Artificial Intelligence-Enabled Businesses
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.
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.
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.
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Persons
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.
Content
Preface xix
1 Crafting Effective AI Adoption Strategies 1
Aarti Neema and Rashid Khan
1.1 Introduction 2
1.2 Understanding Business Objectives 2
1.3 Seller-Centric and Customer-Centric Approaches 3
1.4 Comparison of Seller-Centric Approach and Customer-Centric Approach 4
1.5 Readiness Assessment 5
1.6 Data as the Foundation 6
1.7 Data Collection and Management 9
1.8 Data Integration 9
1.9 Building an AI Dream Team: Unleashing the Power of Expertise 11
1.10 Choosing the Right AI Solutions: Navigating the Sea of Possibilities 16
1.11 Ethics and Transparency: Ensuring Moral Integrity in AI Adoption 17
1.12 Managing Change and Resistance: Navigating the Human Dynamics of AI Adoption 19
1.13 Measuring Success and Iterative Improvement: Data-Driven Evolution of AI Initiatives 20
1.14 Conclusion: Navigating the AI-Enabled Future 22
References 23
2 Role of Artificial Intelligence in Management and Preservation of Old Text Through New Tech 25
Veenus Jain, Pallavi Mohanan and Mkrtchyan Naira
2.1 Introduction 25
Conclusion 35
References 36
3 Deployment of AI and ML Techniques in the Form of Ontology for Improving Business Management Perspectives 39
Aman Jandwani, Balraj Verma, Amit Mittal, Paras Jandwani and Gagandeep Singh Narula
3.1 Introduction 39
3.2 AI/ML Applications in Business and Marketing Management 41
3.3 Methodology Adopted 41
3.4 Discussion and Findings 43
3.5 A Few Studies in Context of Innovation and Business Opportunity or Enterprise Ontologies 46
3.6 Conclusion and Future Scope 52
References 53
4 Blockchain in Supply Chain Management: Applications, Advantages and Challenges 57
Pijush Kanti Dutta Pramanik and Saurabh Pal
4.1 Introduction 57
4.2 Related Work 59
4.3 Importance of Efficient SCM in Business 60
4.4 Components of SCM 66
4.5 Issues in Traditional SCM Systems 67
4.6 Advancement in SCM 68
4.7 Use of Blockchain in SCM 69
4.8 Advantages of Blockchain for SCM 70
4.9 Challenges in Implementing Blockchain in SCM 71
4.10 A General Framework of Blockchain-Based SCM 72
4.11 Considerations for Implementing Blockchain in SCM 74
4.12 Case Studies of Blockchain Adoption in SCM 75
4.13 Conclusions 77
References 78
5 Artificial Intelligence for Supply Chain Optimization: Benefits, Challenges, and Potential Solutions 81
Dhruv Kishore Bole and Narasimha Rao Vajjhala
5.1 Introduction 81
5.2 AI in Organizational Supply Chain: Benefits 83
5.3 AI in Organizational Supply Chain: Implementation Challenges 85
5.4 Conclusion 90
5.5 Future Work 90
References 91
6 Fusing New Age Technologies with Marketing Management: Navigating the Digital Frontier 95
Ankitha K., Jayapadmini Kanchan, Harish Kunder, Shwetha S. Shetty, Ganaraj K. and Madhura Hegde
6.1 Introduction 96
6.2 The Evolution of Marketing Management in the Digital Era 97
6.3 Understanding New Age Technologies in Marketing 98
6.4 Leveraging Data-Driven Insights for Targeted Marketing 100
6.5 Enhancing Customer Engagement Through Immersive Experiences 100
6.6 The Power of Social Media and Influencer Marketing 100
6.7 Blockchain for Transparent and Trustworthy Marketing 101
6.8 The Challenge of Data Privacy and Ethics 101
6.9 Overcoming Barriers and Implementing New Age Technologies 102
6.10 Conclusion 102
References 102
7 Nth Floor at Accenture-Next-Gen Onboarding Using Metaverse 105
Rachna Bansal Jora, Ansh Agrawal, Edoardo Mazzetto, Sweta Dixit and Samson Lallawmkipa Darlong
7.1 Introduction 105
7.2 Concept of Metaverse 109
7.3 Nth Floor at Accenture 112
7.4 Conclusions 117
References 117
8 Smart HR with Smart Technologies 121
Arnav Malhotra, Alka Maurya and Balasundram Maniam
8.1 Introduction 121
8.2 Technology Integration for Effective Human Resource Management 121
8.3 Adoption of Latest Technologies for Effective HRM 125
8.4 Conclusion 132
References 133
9 Securing Business Transactions Using Merkle Tree 137
Ambika N.
