a Mentor
23.4.10 Business Meetings
23.5 Brands Leveraging Clubhouse
23.6 Psychological Aspects of Clubhouse Success
23.6.1 Unexpected Benefits
23.6.2 Community Spirit
23.6.3 Only Audio
23.6.4 Interactive
23.6.5 No Recordings or Saved Conversations
23.7 The Acceptance and Arrival of Clubhouse in India
23.8 Social Audio Application Challenge
23.9 Clubhouse Expansion
23.10 Conclusions and Future Scope
References
24. Artificial Intelligence (AI) as Strategy to Gain Competitive Advantage for Australian Higher Education Institutions (HEI) Under the New Post COVID-19 Scenario
Rubaiyet Hasan Khan and Rohini Balapumi
24.1 Introduction
Types of Higher Educational Institutions in Australia
Business Processes in Australian HEIs
COVID-19 Impact on Business Processes for Australian HEIs
Key Challenges of the Current Day
Possible Solutions
References
25. AI for a Better Future—Perspectives from Young Employees in Malaysia and China
Geetha Subramaniam, Wang Zhe, Zhu Dan and Narayanaswami Subramaniam
25.1 Introduction
25.1.1 Overview of AI in Malaysia
25.1.2 Overview of AI in China
25.1.3 AI and Labor Market
25.2 Integration of AI in Job Roles and Professional Development
25.2.1 AI Automation and Diminishing Need for Human Intervention
25.2.2 AI and Job Functions of Young Employees
25.3 Ethical and Social Implications of AI
25.3.1 AI and Technical Intricacies
25.3.2 AI—Ethical and Social Implications at the Workplace
25.4 Future of AI in the Work Ecosystem
25.4.1 AI as a Catalyst or Replacement
25.4.2 AI and the Work Ecosystem
25.5 Ability to Adapt to Use AI in Training and Job Roles
25.6 Conclusion—AI and Workforce of the Future
References
26. Personalization and Customer Experience in the Era of Data-Driven Marketing
Ambarish G. Mohapatra, Anita Mohanty, Subrat Kumar Mohanty, Nitaigour Premchand Mahalik and Sasmita Nayak
26.1 Introduction to Data-Driven Marketing and Personalization
26.1.1 Understanding Data-Driven Marketing Practices
26.1.2 Significance of Personalization in Customer-Centric Marketing
26.2 Customer Segmentation and Targeting Strategies
26.2.1 Utilizing Data for Effective Customer Segmentation
26.2.2 Targeting Specific Customer Groups with Personalized Offers
26.2.3 Case Studies: Successful Targeted Marketing Campaigns
26.3 Content Personalization and Dynamic Messaging
26.3.1 Leveraging Customer Data for Content Personalization
26.3.2 Dynamic Messaging and Real-Time Personalization
26.3.3 Case Studies: Effective Content Personalization in Marketing
26.4 Optimizing Customer Journeys with Data Insights
26.4.1 Mapping Customer Journeys Through Data Analytics
26.4.2 Personalized Customer Touchpoints and Interactions
26.4.3 Case Studies: Enhancing Customer Experiences with Data-Driven Journeys
26.5 The Role of Artificial Intelligence in Personalization
26.5.1 AI-Driven Recommendation Engines
26.5.2 Chatbots and Virtual Assistants for Personalized Customer Service
26.5.3 Case Studies: AI Applications in Personalized Marketing
26.6 Personalization and Privacy: Balancing Data Ethics
26.6.1 Addressing Data Privacy and Security Concerns
26.6.2 Ethical Use of Customer Data for Personalization
26.6.3 Case Studies: Building Trust Through Ethical Data Practices
26.7 Personalization in Omnichannel Marketing
26.7.1 Creating Consistent Personalized Experiences Across Channels
26.7.2 Integrating Online and Offline Data for Seamless Personalization
