Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1s (2025) 143 https://internationalpubls.com An Analysis of Artificial Intelligence Implications and its Impact on Marketing. A Systematic Review Simerjeet Singh Bawa1, Kishore Kunal2, Kismat Kaur3, Jyotsna Sharma4,Vishal Srivastava5, Prerna Tikku6 1Professor, Chitkara School of Business, Chitkara University, Punjab 2Professor, Loyola Institute of Business Administration, Nungambakkam, Chennai. 3Associate Professor, Chandigarh Business School of Administration, Chandigarh Group of Colleges, Landran, Mohali, Punjab. 4Associate Professor, Department of Management Studies and Commerce, RIMT University, Mandi Gobindgarh, Punjab. 5Associate Professor, School of Commerce, Jain University, Bangalore, Karnataka. 6Assistant Professor, Chandigarh Business School of Administration, Chandigarh Group of Colleges, Landran, Mohali, Punjab. Article History: Received: 08-08-2024 Revised: 24-09-2024 Accepted: 11-10-2024 Abstract: Artificial intelligence (AI) is being used by marketers to track and forecast the future purchasing behaviour of their target audience and to gain further insights into their journey. The three areas of artificial intelligence that best demonstrate its potential are machine learning, big data, and resilient solution. Big data allows marketers to quickly and manually sort through enormous amounts of data and combine it. Businesses can use this information to ensure that the correct message is sent through the right channel to the right target at the right time. Marketers can use machine learning (deep learning) to analyse and interpret vast amounts of data. They have the ability to predict consumption trends and keep an eye on, assess, and analyse consumer spending and behaviour. Effective decision-making proves that we live in a time when robots can think like humans and have a complete comprehension of the universe. Automated systems have the ability to promptly identify concepts and trends throughout diverse datasets, interpret human emotions and actions, and provide pertinent customer support. They have the ability to quickly predict the decisions and actions of their clients and use that knowledge to solve issues down the road. More innovative and intelligent ads, reliance on bots, better content delivery, continuous learning, prevention of fraud and data breaches, sentiment analysis, voice and image recognition, predictive customer services, sales forecast, customer segmentation, etc. are just a few of the ways that marketers can expect a greater impact from AI in the coming years. The study aims to explore how artificial intelligence has changed the marketing environment overall, as well as to better comprehend the function that AI plays in it. Keywords: Artificial Intelligence (AI), Big Data, Machines, Marketers, Consumer Behaviour, Solutions. Introduction Artificial Intelligence, which includes capabilities like image and voice recognition, semantic search, decision-making, and machine learning techniques, is the replication of human intelligence processes by computers. The next big thing, artificial intelligence, or AI, is predicted to completely upend digital technology. The "big data" is changing how people work, live, and conduct business, as well as how they shop and purchase movie tickets. Each piece of data is divided into segments, which are then utilised to assess how consumers think and choose products (Fu et al., 2023). One must abandon the current Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1s (2025) 144 https://internationalpubls.com antiquated and non-innovative solutions in order to seize this chance. These days, new technologies make it easy to forecast consumer trends and look more closely at the decision-making process involved in consumer purchases (Ali & Johl, 2023). Since 2013, the investment in AI has increased at a rate that has roughly tripled, although only 20% of the market has adopted AI altogether (McKinsey, 2017). organisations that outperform their competitors financially are more likely (88%) than other organisations (57%) to think they are ready to employ cognitive computing, according to a study done by the IBM Institute for Business Value. Furthermore, they are more likely to believe that artificial intelligence will be essential for the future prospects of their company (92% vs. 63%) and that the technology is advanced enough to be economically viable (94% vs. 58%). (Ellett, John’s 2017). The digital frontier will see a increase in the use of AI, expanding the divide between leaders and laggards in a variety of sectors, locales, and businesses. Significant expenditures are being made in the sectors closest to the company's core, even if AI is being employed at every stage of the value chain. According to Peter Drucker, marketing and innovation are a company's two main responsibilities. As a result, sales and marketing are a crucial linkage with value chain and has witnessed a great adoption of AI since they produce enormous amounts of data. (McKinsey 2017). Although AI applications are employed globally along the entire value chain, an analysis of their advantages from an Indian perspective has