9.1 Introduction 137
Background 144
Merkle Tree 144
Literature Survey 146
Previous Study 148
Analysis of the Work 151
Future Scope 154
Conclusion 156
References 156
10 InvestoAI-Tailored Investment Recommendation 159
Niomi Samani, Tejas Uttare, Rama M. Maliya and Kedar Semani
10.1 Introduction 160
10.2 Literature Survey 161
10.3 Methodology 165
10.4 Conclusion and Future Scope of Work 179
References 182
11 Using AI Technology to Enhance Data-Driven Decision-Making in the Financial Sector 187
Meng Wu, Geetha Subramaniam, Zeyu Li and Xiuchun Gao
11.1 Introduction 188
11.2 An Overview of AI Technology in Financial Analysis 190
11.3 Case Studies of AI Technology in Financial Analysis 194
11.4 Conclusion, Challenges, and Outlook 204
References 205
12 The Role of Artificial Intelligence (AI) in the Transformation of Smalland Medium-Sized Businesses: Challenges and Opportunities 209
Arjita Jain, Kiran Shrimant Kakade and Swati Amit Vispute
12.1 Introduction 209
AI and Business Transformation in SMEs 211
Growth of Artificial Intelligence Structures 212
Implications of AI Systems for SMEs 213
Implications for the Business Environment 214
AI Diffusion and Challenges for SMEs 214
Contribution of Artificial Intelligence (AI) in Transforming Businesses 218
Overcoming Barriers and Ensuring Inclusivity 221
Future Directions and Policy Interventions 222
Future Research 223
Conclusion 224
References 225
13 Applications of Artificial Intelligence and Machine Learning-Enabled Businesses: A SWOT Analysis for Human Society 227
Santanu Koley, Shatadru Sengupta, Bipasha Biswas, Kankana Datta, Manasi Jana and Apratim Mitra
13.1 Introduction 228
13.2 Artificial Intelligence 229
13.3 Machine Learning 230
13.4 Deep Learning 231
13.5 Applications of AI, ML and DL in Various Types of Businesses with SWOT Analysis 232
13.6 Conclusion 255
13.7 Future Work 256
References 256
14 Gamified Learning Environments for Higher Education Sustainability in Delhi Metropolitan Region 263
Sonia Yadav, Sweta Dixit, Rachna Bansal and Canh Van Ta
14.1 Introduction 263
14.2 "Gamification" Online and Education for Sustainability 264
14.3 Gamification and Theory of Self Determination 264
14.4 In-Class Instruction in an Online Gamification EfS Learning Exercise 266
14.5 The Idea of Gamification in Online Education 267
14.6 Sustainability and Gamification in Recent Times 267
14.7 Ecological Online Education 268
14.8 Objectives 269
14.9 Research Methodology 269
14.10 Data Analysis 269
14.11 Conclusion 271
References 272
15 Exploring the Impact of AI on Management and Healthcare for Streamlining Operations and Decision-Making 275
Pranshu Mathur and Ajay Kumar
15.1 Introduction 275
15.2 Conceptual Framework 276
15.3 Research Methodology 277
15.4 AI Applications in Management 281
15.5 Conclusion 286
15.6 Future Research Direction 286
References 286
16 Empowering Defense: Harnessing AI for Next-Generation Warfare 289
Nikki Rani, Komal Jindal, Rita Chikkara and Nidhi Malik
16.1 Introduction 289
16.2 AI Complies with Acquisition Procedures and Defense Behaviors 290
16.3 Intelligent DDoS Mitigation System 295
16.4 Advancing Defense: Nanotechnology and Natural Pathogen Defense in Fish 298
16.5 Emerging Technologies and Defense: Exploring the Intersection of AI, Robotics, Swarm Drones, and India's Defense Preparedness 300
16.6 AI Projects in Defense: Impressive Achievements by the Indian Government 304
16.7 Conclusion and Future Scope 307
References 308
17 Industry Augmented Reality Along with Artificial Intelligence: Developments, Resources, and Possible Concerns 311
Sarabjeet Singh Sethi and Priyanka Sharma
17.1 Introduction 311
17.2 The Frameworks and Platforms for Augmented Reality 313
17.3 Artificial Intelligence with Augmented Reality in Enterprise 320
17.4 Challenges 324
17.5 Conclusion 325
References 326
18 Transformative Effects of Smarter Chatbots: Unravelling the Vision, Challenges, and Capabilities of ChatGPT-Conversational AI 333
C. Kishor Kumar Reddy, Patlolla Sathvika Reddy, Ashritha Pilly and Srinath Doss