26.7.3 Case Studies: Successful Omnichannel Personalization Strategies
26.8 Personalization in E-Commerce and Retail
26.8.1 Personalized Product Recommendations and Shopping Experiences
26.8.2 Personalized Loyalty Programs and Offers
26.8.3 Case Studies: Innovative Personalization in E-Commerce
26.9 Conclusion
26.9.1 Recapitulation of Data-Driven Personalization in Marketing
26.9.2 Future Trends and Challenges in Customer Experience Enhancement
References
Index
Back to Top
Preface
1. Crafting Effective AI Adoption Strategies
Aarti Neema and Rashid Khan
1.1 Introduction
1.2 Understanding Business Objectives
1.2.1 Aligning AI with Business Goals
1.2.2 Defining Clear Objectives
1.2.3 Incorporating Stakeholder Input
1.3 Seller-Centric and Customer-Centric Approaches
1.3.1 Seller-Centric Approach
1.3.2 Customer-Centric Approach
1.4 Comparison of Seller-Centric Approach and Customer‑Centric Approach
1.5 Readiness Assessment
1.5.1 Evaluating Technological Maturity
1.5.2 Analyzing Data Infrastructure
1.5.3 Gauging Workforce AI Expertise
1.5.4 Defining Resources and Budget
1.5.5 Aligning with Business Objectives
1.6 Data as the Foundation
1.6.1 Data Quality and Accessibility: The Pillars of Trust
1.6.2 Data Utilization
1.6.3 Breaking Data Silos: Paving the Path for Holistic Insights
1.6.4 Ethical Data Usage: The Trust Imperative
1.6.4.1 Transparency and User Consent: The Pillars of Ethical Data Usage
1.6.4.2 Navigating the Ethics of AI-Driven Insights
1.6.4.3 Data Anonymization and De-Identification
1.6.4.4 Balancing Personalization and Privacy
1.6.4.5 Responsible Data Sharing and Third-Party Collaboration
1.6.5 Data Security: Fortifying the Data Fortress
1.6.6 Informed Decision-Making
1.7 Data Collection and Management
1.7.1 Data Collection Strategies
1.7.2 Data Quality Assurance
1.7.3 Data Storage and Accessibility
1.7.4 Data Governance and Compliance
1.8 Data Integration
1.8.1 Understanding Data Integration
1.8.2 ETL Processes: Extract, Transform, Load
1.8.3 Real-Time Data Integration
1.8.3.1 Benefits and Challenges
1.9 Building an AI Dream Team: Unleashing the Power of Expertise
1.9.1 The Composition of an AI Dream Team
1.9.2 Assembling the Right Expertise: Crafting a Multidisciplinary Team
1.9.3 Defining Roles and Responsibilities
1.9.4 Identifying Relevant Use Cases: The Dream Team’s Initial Focus
1.9.5 Defining Success Metrics: Translating Vision into Measurable Outcomes
1.9.6 Deriving Actionable Insights: The Confluence of Expertise
1.9.7 Continuous Learning and Upskilling: Navigating an Evolving Landscape
1.9.8 Navigating Challenges and Complexities
1.10 Choosing the Right AI Solutions: Navigating the Sea of Possibilities
1.10.1 Defining Evaluation Criteria: The Roadmap to Selection
1.10.2 Piloting Projects: Testing the Waters
1.10.3 Considering AI Solution Types: From Off-the-Shelf to Custom-Built
1.10.4 Vendor Assessment: Beyond Features and into Partnership
1.10.5 Addressing Data Requirements: Fueling the AI Engine
1.11 Ethics and Transparency: Ensuring Moral Integrity in AI Adoption
1.11.1 Development of an Ethical Framework: Guiding Values in AI Adoption
1.11.2 Ensuring Algorithmic Transparency: Decoding the Black Box
1.11.3 Data Privacy and Informed Consent: Safeguarding Personal Information
1.11.4 Bias Mitigation and Fairness: The Quest for Equitable AI
