AI Customer Engagement Strategies on Enterprise Growth in Kenya: Adaptation and Navigation

AI Customer Engagement Strategies on Enterprise Growth in Kenya: Adaptation and Navigation

Dr. Sedina Misango, Dr. Faraji Yatundu, Dr. Janet Mulwa

South Eastern Kenya University

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90400513

Received: 19 April 2024; Accepted: 23 April 2025; Published: 26 May 2025

ABSTRACT

The adoption of Artificial Intelligence (AI) in customer engagement is transforming enterprise growth in Kenya by enhancing service delivery, marketing strategies, and decision-making processes. This study examines the adaptation and navigation of AI-driven customer engagement strategies and their influence on enterprise growth in Kenya. Specifically, it evaluates the effect of automated customer support and personalized marketing on business growth. The study employs a secondary data analysis methodology, drawing from existing scholarly articles, industry reports, and case studies on AI adoption in customer engagement. By synthesizing data from reputable sources, this research identifies trends, challenges, and opportunities associated with AI implementation in Kenyan enterprises. The findings contribute to knowledge by providing empirical insights into how AI-driven customer engagement strategies enhance operational efficiency, customer satisfaction, and business expansion. From a policy perspective, the study offers recommendations on AI governance, data privacy, and infrastructure development to support AI adoption in Kenya’s business ecosystem. Additionally, the study informs enterprise decision-makers on best practices for leveraging AI technologies to optimize customer interactions and drive sustainable growth.

Keywords: AI Customer Engagement, Enterprise Growth, Automated Customer Support, Personalized Marketing

BACKGROUND INFORMATION

The rapid advancement of artificial intelligence (AI) is transforming customer engagement strategies, enabling businesses to enhance service delivery, personalize customer interactions, and optimize decision-making processes (Chatterjee et al., 2021). Customer engagement strategies refer to business practices designed to create meaningful interactions with customers, fostering emotional and behavioral connections that enhance loyalty and business performance (Brodie et al., 2011). These strategies leverage digital tools, personalized interactions, and AI-driven solutions to create meaningful experiences that keep customers actively involved with a company’s products or services (Van Doorn et al., 2010).

In Kenya, enterprises are increasingly adopting AI-driven customer engagement solutions such as automated customer support, personalized marketing, predictive analytics and voice and conversational AI to improve customer experiences and drive business growth (Muthoki & Wambua, 2022). These AI technologies are redefining how businesses interact with customers by streamlining operations, enhancing efficiency and fostering customer satisfaction, ultimately contributing to enterprise expansion and competitive advantage (Kariuki et al., 2023).

Automated customer support, through AI-powered chatbots and virtual assistants, is revolutionizing customer service by providing real-time responses, reducing response times, and ensuring 24/7 availability (Huang & Rust, 2020). Personalized marketing, driven by AI algorithms, enables businesses to analyze consumer behavior and deliver tailored advertisements and recommendations, thereby increasing customer engagement and conversion rates (Kaplan & Haenlein, 2019).

Despite these advantages, Kenyan enterprises face several challenges in adopting AI-based customer engagement strategies. Limited access to technological infrastructure, data privacy concerns, high implementation costs, and a shortage of skilled AI professionals hinder effective deployment (Njoroge & Kamau, 2021). Moreover, businesses must navigate regulatory frameworks, consumer trust issues, and ethical considerations to successfully integrate AI into their customer engagement models (Chege & Wang, 2022).

Globally, the integration of artificial intelligence (AI) into customer engagement can be traced back to the early developments in machine learning and natural language processing in the mid-20th century. AI’s role in business operations gained traction with the introduction of expert systems in the 1980s, which allowed businesses to automate decision-making processes (Russell & Norvig, 2021). By the 1990s, the advent of the internet and e-commerce platforms such as Amazon and eBay introduced recommendation algorithms, enabling businesses to personalize customer experiences based on browsing and purchase history (Kaplan & Haenlein, 2019).

