Human-ai Collaboration In Marketing Decision- Making With Special Reference To Business Located At Coimbatore City

IJSART - Volume 11 Issue 9 – SEPTEMBER 2025 ISSN [ONLINE]: 2395-1052 Page | 76 www.ijsart.com Human-AI Collaboration In Marketing DecisionMaking With Special Reference To Business Located At Coimbatore City Mr. Vishnu S1, Dr.G.Sowmya2 1Dept of Commerce with Professional Accounting 2Assist.Professor, Dept of Commerce Retail Marketing with ACCA 1, 2Sri Ramakrishna College of Arts and Science, Coimbatore. AbstractThis study explores the role of Artificial Intelligence (AI) in marketing, with a focus on businesses in Coimbatore. AI has become a transformative tool that enables organizations to analyse consumer data, predict buying behaviour, personalize marketing campaigns, and improve decision-making. The research evaluates the impact of AI on marketing strategies, its contribution to consumer convenience, and its potential to increase sales and market share. At the same time, it identifies key challenges such as high implementation costs, lack of skilled professionals, and data privacy concerns, which hinder effective adoption. Based on responses from 66 participants, the findings highlight that while AI adoption is steadily increasing, greater awareness, skill development, and strategic integration are essential to maximize its benefits. The study concludes that AI is not only a driver of efficiency and personalization but also a critical factor in creating sustainable, customer-centric marketing practices. KeywordsArtificial Intelligence (AI), Marketing Strategies, Consumer Behaviour. I. INTRODUCTION Artificial Intelligence (AI) is one of the most powerful and rapidly growing technologies of the modern era. Over the past decade, AI has made remarkable progress and has been adopted across various industries such as healthcare, education, finance, transport, and marketing. Among these, marketing has seen a significant transformation with the integration of AI, making business strategies more datadriven, accurate, and customer-focused. The role of AI in marketing is not limited to automation alone. It provides businesses with the ability to analyse massive volumes of customer data, identify buying patterns, and predict consumer behaviour. Through AI-powered tools such as chatbots, recommendation engines, voice assistants, and targeted advertising platforms, companies can now personalize their services and deliver a more engaging customer experience. Furthermore, AI enables marketers to optimize their campaigns in real time, reduce costs, and increase efficiency. It helps in segmenting audiences, improving content strategies, forecasting demand, and even enhancing customer relationships through predictive analysis. With the growing digital marketplace and increasing competition, AI has become a key driver for businesses to stay relevant and innovative. In short, Artificial Intelligence has revolutionized marketing by shifting the focus from traditional approaches to smarter, technology-driven strategies. As businesses continue to adopt AI, its role in shaping the future of marketing will only become more significant. II. STATEMENT OF PROBLEM The way companies engage with customers and develop their marketing strategies has undergone a transformation thanks to the fast development of artificial intelligence (AI). Despite the potential of AI for personalization, predictive analytics, and automated decision-making, many businesses have difficulty implementing these technologies successfully. Among the difficulties are the exorbitant cost of implementation, the shortage of qualified personnel, ethical issues pertaining to data privacy, and marketers' restricted knowledge of AI tools. Consequently, there is a disconnect between the potential of AI in marketing and its real-world implementation. The goal of this research has been to increase the Business's use of AI in its marketing plans in order to tackle this. III. OBJECTIVE OF THE STUDY  To evaluate how AI is currently impacting marketing strategies  To study how AI enhances consumer convenience leading to increased sales and market share for an organization  Explore the challenges and obstacles encountered in the integration of AI into marketing practices IJSART - Volume 11 Issue 9 – SEPTEMBER 2025 ISSN [ONLINE]: 2395-1052 Page | 77 www.ijsart.com