1.11.5 Public Engagement and AI Literacy: Building Trust through Transparency
1.12 Managing Change and Resistance: Navigating the Human Dynamics of AI Adoption
1.12.1 Recognizing the Human Element: Cultivating an AI-Ready Culture
1.12.2 Communicating Vision and Benefits: Overcoming Fear with Clarity
1.12.3 Involving Employees: From Opposition to Ownership
1.12.4 Addressing Concerns: Creating a Safe Space for Feedback
1.12.5 Upskilling and Training: Empowering for a Tech-Driven Future
1.12.6 Building Resilience: Preparing for Challenges Ahead
1.13 Measuring Success and Iterative Improvement: Data-Driven Evolution of AI Initiatives
1.13.1 Defining Key Performance Indicators (KPIs): Guiding the Journey
1.13.2 Collecting and Analyzing Data: Insights for Iteration
1.13.3 Continuous Improvement Cycles: Optimizing AI Performance
1.13.4 Real-Time Decision-Making: Agility Through Insights
1.13.5 User Feedback and Adaptation: Aligning with User Needs
1.14 Conclusion: Navigating the AI-Enabled Future
References
2. Role of Artificial Intelligence in Management and Preservation of Old Text
Through New Tech
Veenus Jain, Pallavi Mohanan and Mkrtchyan Naira
2.1 Introduction
Conclusion
References
3. Deployment of AI and ML Techniques in the Form of Ontology for Improving
Business Management Perspectives
Aman Jandwani, Balraj Verma, Amit Mittal, Paras Jandwani and Gagandeep Singh Narula
3.1 Introduction
3.2 AI/ML Applications in Business and Marketing Management
3.3 Methodology Adopted
3.4 Discussion and Findings
3.4.1 Ontology Definitions
3.4.2 Types of Ontologies
3.4.3 Utilities of Ontologies
3.5 A Few Studies in Context of Innovation and Business Opportunity or Enterprise Ontologies
3.5.1 Development of Business-Related or Enterprise Ontology
3.5.2 Taxonomy Elucidation
3.5.2.1 Description of Features/Instances and Selection of Use Cases
3.5.3 Discussion and Findings
3.6 Conclusion and Future Scope
References
4. Blockchain in Supply Chain Management: Applications, Advantages and Challenges
Pijush Kanti Dutta Pramanik and Saurabh Pal
4.1 Introduction
4.2 Related Work
4.3 Importance of Efficient SCM in Business
4.3.1 Cost Reduction
4.3.2 Customer Satisfaction
4.3.3 Competitiveness
4.3.4 Innovation
4.3.5 Sustainability
4.4 Components of SCM
4.5 Issues in Traditional SCM Systems
4.6 Advancement in SCM
4.7 Use of Blockchain in SCM
4.8 Advantages of Blockchain for SCM
4.9 Challenges in Implementing Blockchain in SCM
4.10 A General Framework of Blockchain-Based SCM
4.11 Considerations for Implementing Blockchain in SCM
4.12 Case Studies of Blockchain Adoption in SCM
4.13 Conclusions
References
5. Artificial Intelligence for Supply Chain Optimization: Benefits, Challenges,
and Potential Solutions
Dhruv Kishore Bole and Narasimha Rao Vajjhala
5.1 Introduction
5.2 AI in Organizational Supply Chain: Benefits
5.2.1 Better Demand and Sales Forecasting
5.2.2 Improved Supplier Management
5.2.3 Enhanced Logistical Efficiency
5.2.4 Improved Product Return Efficiency
5.3 AI in Organizational Supply Chain: Implementation Challenges
5.3.1 High Initial Investment and Implementation Cost
5.3.2 Lack of Access to High-Quality Data and Scalable Data Pipelines
5.3.3 Employee-Related Challenges
5.3.4 Data Privacy and Security Issues
5.3.5 System Integration Challenges
5.4 Conclusion
5.5 Future Work
References
6. Fusing New Age Technologies with Marketing Management: Navigating the Digital Frontier