In the 21st century, AI-powered customer engagement evolved significantly with the rise of big data, cloud computing, and deep learning (Dwivedi et al., 2021). Businesses worldwide adopted AI-driven strategies, such as chatbots, predictive analytics, and voice assistants, to enhance customer interactions. Leading companies like Google, Amazon, and Microsoft pioneered AI applications such as virtual assistants (e.g., Alexa, Google Assistant) and AI-based customer service solutions (Grewal et al., 2020). The COVID-19 pandemic further accelerated AI adoption, as businesses sought digital solutions to maintain customer engagement amid restrictions and lockdowns (Huang & Rust, 2020).

AI adoption in customer engagement in Africa has been relatively slower than in developed regions due to infrastructural limitations and low digital literacy levels. However, in recent years, African businesses have increasingly embraced AI technologies to improve service delivery and customer interactions (Chege & Wang, 2022). Mobile-based AI solutions have gained prominence, particularly in the financial and telecommunications sectors. For example, Safaricom’s M-Pesa introduced AI-driven chatbots to enhance customer support, while banks such as Equity Bank and Standard Chartered Bank adopted AI-powered fraud detection and predictive analytics (Njoroge & Kamau, 2021).

Moreover, the e-commerce sector has witnessed a surge in AI-driven customer engagement strategies. Companies like Jumia and Kilimall leverage AI for personalized recommendations and automated customer service (Muthoki & Wambua, 2022). Governments and technology hubs, such as Kenya’s Konza Technopolis and Nigeria’s AI Hub, are also promoting AI adoption by supporting research and innovation in AI-driven customer engagement (Kariuki et al., 2023). Despite these advancements, challenges such as high implementation costs, regulatory gaps, and ethical concerns continue to hinder widespread AI adoption in Africa (Chege & Wang, 2022).

Kenya has emerged as a leader in AI-driven customer engagement in East Africa, with businesses increasingly leveraging AI to enhance customer interactions. The rise of fintech, e-commerce, and digital banking has spurred AI adoption, with institutions such as Kenya Commercial Bank (KCB) and Co-operative Bank utilizing AI-powered chatbots to handle customer inquiries (Njoroge & Kamau, 2021). The telecommunications industry, led by Safaricom, has also integrated AI-driven solutions, such as voice recognition and predictive analytics, to enhance customer service efficiency (Muthoki & Wambua, 2022).

Furthermore, AI-based personalized marketing has transformed the retail sector, with businesses using AI algorithms to analyze consumer preferences and deliver targeted advertisements. Companies such as Naivas and Quickmart have implemented AI-driven inventory management and customer engagement systems to improve service delivery (Kariuki et al., 2023). Government initiatives, such as the Kenya National AI Strategy, are also fostering AI adoption by creating a regulatory framework to guide businesses in implementing AI technologies (Chege & Wang, 2022).

Despite these advancements, Kenyan enterprises still face several challenges, including data privacy concerns, limited AI expertise, and resistance to change. However, the increasing investment in digital transformation and AI capacity-building initiatives indicates a promising future for AI-driven customer engagement in Kenya (Muthoki & Wambua, 2022).

This study sought to examine the effect of AI-driven customer engagement strategies on enterprise growth in Kenya. The study specifically evaluated the role of automated customer support and personalized marketing on enterprise growth in Kenya. By analyzing industry trends, challenges, and success factors, this research aims to provide insights into how AI technologies are shaping business performance and competitiveness in the Kenyan market.

The evolution of automated customer support has significantly transformed business operations globally. AI-powered chatbots and virtual assistants have become essential tools for handling customer inquiries, reducing response times, and improving service efficiency (Huang & Rust, 2020). Empirical studies show that businesses adopting AI-driven customer support experience enhanced customer satisfaction and operational efficiency. For instance, AI chatbots in e-commerce platforms have improved response accuracy and reduced operational costs by 30% (Luo et al. 2019) . In Africa, automated customer support has gained traction in the banking and telecommunications sectors. In Kenya, banks such as KCB and Equity Bank have integrated AI-powered chatbots to enhance real-time customer engagement, reducing wait times and improving user experience (Muthoki & Wambua, 2022). However, challenges such as data privacy concerns and AI system limitations remain key obstacles to widespread adoption (Njoroge & Kamau, 2021).