IV. REVIEW OF LITERATURE Mariani, M., et al (2022), Conducted research about the “AI in marketing, consumer research and psychology”. A systematic literature review and research agenda. To provide an integrated, comprehensive view of the body of knowledge on AI as represented in marketing, consumer research, and psychology literature; to map the intellectual structure and identify theoretical lenses, themes, gaps, and propose a future research agenda. Systematic literature review; quantitative bibliometric methods including bibliographic coupling; identification of topical clusters; analysis of theoretical lenses used in the studies. AI is deeply embedded in various aspects of marketing, psychology, consumer research; there are rich theoretical underpinnings but also gaps—some topics underexplored. They propose research directions such as cross fertilization of theories across fields, addressing neglected themes, deeper psychological and cognitive work, and more empirical studies. Sheikh Khurshid Alam, Imran Uddin et al (2025),Conducted the study about “With a particular emphasis on how AI impacts or engages consumers”, to analyse literature up to 2021 on AI applications in marketing; to explore how AI contributes to making marketing procedures simpler.Thematic analysis; gathering papers from Google Scholar and Scopus; evaluating research using AI technologies; mapping themes appearing in the literature on AI marketing. Although AI shows tremendous promise in revolutionizing many marketing tasks and consumer engagement, further research is necessary, particularly empirical studies, wider adoption, and a better understanding of how consumers perceive and react to AI. Chintalapati, et al (2021), Conducted the study about“Artificial intelligence in marketing: A systematic literature review”. To explore how digital transformation via AI is influencing marketing; to classify the current literature into marketing functional themes and use-cases; to identify how AI is being leveraged for superior marketing outcomes. Systematic literature review (SLR); qualitative and quantitative evaluation of literature; categorization into functional themes; identification of use-cases; evaluation of 57 publications. AI is being applied in many diverse marketing functional areas; however, depth and spread vary; there is room for more rigorous empirical work and better clarity on impact metrics. The paper also outlines implications for practitioners and suggests future research agendas to explore understudied sub-themes. Zyminkowska.K; Zachurzok-Srebrny.E (2025), Analysed the study about“The Role of Artificial Intelligence in Customer Engagement and Social Media Marketing”— Implications from a Systematic Review for the Tourism and Hospitality Sectors. To examine how AI is used for customer engagement and in social media marketing within the tourism and hospitality sectors. To draw implications from those uses for both theory and practice. Systematic review of existing literature; focusing specifically on studies in the tourism & hospitality sector; examining themes, uses, effects; likely content analysis of reviewed articles. AI has considerable influence in improving customer engagement via social media in tourism & hospitality, but there are challenges: ethical issues, trust, data privacy, and a need for more longitudinal and empirical studies in these sectors. Recommendations for future work include deeper investigations of effects on customer behaviour, integratingAI with human touch, and balancing automation with service quality. Yusup, M., et al (2024),Conducted the study about “The Role of Artificial Intelligence in Digital Marketing: A Systematic Literature Review”, To discover journal publications pertaining to AI's function, consider analytical techniques, applications, and performance indicators in AI in digital marketing; to address research questions about these measurements over the time (2015–2022). review of publications during the specified time using the systematic literature review (SLR) approach, inclusion/exclusion criteria, and extraction of the research's analytical strategies, application types, performance measurements, and mapping. Although AI is a significant element of digital marketing, more thorough research is needed to connect the use of AI with accurate performance metrics in order to advance