Ankitha K., Jayapadmini Kanchan, Harish Kunder, Shwetha S. Shetty, Ganaraj K. and Madhura Hegde
6.1 Introduction
6.2 The Evolution of Marketing Management in the Digital Era
6.3 Understanding New Age Technologies in Marketing
6.3.1 Artificial Intelligence (AI) and Machine Learning
6.3.2 Big Data Analytics
6.3.3 Virtual and Augmented Reality (VR/AR)
6.3.4 Internet of Things (IoT)
6.3.5 Blockchain Technology
6.4 Leveraging Data-Driven Insights for Targeted Marketing
6.5 Enhancing Customer Engagement Through Immersive Experiences
6.6 The Power of Social Media and Influencer Marketing
6.7 Blockchain for Transparent and Trustworthy Marketing
6.8 The Challenge of Data Privacy and Ethics
6.9 Overcoming Barriers and Implementing New Age Technologies
6.10 Conclusion
References
7. Nth Floor at Accenture—Next-Gen Onboarding Using Metaverse
Rachna Bansal Jora, Ansh Agrawal, Edoardo Mazzetto, Sweta Dixit and Samson Lallawmkipa Darlong
7.1 Introduction
7.1.1 About the Company
7.1.2 Accenture—A Pioneer in Technology
7.1.3 Accenture Rankings
7.2 Concept of Metaverse
7.2.1 Metaverse and the Future of Work
7.2.2 Concept of VR Training
7.3 Nth Floor at Accenture
7.3.1 VR Onboarding Experience at Accenture
7.3.2 Future of VR and Challenges
7.4 Conclusions
References
8. Smart HR with Smart Technologies
Arnav Malhotra, Alka Maurya and Balasundram Maniam
8.1 Introduction
8.2 Technology Integration for Effective Human Resource Management
8.2.1 Recruitment
8.2.2 Selection
8.2.3 Training and Development
8.2.4 Performance Appraisal System
8.2.5 Compensation Management
8.3 Adoption of Latest Technologies for Effective HRM
8.3.1 Artificial Intelligence
8.3.1.1 Career Path
8.3.1.2 Recruitment
8.3.2 Big Data
8.3.2.1 Pay for Performance
8.3.2.2 Talent Assessment
8.3.3 Blockchain
8.3.3.1 Background Checks
8.3.3.2 Data Security
8.3.4 Augmented Reality
8.3.4.1 Communication
8.3.5 Virtual Reality
8.3.5.1 Training and Development
8.3.5.2 Employee Safety
8.4 Conclusion
References
9. Securing Business Transactions Using Merkle Tree
Ambika N.
9.1 Introduction
Background
Merkle Tree
Literature Survey
Previous Study
Analysis of the Work
Future Scope
Conclusion
References
10. InvestoAI-Tailored Investment Recommendation
Niomi Samani, Tejas Uttare, Rama M. Maliya and Kedar Semani
10.1 Introduction
10.2 Literature Survey
10.3 Methodology
10.4 Conclusion and Future Scope of Work
References
11. Using AI Technology to Enhance Data-Driven Decision-Making in the Financial Sector
Meng Wu, Geetha Subramaniam, Zeyu Li and Xiuchun Gao
11.1 Introduction
11.2 An Overview of AI Technology in Financial Analysis
11.2.1 Machine Learning
11.2.2 Deep Learning and Natural Language Processing (NLP)
11.2.3 Predictive Analytics and Predictive Algorithms
11.3 Case Studies of AI Technology in Financial Analysis
11.3.1 Yield Prediction of Agricultural Products Based on Remote Sensing Images
11.3.1.1 Research Workflow
11.3.1.2 Data Collection
11.3.1.3 Data Preprocessing
11.3.1.4 Model Training
11.3.1.5 Findings and Discussions
11.3.2 Futures Price Forecasting Using AI Algorithms
11.3.2.1 Research Process
11.3.2.2 Showcase of Industrial Silicon Futures
11.3.3 Commodity Supply Chain Financial Big Data Intelligent Analysis Platform
11.3.3.1 Platform Architecture
11.3.3.2 Platform Showcase—Financial AI Analysis Result