Personalized marketing has revolutionized consumer engagement by enabling businesses to tailor content and promotions based on customer behavior and preferences (Kaplan & Haenlein, 2019). AI-powered recommendation systems, which analyze big data and customer interactions, have significantly enhanced marketing efficiency. AI-driven personalized marketing has increased customer retention rates by 25% in the retail sector (Grewal et al. 2020). In Africa, personalized marketing has been successfully implemented in e-commerce platforms such as Jumia, which uses AI algorithms to suggest products based on user browsing history (Chege & Wang, 2022). In Kenya, supermarkets such as Naivas and Quickmart have adopted AI-powered loyalty programs that analyze purchasing patterns to offer targeted discounts and promotions (Kariuki et al., 2023). Despite these advancements, concerns over consumer data protection and ethical AI usage remain critical challenges for businesses (Njoroge & Kamau, 2021).

Statement of the problem

The rapid advancement of artificial intelligence (AI) has transformed customer engagement strategies across various industries, enhancing efficiency, personalization, and decision-making (Dwivedi et al., 2021). Globally, businesses leveraging AI-driven customer support and personalized marketing, have reported significant improvements in customer satisfaction and enterprise growth (Grewal et al., 2020). However, the adoption and impact of AI-powered customer engagement strategies in Kenya remain underexplored, with businesses facing challenges such as high implementation costs, data privacy concerns, and limited AI expertise (Chege & Wang, 2022).

Kenyan enterprises, particularly in the banking, telecommunications, and retail sectors, have begun integrating AI to enhance customer interactions. Financial institutions such as KCB and Equity Bank have implemented AI-powered chatbots to improve service delivery, while e-commerce platforms like Jumia use AI-driven recommendation systems to enhance customer experiences (Muthoki & Wambua, 2022). Despite these developments, there is a lack of empirical evidence on the effectiveness of AI-driven customer engagement strategies in driving enterprise growth in Kenya. Many businesses remain uncertain about the return on investment (ROI) and the long-term benefits of AI adoption in customer engagement (Njoroge & Kamau, 2021).

Furthermore, existing studies on AI adoption in Kenya primarily focus on general technological advancements rather than the specific impact of AI-driven customer engagement on business growth (Kariuki et al., 2023). This research gap necessitates a comprehensive analysis of how AI-powered customer support and personalized marketing, influence enterprise performance and competitiveness in Kenya. Addressing this gap will provide valuable insights for businesses, policymakers, and industry stakeholders to make informed decisions regarding AI adoption.

Study Objectives

The study was guided by the following study objectives

1. To evaluate the role of automated customer support on business growth in Kenya
2. To examine the role of personalized marketing on business growth in Kenya

LITERATURE REVIEW

Automated customer support as an AI customer engagement strategy on business growth

​The McKinsey & Company (2021) study titled “The State of AI in 2021” provides a comprehensive analysis of how organizations worldwide are adopting and leveraging artificial intelligence (AI), with a particular focus on its impact in customer service functions. The study spans multiple industries globally, emphasizing the integration of AI in customer service operations. It highlights how AI technologies are being utilized to enhance customer interactions, streamline service operations, and drive business value. The research adopts a positivist approach, aiming to objectively measure and analyze the extent of AI adoption and its tangible impacts on business functions, including customer service. The study adopted a Quantitative cross-sectional survey design and conducted an online survey from May 18 to June 29, 2021 on 1,843 participants from various regions, industries, company sizes, and functional specialties. . The data were weighted by the contribution of each respondent’s nation to global GDP to adjust for differences in response rates. The study conceptualizes AI adoption as the implementation of AI capabilities—such as machine learning, computer vision, and natural-language processing—in at least one business function. It examines the prevalence of AI use cases across different functions, including customer service, and assesses the impact on organizational performance.