not yet been done. We still don't know how Indian firms are using AI in day-to-day operations or whether Indian consumers are ready for AI. Literature Review In the twenty-first century, almost every field has become a significant area of study, including engineering, medicine, education, science, business, marketing, accounting, economics, law, stock markets and finance (Masnikosa 1998, Halal 2003, Raynor 2000, Metaxiotis et al. 2003, Tay and Ho 1992, Zhozhikashvili and Stefanuk 2002). Keeping up with the increase of studies in the field of artificial intelligence has become difficult (Balazinski et al. 2002, Knoblock and Ambite 2001, Goyache 2003 and Cristani 1999). Artificial intelligence as opposed to human intelligence, is the intelligence exhibited by machines. The most common definition of artificial intelligence is a system of intelligent agent machines that can efficiently finish a task by observing its environment. Norvig and Russell, 2016, define artificial intelligence as the term used to characterize machines (computers) that replicate the emotional and cognitive processes of the human mind. Because of the experts' hard work over the last few decades, artificial intelligence has advanced dramatically. Some significant advancements came from the study, such as the use of machine learning and big data analytics in a variety of contexts and industries (Han et al., 2021; McElheran et al., 2024; Polisetty et al., 2023; Alwadain et al., 2024) AI-powered equipment is designed to be aware of its environment and react appropriately. They observe their surroundings, react appropriately, and consider what might happen in the future. For example, AI can forecast when a machine will break down based on past data. It has the ability to alert us before we act. The market for AI is expected to reach $80 billion by 2025, and it will have a profound impact on consumer, business, and government markets all around the world. Although there are still obstacles to be solved, customers believe AI may help develop medicine, democratise pricey services, enhance mediocre customer care, and perhaps unleash a suppressed power. Some academic optimists believe artificial intelligence (AI) will result in a future where people's talents will be increased because robots will assist humans in processing, assessing, and appraising the vast amount of knowledge that Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1s (2025) 145 https://internationalpubls.com makes up the modern world. This will free up more time for people to engage in higher-order thinking, creativity, and decision-making (Verma et al. 2021). It's crucial to understand how people currently perceive AI, how it will change the world in the future, and what guiding principles will be required to navigate this enormous shift (PwC, 2017). Research Objectives This paper has the following objectives 1. To understand the concept of AI. 2. To investigate how AI improves consumer convenience, which increases an organization's sales and market share. Research Methodology We used a qualitative method to better understand consumer acceptance of AI in order to evaluate the influence of AI on the broader marketing environment in India. The following parameters of comparison were used in the creation of the questionnaire to align with the objectives of the study, which were to understand Indian customers' adoption of AI: i. A willingness to personalize services using AI. ii. AI's simplicity of usage iii. The use of AI in practice Customer acceptance of AI is referred to as adoption, whereas service personalisation is the process of tailoring services to individual consumers' interests. The millennial who answered the question didn't fall between the 22 and 37 age range. Millennials were chosen for the survey because they are early adopters of cutting-edge technology and will profit from it in the future. 276 replies were assessed out of the 302 comments that were received, 26 of which were outliers. Convenience sampling was used. To analyse the collected data, SPSS was utilised. The initial section of the questionnaire gathered information on respondents' age, gender, income, and frequency of internet use. Other questions measured respondents' agreement or disagreement with the use of artificial intelligence (AI) in customer support services, online stores, and virtual assistants. Result and Discussions 1. Personalisation By comprehending customer preferences and customising the service accordingly, AI aids in delivering a more personalised service experience. From a research standpoint, understanding Indian customers' inclination towards service personalisation is crucial before estimating the extent to which use of AI in personalisation might impact India. Hypothesis: H0A: There is no interest in implementing AI to personalize services. H1A: There is a willingness to utilise AI for service personalization. When creating the research questionnaire, consideration was given to the inclination of Indian consumers for a personalised shopping experience on e-commerce platforms. This was a result of the target audience's extensive knowledge with the same. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1s (2025) 146 https://internationalpubls.com Table 1.1: Personalization Preferences Cross tabulation Purchases using E-Commerce website, After Recommendations Total Yes No Number 25 65 90 % Preferences for Personalization 27.78% 72.22% 100.00% Total Percentage 9.19% 23.89% 33.08% Number 36 42 78 % Preferences for Personalization 46.10% 53.90% 100.00% Total Percentage 13.00% 15.20% 28.30% Table 1.2: Chi - Square Test Val df. Assymp. sign. (2 - side) Pearson Chi-Square 7.720a 2 0.021 Table 1.3: Symmetric Measure Value Assymp. Std . Errora Approx . Tb Apprx. Sign. Nominal by Nominal phi 0.167 0.21 Crammer's V 0.167 0.21 Contingency Coefficient 0.165 0.21 As can be seen in the above table, the majority of respondents who do not presently rely their purchases on suggestions from e-commerce websites believe that these recommendations should be more tailored to the person rather than being overly generic. Table 2.1: Usefulness of advice offered on e-commerce websites Cross tabulation Attitude toward purchases made on websites offering a virtual trial Neutral Likely Most Likely Number 15 36 18 % Usefulness of advice offered on e- commerce websites 20.10% 48.00% 24.00% Total Percentage 5.50% 13.00% 6.50% Number 24 51 36 % Usefulness of advice offered on e- commerce websites 17.80% 37.80% 25.90% Total Percentage 8.70% 18.60% 13.10% Number 15 9 6 % Usefulness of advice offered on e- commerce websites 33.30% 20.10% 13.20% Total Percentage 5.40% 3.30% 2.20% Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1s (2025) 147 https://internationalpubls.com Table 2.2: Chi - Square Test Val df. Assymp. Sign. (2- side) Pearsn Chi - Square 64.927a 16 .000 Table 2.3: Symmetric Measure Val Assymp. Std . Errora Apprx. Tb Apprx. Sign. Nominal by Nominal Phi 0.488 .000 Crammer's V 0.243 .000 Contingency Coefficient 0.429 .000 According to the above table, respondents who are unsure about the guidance given by e-commerce websites have revealed a greater preference towards online purchasing when a virtual trial is available. Hypothesis Results Given a 95% confidence level, it is evident that the Cramer’s V and PHI value is significance and lesser than 0.05 in both cases. Consequently, the null hypothesis is refuted. Therefore, it can be concluded that there is a readiness to use AI to personalize services. 2. Customer Services By automating customer support services, AI can not only help enterprises cut costs but also improve consumer convenience. From a research standpoint, it is essential to comprehend Indian consumers' comfort levels and preference for using automated customer support services before estimating how big of an impact acceptance of artificial intelligence application for Customer Support Service would have from an Indian perspective. Hypothesis: H0B: There is no interest in implementing AI in applications for customer support services. H1B: Applications for customer support services are open to include AI. Given the nature of the powered by AI current application for supporting customer services, which primarily use texting as a communication mode, the research questionnaire was designed using Indian consumers' preferences for the communication mode with customers support and their perceptions of the usefulness of each mode of communication. Table 3.1: Communication Preference Cross tabulation Texting is easier to use than calling Extremely Easy Easy Neutral Difficult Number 15 54 93 30 % of Communication Preferences 7.50% 26.90% 46.30% 14.90% Total Percentage 5.40% 19.60% 33.70% 10.90% Number 18 18 21 12 % of Communication Preference 24.00% 24.00% 28.00% 16.00% Total Percentage 6.50% 6.50% 7.60% 4.30% Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1s (2025) 148 https://internationalpubls.com Table 3.2: Chi - Square Test Val df. Assymp. Sign. (2 - side) Peerson Chi - Square 19.36a 4 .001 Table 3.3: Symmetric Measure Val Assymp. Std . Errora Appro x. Tb Approx. Sign. Nominal by Nominal Phi 0.258 .001 Crammer's V 0.258 .001 Contingency Coefficient 0.25 .001 If texting is perceived as being more convenient than phoning, the aforementioned data shows that phone usage is still preferred over texting. This can be explained by the fact that calling establishes a stronger emotional connection because a human is involved. Texting may become the preferred method of communication if AI is used to enable texting incorporate the human element. Hypothesis Results: Assuming a 95% confidence level, it can be demonstrated that the significance value of Cramer's V is 0.001, or lesser than 0.05. Consequently, the null hypothesis is refuted. Consequently, there is a readiness to use AI in customer support applications. Conclusion While there are a number of obstacles to be overcome, AI has the ability to both address many current issues and go far beyond. Companies change their operational procedures to boost productivity, responsiveness, and competition. Technical developments have always created new marketing options. Artificial intelligence will completely change the way that people interact with services, products, technology and information. 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