11.4 Conclusion, Challenges, and Outlook
11.4.1 Existing Challenges
11.4.2 Outlook of the Financial Industry
11.4.3 Conclusion
References
12. The Role of Artificial Intelligence (AI) in the Transformation of Smalland
Medium-Sized Businesses: Challenges and Opportunities
Arjita Jain, Kiran Shrimant Kakade and Swati Amit Vispute
12.1 Introduction
AI and Business Transformation in SMEs
Growth of Artificial Intelligence Structures
Implications of AI Systems for SMEs
Implications for the Business Environment
AI Diffusion and Challenges for SMEs
Contribution of Artificial Intelligence (AI) in Transforming Businesses
Overcoming Barriers and Ensuring Inclusivity
Future Directions and Policy Interventions
Future Research
Conclusion
References
13. Applications of Artificial Intelligence and Machine Learning-Enabled Businesses: A SWOT Analysis for Human Society
Santanu Koley, Shatadru Sengupta, Bipasha Biswas, Kankana Datta, Manasi Jana and Apratim Mitra
13.1 Introduction
13.2 Artificial Intelligence
13.3 Machine Learning
13.4 Deep Learning
13.5 Applications of AI, ML and DL in Various Types of Businesses with SWOT Analysis
13.5.1 Biometric Recognition
13.5.1.1 Fingerprint Recognition
13.5.1.2 Facial Recognition
13.5.1.3 Recognition of Other Biometric Features
13.5.2 Recommendation Systems
13.5.2.1 Why Recommendation System?
13.5.2.2 Definition of Recommendation System
13.5.2.3 Various Applications of Recommendation System
13.5.2.4 Types of Recommendation System
13.5.2.5 Advantages of Recommendation System
13.5.2.6 Challenges Involved with Recommendation System
13.5.3 Digital Healthcare
13.5.3.1 Why Digital Healthcare is Needed?
13.5.3.2 Need for AI/ML-Enabled Automation in the Healthcare Industry of India
13.5.3.3 Disease Detection and Prophylaxis Through AI
13.5.3.4 Advantages of AI-Enabled Healthcare System
13.5.3.5 Digital Biomarker
13.5.3.6 The Disadvantages of AI
13.5.4 Sentiment Analysis
13.5.5 Logistics Management
13.6 Conclusion
13.7 Future Work
References
14. Gamified Learning Environments for Higher Education Sustainability in Delhi Metropolitan Region
Sonia Yadav, Sweta Dixit, Rachna Bansal and Canh Van Ta
14.1 Introduction
14.2 “Gamification” Online and Education for Sustainability
14.3 Gamification and Theory of Self Determination
14.4 In-Class Instruction in an Online Gamification EfS Learning Exercise
14.5 The Idea of Gamification in Online Education
14.6 Sustainability and Gamification in Recent Times
14.7 Ecological Online Education
14.8 Objectives
14.9 Research Methodology
14.10 Data Analysis
14.11 Conclusion
References
15. Exploring the Impact of AI on Management and Healthcare for Streamlining
Operations and Decision-Making
Pranshu Mathur and Ajay Kumar
15.1 Introduction
15.2 Conceptual Framework
15.2.1 Artificial Intelligent Need for Business
15.2.2 Current Uses of AI
15.3 Research Methodology
15.4 AI Applications in Management
15.4.1 AI in Finance
15.4.2 AI in Healthcare
15.4.3 AI in Human Resource (HR)
15.4.4 AI in Marketing
15.5 Conclusion
15.6 Future Research Direction
References
16. Empowering Defense: Harnessing AI for Next-Generation Warfare
Nikki Rani, Komal Jindal, Rita Chikkara and Nidhi Malik
16.1 Introduction
16.2 AI Complies with Acquisition Procedures and Defense Behaviors
16.2.1 Artificial Intelligence Methods That Might Be Used for Active Cyber Defense
16.2.2 Misconceptions