The African Development Bank (AfDB) 2021 report examines the impact of automated customer support on business operations across Africa, focusing on the banking, telecommunications, and retail sectors. The study highlights how automation has enabled businesses to provide 24/7 customer service, contributing to business expansion and improved customer satisfaction. The report is set against the backdrop of Africa’s rapidly evolving digital landscape, where businesses are increasingly adopting automated solutions to meet growing customer demands. The banking, telecom, and retail sectors are at the forefront of this transformation, leveraging automation to enhance service delivery and operational efficiency. The study adopts a positivist research philosophy, aiming to objectively measure and analyze the effects of automated customer support on business performance. By utilizing quantitative data, the research seeks to establish clear relationships between automation and business outcomes. The study utilized Cross-sectional survey research design with Structured questionnaires distributed to businesses across the banking, telecom, and retail sectors.​Target Population included businesses operating within the specified sectors across various African countries. Stratified random sampling was used to ensure representation across different sectors and regions. The study conceptualizes automated customer support as a critical component of digital transformation, influencing various aspects of business operations that includes Customer accessibility where automation enables businesses to offer round-the-clock services, enhancing customer convenience. Operational efficiency with Automated systems that streamline processes, reducing the need for manual intervention and minimizing errors and business expansion through improving service delivery, businesses can attract and retain more customers, facilitating growth.

The study by Nkosi and Moyo (2023) investigates the integration and impact of Artificial Intelligence (AI) within South Africa’s e-commerce sector. Employing a case study approach, the research focuses on leading e-commerce platforms, analyzing sales data, customer feedback, and AI adoption metrics to understand how AI influences business performance and customer engagement. Situated in South Africa’s dynamic e-commerce landscape, the study examines how AI technologies are adopted by major online retailers to enhance operational efficiency and customer experience. The research is contextualized within a market experiencing rapid digital transformation and increasing competition from international players. Adopting a pragmatic research philosophy, the study combines qualitative and quantitative methods to provide a comprehensive understanding of AI’s role in e-commerce. This approach allows for flexibility in exploring complex phenomena through multiple data sources. The study adopted a Multiple case study design focusing on prominent South African e-commerce platforms. Data was Collected for analysis of sales data to assess performance metrics, there was Customer feedback analysis to gauge satisfaction and engagement with evaluation of AI adoption metrics to determine the extent and effectiveness of AI integration. Target Population included Leading South African e-commerce companies and their customer base with Purposive sampling used to select e-commerce platforms that have implemented AI technologies, ensuring relevance to the study’s objectives. The study conceptualizes AI adoption in e-commerce as a multifaceted process influencing various aspects of business operations, including Customer Engagement that involves Utilizing AI for personalized recommendations and interactive shopping experiences, Operational Efficiency necessitated through Implementing AI-driven logistics and inventory management systems and Competitive Advantage that is availed through Leveraging AI to differentiate services in a competitive market.

The study by Otieno and Wambua (2022) investigates the role of AI-driven chatbots in enhancing customer service within Kenyan commercial banks. Employing a descriptive survey design, the research gathers insights from both bank customers and staff to assess the impact of chatbot adoption on brand reputation and customer satisfaction. Set in Kenya’s banking industry, the study explores how commercial banks are integrating AI-driven chatbots to improve customer interactions and service delivery. The research aims to understand the effectiveness of chatbots in managing brand reputation and meeting customer expectations in a rapidly digitizing financial sector. The study adopts a positivist research philosophy, focusing on quantifiable data to objectively assess the relationship between chatbot adoption and various indicators of brand reputation, such as customer satisfaction, trust, and service quality. Structured questionnaires were administered to bank customers and brand managers. The study targeted 34 out of 38 commercial banks operating in Kenya, including 34 brand managers and 384 bank customers. A census approach was used for selecting brand managers across the 34 banks. Stratified random sampling was employed to select 384 bank customers, ensuring representation across different customer segments. The research conceptualizes AI-driven chatbots as tools that can influence key aspects of brand reputation in the banking sector. The study examines how chatbot adoption correlates with customer satisfaction, trust, confidence, and perceived service quality.