18.1 Introduction 333
18.2 ChatGPT Summary Compilation 335
18.3 Architecture of ChatGPT 340
18.4 Training ChatGPT 341
18.5 Applications of ChatGPT 345
18.6 Conclusion 348
References 348
19 Application of Artificial Intelligence in Business Management for Prudent Decision Making 351
Syed Tabassum Sultana and T. Venkat Narayana Rao
19.1 Introduction 351
19.2 Review of Literature 352
19.3 Merits and Cons in Business Decisions with AI Involvement 354
19.4 Applications of AI Tools in Business Models 355
19.5 Artificial Intelligence-Based Data-Driven Insights 356
19.6 How AI Can Transform the Industry 359
19.7 Process Optimization Empowered by AI 360
19.8 Risk Mitigation Using AI 363
19.9 Applications of AI in Business Processes 365
19.10 Advantages of AI 366
19.11 Conclusion 367
References 368
20 Technology-Driven Business Ethics: A Philosophical Discourse 371
Sooraj Kumar Maurya, Amarbahadur Yadav and Rajiv Nayan
20.1 Introduction 371
20.2 Research Methodology 372
20.3 Business Ethics and Technology 372
20.4 Evaluating the Applicability of Ethical Theories in Tech-Infused Business Landscapes 373
20.5 Hurdles Encountered in Maintaining Ethical Standards Within Technology-Driven Business Environments 378
20.6 Conclusion 386
References 387
21 Harnessing the Power of Artificial Intelligence for Sustainable Development 391
Ayan Harsh Sinha, Alka Maurya and J. Mark Munoz
21.1 Introduction 391
21.2 Conclusion 395
References 395
22 University Students' Perception of Artificial Intelligence (AI) for Entrepreneurship Development in Selected Asian Countries of China, India, Indonesia, and Malaysia 397
Doris Padmini Selvaratnam, Jaheer Mukhtar K.P., Evi Gravitiani and Wen Meiting
22.1 Introduction 397
22.2 Student Entrepreneurship 399
22.3 Sustainability Livelihood 399
22.4 Theoretical and Conceptual Framework 400
22.5 Methodology 402
22.6 Results and Discussion (ALL) 402
22.7 Future of Entrepreneurship With The Advancement of Artificial Intelligence (AI) 418
22.8 Policy Implication 418
22.9 Conclusion 419
References 419
23 Clubhouse Unleashed: Harnessing the Power of Voice for Robust Social Networking and Business Growth 421
Pooja Darda, Shailesh Pandey, Om Jee Gupta, Manpreet Kaur and Sanjaya Singh Gaur
23.1 Introduction 422
23.2 The Rise of Clubhouse 422
23.3 The Clubhouse is the Next Major Thing 423
23.4 Clubhouse's Unique Appeal 425
23.5 Brands Leveraging Clubhouse 428
23.6 Psychological Aspects of Clubhouse Success 431
23.7 The Acceptance and Arrival of Clubhouse in India 432
23.8 Social Audio Application Challenge 432
23.9 Clubhouse Expansion 433
23.10 Conclusions and Future Scope 433
References 436
24 Artificial Intelligence (AI) as Strategy to Gain Competitive Advantage for Australian Higher Education Institutions (HEI) Under the New Post COVID-19 Scenario 439
Rubaiyet Hasan Khan and Rohini Balapumi
24.1 Introduction 440
Types of Higher Educational Institutions in Australia 440
Business Processes in Australian HEIs 441
COVID-19 Impact on Business Processes for Australian HEIs 442
Key Challenges of the Current Day 444
Possible Solutions 445
References 447
25 AI for a Better Future-Perspectives from Young Employees in Malaysia and China 451
Geetha Subramaniam, Wang Zhe, Zhu Dan and Narayanaswami Subramaniam
25.1 Introduction 451
25.2 Integration of AI in Job Roles and Professional Development 456
25.3 Ethical and Social Implications of AI 457
25.4 Future of AI in the Work Ecosystem 460
25.5 Ability to Adapt to Use AI in Training and Job Roles 462
25.6 Conclusion-AI and Workforce of the Future 464
References 464
26 Personalization and Customer Experience in the Era of Data-Driven Marketing 467
Ambarish G. Mohapatra, Anita Mohanty, Subrat Kumar Mohanty, Nitaigour Premchand Mahalik and Sasmita Nayak
26.1 Introduction to Data-Driven Marketing and Personalization 467
26.2 Customer Segmentation and Targeting Strategies 472
26.3 Content Personalization and Dynamic Messaging 478
26.4 Optimizing Customer Journeys with Data Insights 483