16.3 Intelligent DDoS Mitigation System
16.3.1 Automation Degree of DDoS Attack
16.3.2 Exploited Vulnerability of DDoS Attack
16.3.3 The DDoS Attack’s Dynamic Rate
16.3.4 Impact of DDoS Attack
16.4 Advancing Defense: Nanotechnology and Natural Pathogen Defense in Fish
16.5 Emerging Technologies and Defense: Exploring the Intersection of AI, Robotics, Swarm Drones, and India’s Defense Preparedness
16.5.1 Swarm Drones and India’s Defense Preparedness
16.5.2 Collaboration: Partnership Between Boeing and AIESL for Key Indian Defense Platforms’ Maintenance, Repair, and Overhaul
16.5.3 AI and Robotics: Reshaping Indian Industries in Industry 4.0
16.6 AI Projects in Defense: Impressive Achievements by the Indian Government
16.7 Conclusion and Future Scope
References
17. Industry Augmented Reality Along with Artificial Intelligence: Developments, Resources, and Possible Concerns
Sarabjeet Singh Sethi and Priyanka Sharma
17.1 Introduction
17.2 The Frameworks and Platforms for Augmented Reality
17.2.1 ARBlocks
17.2.2 ARCore
17.2.3 ARKit
17.2.4 AR-Media
17.2.5 ARToolKit
17.2.6 ARWin
17.2.7 Bright
17.2.8 CoVAR
17.2.9 DUIRA
17.2.10 KITE
17.2.11 Nexus
17.2.12 WARP
17.3 Artificial Intelligence with Augmented Reality in Enterprise
17.3.1 Significance
17.3.2 AR in Manufacturing
17.3.3 AI-Aided Manufacturing
17.3.4 AI and AR in Business and Academia
17.3.4.1 Education
17.3.4.2 Construction Business
17.3.4.3 Manufacturing Business
17.3.4.4 Healthcare
17.3.4.5 Gaming
17.4 Challenges
17.5 Conclusion
References
18. Transformative Effects of Smarter Chatbots: Unravelling the Vision, Challenges, and Capabilities of ChatGPT-Conversational AI
C. Kishor Kumar Reddy, Patlolla Sathvika Reddy, Ashritha Pilly and Srinath Doss
18.1 Introduction
18.2 ChatGPT Summary Compilation
18.2.1 Background of ChatGPT
18.3 Architecture of ChatGPT
18.4 Training ChatGPT
18.4.1 Data Sources Used in Training ChatGPT
18.5 Applications of ChatGPT
18.5.1 Advantages
18.5.2 Disadvantages
18.6 Conclusion
References
19. Application of Artificial Intelligence in Business Management for Prudent
Decision Making
Syed Tabassum Sultana and T. Venkat Narayana Rao
19.1 Introduction
19.2 Review of Literature
19.3 Merits and Cons in Business Decisions with AI Involvement
19.4 Applications of AI Tools in Business Models
19.5 Artificial Intelligence-Based Data-Driven Insights
19.5.1 Predictive Analytics Models
19.5.2 Supply Chain Predictive Analytics
19.5.3 Recommendation Systems
19.5.4 Natural Language Processing (NLP) Models
19.5.5 Computer Vision Models
19.6 How AI Can Transform the Industry
19.7 Process Optimization Empowered by AI
19.7.1 Automation and Robotic Process Automation (RPA)
19.7.2 Personalization Models
19.7.3 Personalized Marketing
19.7.4 E-Commerce Personalization
19.7.5 Supply Chain Optimization Models
19.7.6 Healthcare Models
19.7.7 Human Resources Models
19.7.8 Financial Models
19.7.9 Energy Efficiency Models
19.8 Risk Mitigation Using AI
19.8.1 Anomaly Detection Model
19.8.2 Threats Analysis and Management
19.8.3 Risk Minimization
19.8.4 Fraud Discovery
19.8.5 How to Employ Artificial Intelligence in Risk Management Strategy
19.8.6 Ideation
19.8.7 Data Classification Data Sourcing
19.8.8 Model Development
19.8.9 Monitoring
19.9 Applications of AI in Business Processes
19.10 Advantages of AI
19.11 Conclusion
References
20. Technology-Driven Business Ethics: A Philosophical Discourse