the discipline. Additionally, there is room for improvement in the use of a wider range of analytics tools, more diverse environments, cross-cultural research, and so on. V. RESEARCH METHODOLOGY A research design is the methodical plan of action to be carried out in association with a proposed research work. The study carries descriptive method. AREA OF THE STUDY: The research is confined to Coimbatore city, one of the fastest-growing industrial and commercial hubs in Tamil Nadu. Coimbatore provides a suitable setting for this study because of its diverse mix of industries including textiles, manufacturing, IT services, retail, and educational institutions. These sectors are increasingly integrating AI into their marketing activities, making the city an appropriate location for this research. IJSART - Volume 11 Issue 9 – SEPTEMBER 2025 ISSN [ONLINE]: 2395-1052 Page | 78 www.ijsart.com TOOLS USED: The Tools used for the study is Percentage Analysis. RESEARCH DESIGN: The present study adopts a descriptive research design. This design was chosen because it helps to describe and analyse how businesses in Coimbatore are adopting Artificial Intelligence (AI) in their marketing strategies, what challenges they face, and what impact it has on customer engagement and organizational growth. POPULATION AND SAMPLING: The study includes business organizations and marketing professionals in Coimbatore who are either currently using or planning to use AI-based marketing tools. Sample Size: A total of 66 respondents were selected for the study. Sampling Technique: The study employed a convenience sampling method, as respondents were chosen based on accessibility and willingness to participate. DATA COLLECTION: The study contains both Primary and Secondary data for the research. Primary Data: Collected through a structured questionnaire administered to business owners, managers, and marketing executives in Coimbatore. Secondary Data: Collected from journals, books, research articles, company reports, and online sources related to Artificial Intelligence and marketing practices. LIMITATON OF THE STUDY  The study focuses only on business located within Coimbatore. Findings may not applicable to other cities or regions.  The 66 responses may not represent the large population. The large sample could have provided more diversified perspective.  The research is confined to AI in marketing only. The results may not relate to traditional. VI. ANALYSIS AND INTERPRETATION TABLE NO 1: SOCIO-ECONOMIC PROFILES OF THE RESPONDENSE VARIABLE GROUP NO OF RESPONS ES PERCENTA GE (%) AGE 18-25 34 51.52 26-40 20 30.3 41-60 12 18.18 Above 60 0 0 GENDER Male 49 74.24 Female 17 25.76 EDUCATIO N LEVEL Uneducated 6 9.09 Till 12th 0 0 Under Graduate 30 45.45 Post Graduate 15 22.73 Professional 15 22.73 BUSINESS REVENUE 0-25000 21 31.82 2500050000 18 27.27 5000075000 21 31.82 75000100000 6 9.09 Above 100000 0 0 BUSINESS TYPE Sole Proprietorsh ip 22 33.33 Partnership 22 33.33 Public Limited 2 3.03 Private Limited 4 6.06 Non-Profit Organizatio n 16 24.24 LOCATION Urban 65 98.48 Rural 1 1.52 IJSART - Volume 11 Issue 9 – SEPTEMBER 2025 ISSN [ONLINE]: 2395-1052 Page | 79 www.ijsart.com NO OF YEARS IN OPERATIO -N OF BUSINESS (IN YEARS) 0-5 29 43.94 6-10 20 30.3 11-15 9 13.64 15-20 4 6.06 Above 20 4 6.06 SOURCE OF DATA: PRIMARY DATA It is evident from the table that most of the responses are obtained from the age group of 1825 years (51.52%). The majority are Male (74.24%). The respondents are mostly Under Graduates (45.45%). Most of their earnings ranges between Less than RS.25000 & RS.50000 – RS.75000 (31.82%). Most of the respondents are involved in Sole Proprietorship & Partnership (33.33%). Business majority of the responses are from the urban (98.48%). With mostly having business experience of Less than 5 years (43.94%). TABLE NO 2: AI-IMPACT ON MARKETING STRATEGIES MARKETI NG STRATEG IES VERY HIGH IMPA CT HIGH IMPA CT NEUT RAL LOW IMPA CT VER Y LO W IMP ACT Data-Driven Decision Making 7 (10.60 %) 18 (27.27 %) 30 (45.45 %) 8 (12.12 %) 3 (4.54 %) Personalizat ion and Customer Targeting 1 (1.51% ) 31 (46.96 %) 32 (48.48 %) 2 (3.03 %) 0 (0%) Chatbot and Customer Support 4 (6.06% ) 8 (12.12 %) 19 (28.78 %) 25 (37.87 %) 10 (15.1 5%) Small Businesses with Limited Data 1 (1.51% ) 9 (13.63 %) 41 (62.12 %) 13 (19.69 %) 2 (3.03 %) Traditional, Non-Digital Markets 