26.5 The Role of Artificial Intelligence in Personalization 487
26.6 Personalization and Privacy: Balancing Data Ethics 492
26.7 Personalization in Omnichannel Marketing 497
26.8 Personalization in E-Commerce and Retail 501
26.9 Conclusion 506
References 509
Index 513
1
Crafting Effective AI Adoption Strategies
Aarti Neema1 and Rashid Khan2*
1Department of Electronics & Communication Engineering, Galgotias University, Greater Noida, India
2Department of Mechanical Engineering, Galgotias University, Greater Noida, India
Abstract
In this chapter, we delve into vital aspects of constructing successful artificial intelligence (AI) adoption strategies for businesses. The integration of AI has immense potential to revolutionize industries and spur growth, necessitating a purposeful approach. We emphasize understanding business objectives and aligning AI initiatives with overall goals for cohesive implementation.
A readiness assessment is fundamental in evaluating technological maturity, data infrastructure, and AI expertise. Recognizing capabilities and limitations is crucial for setting expectations and allocating resources effectively. Data's significance as AI's foundation is explored. Acquiring, managing, and utilizing high-quality data is pivotal. Breaking data silos and ensuring data privacy are highlighted.
Building a proficient AI team is essential. A diverse team with AI, data science, and domain expertise identifies use cases and drives insights. Employee training ensures adaptability. Selecting suitable AI solutions requires a structured evaluation process. Pilot projects test feasibility before larger implementation.
Ethics and transparency are addressed through strong frameworks and clear communication about AI's use. Managing change and resistance is vital. Involving employees, highlighting AI's potential, and providing support to mitigate resistance. Measuring success via key performance indicators (KPIs) and metrics is critical. Regular evaluation informs data-driven decisions and strategy refinement.
In conclusion, businesses are equipped to craft effective AI adoption strategies. By aligning with objectives, fostering a data-driven culture, investing in talent and technology, and upholding ethics, AI's power optimizes operations, enhances customer experiences, and secures a competitive edge. A well-crafted strategy empowers businesses to navigate evolving tech landscapes and unlock growth and innovation potentials.
Keywords: AI adoption, readiness assessment, data infrastructure, employee training, ethics, transparency, change management, data-driven decisions
1.1 Introduction
In today's dynamic business realm, the integration of artificial intelligence (AI) signifies a pivotal shift [1]. This chapter navigates the realm of crafting potent AI adoption strategies, spotlighting the art of aligning AI's power with organizational goals [2]. Artificial intelligence's prowess in data analysis, trend prediction, and automation has vast implications, demanding a meticulous approach beyond technology implementation.
This journey commences with understanding business objectives and juxtaposing traditional seller-centric and customer-centric approaches with AI paradigms [3]. Evaluating technological readiness, data infrastructure, and workforce skills forms the core of the readiness assessment, underpinning successful AI adoption [4].
Data takes center stage as the fuel for AI engines, reinforcing the significance of robust data management and security [5]. Assembling a skilled AI dream team bridges expertise gaps and aids in recognizing AI use cases tailored to business goals [6].
Selecting fitting AI solutions is a calculated endeavor [7]. Ethical implications underscore transparency, while change management strategies combat resistance to AI adoption [8, 9]. Establishing key performance indicators (KPIs) and iterative improvement cycles ensures AI's transformative impact is quantified and optimized [10].