Sooraj Kumar Maurya, Amarbahadur Yadav and Rajiv Nayan
20.1 Introduction
20.2 Research Methodology
20.3 Business Ethics and Technology
20.4 Evaluating the Applicability of Ethical Theories in Tech‑Infused Business
Landscapes
20.4.1 Utilitarianism
20.4.2 Deontology
20.4.3 Virtue Ethics
20.4.4 Social Contract Theory
20.4.5 The Changing Landscape of Normativity
20.5 Hurdles Encountered in Maintaining Ethical Standards Within Technology-Driven Business Environments
20.5.1 Rapid Technological Advancements
20.5.2 Data Privacy Concerns
20.5.3 Algorithmic Bias and Discrimination
20.5.4 Ethical Dilemmas in Automation
20.5.5 Leveraging Technology for Ethical Advancements in Business
20.5.6 Transparency and Accountability
20.5.7 Ethical Design
20.5.8 Ethical Decision-Support Systems
20.5.9 Stakeholder Engagement
20.6 Conclusion
References
21. Harnessing the Power of Artificial Intelligence for Sustainable Development
Ayan Harsh Sinha, Alka Maurya and J. Mark Munoz
21.1 Introduction
21.1.1 Waste Management
21.1.2 Saving Water and Valuable Resources
21.1.3 Improving Renewable Energy
21.1.4 Team Up with Firms for Goal Alignment
21.1.5 Reduced Environmental Impact
21.1.6 Energy-Efficient Algorithms
21.2 Conclusion
References
22. University Students’ Perception of Artificial Intelligence (AI) for Entrepreneurship Development in Selected Asian Countries of China, India, Indonesia, and Malaysia
Doris Padmini Selvaratnam, Jaheer Mukhtar K.P., Evi Gravitiani and Wen Meiting
22.1 Introduction
22.1.1 Importance of Artificial Intelligence (AI)
22.2 Student Entrepreneurship
22.3 Sustainability Livelihood
22.4 Theoretical and Conceptual Framework
22.4.1 Technological Acceptance and Perceptions of AI
22.4.2 Cultural and Societal Factors
22.4.3 Educational Ecosystem and Innovation
22.4.4 Entrepreneurship Education and Intentions
22.5 Methodology
22.6 Results and Discussion (ALL)
22.6.1 Knowledge of Artificial Intelligence (AI)
22.6.2 Practice of Artificial Intelligence (AI)
22.6.3 Perception of Artificial Intelligence (AI)
22.7 Future of Entrepreneurship With The Advancement of Artificial Intelligence (AI)
22.8 Policy Implication
22.9 Conclusion
References
23. Clubhouse Unleashed: Harnessing the Power of Voice for Robust Social
Networking and Business Growth
Pooja Darda, Shailesh Pandey, Om Jee Gupta, Manpreet Kaurand Sanjaya Singh Gaur
23.1 Introduction
23.2 The Rise of Clubhouse
23.3 The Clubhouse is the Next Major Thing
23.3.1 Audio-Chat Social Networking Apps: Market Overview
23.4 Clubhouse’s Unique Appeal
23.4.1 Social Media Apps Benefits for Business
23.4.2 Grow Your Business
23.4.3 Networking
23.4.4 Personal Branding
23.4.5 Coaching
23.4.6 Create an Engaged Community
23.4.7 Focus on Adding Value
23.4.8 Organizing Virtual Events and Conferences
23.4.9 Find (or Become) a Mentor
23.4.10 Business Meetings
23.5 Brands Leveraging Clubhouse
23.6 Psychological Aspects of Clubhouse Success
23.6.1 Unexpected Benefits
23.6.2 Community Spirit
23.6.3 Only Audio
23.6.4 Interactive
23.6.5 No Recordings or Saved Conversations
23.7 The Acceptance and Arrival of Clubhouse in India
23.8 Social Audio Application Challenge
23.9 Clubhouse Expansion
23.10 Conclusions and Future Scope
References
24. Artificial Intelligence (AI) as Strategy to Gain Competitive Advantage for Australian Higher Education Institutions (HEI) Under the New Post COVID-19 Scenario