18 (27.27 %) 35 (53.03 %) 9 (13.63 %) 3 (4.54 %) 1 (1.51 %) SOURCE OF DATA: PRIMARY DATA It is evident from the table that Small Business with Limited Data (62.12%), Personalization & Customer Targeting (48.48%) and Data-Driven Decision Making (45.45%)marketing strategies have neither high impact nor low impact by AI, followed by Traditional, Non-Digital Market (53.03%) have high impact by AI and Chatbot & Customer Support (37.87%), have low impact by AI. TABLE NO 3: EFFECTIVENESS OF AI TOOLS IMPROVING MARKETING STRATEGIES EFFECTIVENESS OBTAINED RESPONSES PERCENTAGE (%) Very Effective 10 13.89 Effective 41 56.94 Neutral 12 16.67 Ineffective 6 8.33 Very ineffective 3 4.17 SOURCE OF DATA: PRIMARY DATA The above table shows that the Effectiveness of AI tools improving marketing strategies. In which the respondents said it is effective56.94%. 16.67% of respondents opined as neither Effective nor Ineffective, 13.89% opined AI tools are Very effective, 8.33% opined as Ineffective, and the least 4.17% opined AI tools in marketing strategies as Very ineffective. TABLE NO 4: AI-DRIVEN SOLUTIONS IMPROVE CONSUMER CONVENIENCE FACTORS OBTAINED RESPONSES PERCENTAGE (%) Higher Customer Retention 41 23.03 Lower Operational Costs 30 16.85 Brand Loyalty and Trust 50 28.09 Personalized Recommendation 31 17.42 Data-Driven Business Strategies 26 14.61 SOURCE OF DATA: PRIMARY DATA The table clearly shows that the Brand Loyalty and Trust (28.09%), improved their consumer convenience through AI followed by Higher Customer Retention (23.03%),Personalized Recommendation (17.42%),Lower operational Costs (16.85%),and the least obtained response is Data-Driven Business Strategies (14.61%). IJSART - Volume 11 Issue 9 – SEPTEMBER 2025 ISSN [ONLINE]: 2395-1052 Page | 80 www.ijsart.com TABLE 5: CHALLENGES OF INTEGRATING AI INTO MARKETING PROCESSES FACTORS OBTAINED RESPOSES PERCENTAGE (%) High cost of implementation 27 17.09 Lack of skilled professionals 44 27.85 Data privacy concerns 25 15.82 Technology complexity 27 17.09 Resistance to change 35 22.15 SOURCE OF DATA: PRIMARY DATA The table clearly evident that 27.85% of respondents mentioned Lack of Skilled Professionals as barrier in integrating AI into marketing process, followed by Resistance to Change (22.15%), then the both High cost of Implementation and Technology Complexity(17.09%),and the least opted is Data Privacy Concerns (15.82%). VII. CONCLUSION The study highlights that Artificial Intelligence plays a transformative role in modern marketing by enabling datadriven decision-making, personalization, and enhanced consumer engagement. AI applications such as predictive analytics, chatbots, and targeted advertising are reshaping how organizations interact with customers and expand market reach. While businesses in Coimbatore show growing interest in adopting AI, challenges such as high implementation costs, lack of expertise, and concerns over data privacy continue to limit its full potential. Nevertheless, the findings indicate that AI has significant potential to boost sales, improve customer convenience, and strengthen competitiveness. To harness these benefits effectively, organizations must invest in skill development, ethical data practices, and strategic integration of AI technologies. Overall, AI is not just a technological tool but a catalyst for creating sustainable, customer-centric marketing strategies in the digital era. REFERENCES [1] Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755-776. [2] Wu, C. W., & Monfort, A. (2023). Role of artificial intelligence in marketing strategies and performance. Psychology & Marketing, 40(3), 484-496. [3] Eriksson, T., Bigi, A., & Bonera M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795-814. [4] Basha, M. (2023). Impact of artificial intelligence on marketing. East Asian Journal of Multidisciplinary Research, 2(3), 993-1004. [5] Kumar, V., Rajan, B., Venkatesan, R., & Lecinski J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California management review, 61(4), 135-155. [6] https://journals.sagepub.com/doi/abs/10.1177/147078532 11018428 [7] https://www.emerald.com/insight/content/doi/10.1108/TQ M-12-20190303/full/html [8] https://www.sciencedirect.com/science/article/pii/S26670 96820300021 [9] https://journals.sagepub.com/doi/abs/10.1016/j.intmar.202 0.04.007 [10] http://ecbis.net/index.php/go/article/view/130