This chapter's exploration paves the way for a deeper dive into each element, equipping readers to orchestrate AI's potential within the business terrain effectively.
1.2 Understanding Business Objectives
In the landscape of AI adoption, the compass guiding every strategic decision is a thorough understanding of the organization's business objectives. With AI's transformative potential, aligning AI initiatives with these objectives becomes the bedrock of a successful adoption strategy. Figure 1.1 shows a visual representation of the flow from overarching business goals to aligning AI initiatives, setting clear objectives, and involving stakeholders in the process [3].
1.2.1 Aligning AI with Business Goals
Artificial intelligence adoption must be an enabler of overarching business goals. Whether the aim is to enhance customer engagement, optimize operations, or innovate products, AI initiatives should seamlessly integrate with and contribute to these goals [7].
1.2.2 Defining Clear Objectives
Ambiguity in objectives can lead to inefficiencies and misaligned efforts. Each AI initiative should establish clear, specific, and measurable objectives. These objectives provide a yardstick for evaluating success and refining strategies [1].
1.2.3 Incorporating Stakeholder Input
An inclusive approach involves soliciting input from various stakeholders across departments. By involving key players in AI strategy development, a holistic perspective is gained, ensuring that AI efforts align with the collective vision of the organization [2].
Figure 1.1 Visual representation of business objectives.
As an example, consider a retail company aiming to improve customer retention. By aligning AI efforts with this objective, the company might implement AI-driven recommendation systems to personalize product offerings, enhancing the customer experience and fostering loyalty [4].
1.3 Seller-Centric and Customer-Centric Approaches
In the ever-evolving landscape of business strategies, two fundamental approaches have historically steered decision-making: the seller-centric approach and the customer-centric approach. These bedrock concepts illuminate how businesses position themselves in the market and engage with their target audience. As organizations embrace the transformative potential of AI adoption, it is crucial to navigate how these time-honored paradigms intersect with the strategic integration of AI.
As an example, imagine a manufacturing entity that adopts a seller-centric approach. This organization could employ AI to optimize its supply chain logistics, forecast equipment maintenance requirements, and enhance overall operational efficiency. Conversely, picture an e-commerce platform implementing a customer-centric approach. Such a platform might utilize AI algorithms to analyze users' browsing patterns and purchase histories, thereby generating personalized product recommendations and tailoring the shopping journey.
The examples shed light on how AI applications align with each approach. In the manufacturing context, AI optimizes internal operations, boosting efficiency. In the e-commerce realm, AI drives personalized experiences, translating into improved customer satisfaction and loyalty.
1.3.1 Seller-Centric Approach
The seller-centric approach is a cornerstone of traditional business thinking, with a focal point on the products or solutions offered. This approach spotlights the offerings' inherent qualities, functionalities, and attributes. In the context of AI adoption, the seller-centric approach involves leveraging AI technologies to amplify the capabilities of products and solutions. Artificial intelligence becomes a toolkit to optimize internal processes, bolster productivity, and unearth novel revenue streams through innovative applications [11].
1.3.2 Customer-Centric Approach
On the other side of the spectrum is the customer-centric approach, which hinges on delivering value and addressing the specific needs of customers. This approach necessitates an intimate comprehension of customer personas, preferences, and pain points. In the context of AI adoption, the customer-centric approach entails harnessing AI technologies to enrich customer experiences, offer tailor-made solutions, and streamline interactions [12]. Artificial intelligence becomes the conduit to gather and decipher customer data, enabling businesses to tailor offerings to individual preferences, predict customer behavior, and elevate engagement.
1.4 Comparison of Seller-Centric Approach and Customer-Centric Approach
Table 1.1 gives a comparison between the seller-centric approach and the customer-centric approach.
Table 1.1 Comparison of the seller-centric and customer-centric approaches.
S. n. Aspect Seller-centric approach Customer-centric approach 1 Focus Product features Customer value and needs 2 AI application Internal process enhancement Personalized customer experiences 3 Goal Internal efficiency Customer loyalty and satisfaction 4 Data utilization Operational metrics Customer behavior analysis 5 Strategic advantage Product enhancement and innovation Strong customer...