Rubaiyet Hasan Khan and Rohini Balapumi
24.1 Introduction
Types of Higher Educational Institutions in Australia
Business Processes in Australian HEIs
COVID-19 Impact on Business Processes for Australian HEIs
Key Challenges of the Current Day
Possible Solutions
References
25. AI for a Better Future—Perspectives from Young Employees in Malaysia and China
Geetha Subramaniam, Wang Zhe, Zhu Dan and Narayanaswami Subramaniam
25.1 Introduction
25.1.1 Overview of AI in Malaysia
25.1.2 Overview of AI in China
25.1.3 AI and Labor Market
25.2 Integration of AI in Job Roles and Professional Development
25.2.1 AI Automation and Diminishing Need for Human Intervention
25.2.2 AI and Job Functions of Young Employees
25.3 Ethical and Social Implications of AI
25.3.1 AI and Technical Intricacies
25.3.2 AI—Ethical and Social Implications at the Workplace
25.4 Future of AI in the Work Ecosystem
25.4.1 AI as a Catalyst or Replacement
25.4.2 AI and the Work Ecosystem
25.5 Ability to Adapt to Use AI in Training and Job Roles
25.6 Conclusion—AI and Workforce of the Future
References
26. Personalization and Customer Experience in the Era of Data-Driven Marketing
Ambarish G. Mohapatra, Anita Mohanty, Subrat Kumar Mohanty, Nitaigour Premchand Mahalik and Sasmita Nayak
26.1 Introduction to Data-Driven Marketing and Personalization
26.1.1 Understanding Data-Driven Marketing Practices
26.1.2 Significance of Personalization in Customer-Centric Marketing
26.2 Customer Segmentation and Targeting Strategies
26.2.1 Utilizing Data for Effective Customer Segmentation
26.2.2 Targeting Specific Customer Groups with Personalized Offers
26.2.3 Case Studies: Successful Targeted Marketing Campaigns
26.3 Content Personalization and Dynamic Messaging
26.3.1 Leveraging Customer Data for Content Personalization
26.3.2 Dynamic Messaging and Real-Time Personalization
26.3.3 Case Studies: Effective Content Personalization in Marketing
26.4 Optimizing Customer Journeys with Data Insights
26.4.1 Mapping Customer Journeys Through Data Analytics
26.4.2 Personalized Customer Touchpoints and Interactions
26.4.3 Case Studies: Enhancing Customer Experiences with Data-Driven Journeys
26.5 The Role of Artificial Intelligence in Personalization
26.5.1 AI-Driven Recommendation Engines
26.5.2 Chatbots and Virtual Assistants for Personalized Customer Service
26.5.3 Case Studies: AI Applications in Personalized Marketing
26.6 Personalization and Privacy: Balancing Data Ethics
26.6.1 Addressing Data Privacy and Security Concerns
26.6.2 Ethical Use of Customer Data for Personalization
26.6.3 Case Studies: Building Trust Through Ethical Data Practices
26.7 Personalization in Omnichannel Marketing
26.7.1 Creating Consistent Personalized Experiences Across Channels
26.7.2 Integrating Online and Offline Data for Seamless Personalization
26.7.3 Case Studies: Successful Omnichannel Personalization Strategies
26.8 Personalization in E-Commerce and Retail
26.8.1 Personalized Product Recommendations and Shopping Experiences
26.8.2 Personalized Loyalty Programs and Offers
26.8.3 Case Studies: Innovative Personalization in E-Commerce
26.9 Conclusion
26.9.1 Recapitulation of Data-Driven Personalization in Marketing
26.9.2 Future Trends and Challenges in Customer Experience Enhancement
References
Index
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