Personalized Marketing as an AI customer engagement strategy for enterprise growth

Liu and Zhang’s (2024) did a study titled Can Artificial Intelligence (AI)-Driven Personalization Influence Customer Experiences? The study investigates the impact of AI-driven personalization on customer experiences, with a specific focus on the ethical and privacy implications of such technologies in digital marketing. The research is grounded in the context of Sweden—an ideal setting due to its progressive digital infrastructure and robust data protection regulations. The study explores the growing integration of artificial intelligence in digital marketing, particularly how personalization powered by AI shapes consumer experiences. It highlights the dual-edge nature of such advancements: while they enhance user engagement and service relevance, they simultaneously raise ethical and privacy concerns. These tensions form the core of the study’s inquiry. The study is anchored in an interpretivist research paradigm, aiming to uncover the subjective perceptions of both consumers and industry professionals. This philosophical lens allows for an in-depth understanding of how individuals experience and interpret AI-driven personalization, particularly in relation to ethical dilemmas and data privacy. The researchers conceptualize AI-driven personalization as a multifaceted phenomenon—not just a technological function but a customer experience influencer with socio-ethical implications. The study seeks to uncover how AI can be used responsibly to improve personalization while respecting consumer rights. Given the emerging nature of the topic, the authors adopted an exploratory research design. This approach was well-suited to map uncharted areas, discover new insights, and identify emerging patterns and concerns in AI-based marketing. To ensure comprehensive data collection, a mixed-methods strategy was employed, incorporating both qualitative and quantitative techniques where Case Studies, semi structured interviews and surveys were used. Case study was used in In-depth analyses of major global companies like Amazon, Netflix, and Spotify that provided practical illustrations of how AI-driven personalization is implemented and its associated ethical challenges. Semi-Structured Interviews Conducted with digital marketing managers and AI professionals, these interviews offered insider perspectives on the operational realities, benefits, and difficulties in using AI ethically and Surveys where Online questionnaires targeted at consumers captured their perceptions, experiences, and concerns regarding AI personalization in digital marketing. The research focused on two primary participant groups the Industry Professionals being Digital marketing managers and AI specialists involved in implementing AI personalization strategies and Consumers who are Individuals who have experienced AI-driven personalization in digital interactions. Sampling was tailored to the nature of each group. Purposive sampling was used to select knowledgeable professionals, ensuring depth of insight, while convenience sampling was applied to recruit a diverse group of consumers efficiently. Liu and Zhang’s study contributes meaningfully to the discourse on AI in marketing by examining both its technical promise and ethical risks. By situating the research in a digitally advanced and privacy-conscious society like Sweden,

The study titled “Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment” by Canhoto, Keegan, and Ryzhikh (2023) investigates how consumers experience and respond to AI-enabled personalisation (AI-EP) in physical retail settings, focusing on the inherent tension between personalisation benefits and privacy concerns. Set in the United Kingdom, the research centers on the fashion retail sector, a domain increasingly adopting AI technologies to enhance customer engagement. The study specifically examines the use of the Regent Street App in London, which delivers personalised offers to shoppers’ smartphones using geofencing beacons and cloud-based AI. The study adopts an interpretivist paradigm, aiming to understand the subjective experiences and perceptions of consumers interacting with AI-EP. This approach facilitates a nuanced exploration of the personalisation-privacy paradox from the consumers’ perspective. A qualitative, exploratory case study methodology was employed to delve into the relatively unexplored phenomenon of AI-EP in physical retail environments. An In-depth Semi-Structured Interviews Conducted with 18 female shoppers aged 18–30 who had interacted with the Regent Street App. Interviews were held immediately after exposure to personalised offers, either outside the store or at a nearby café, to capture real-time reactions. Purposive sampling was used to select participants who fit the specific demographic criteria and had experience with the Regent Street App.Thematic analysis was conducted using NVivo software. An initial coding framework based on existing literature guided the analysis, which was then refined inductively to capture emerging themes from the data. ​The research conceptualizes AI-EP as a dual-faceted phenomenon, offering both gratifications and raising privacy concerns. The study uses the personalisation-privacy paradox framework to understand how consumers navigate the trade-offs between receiving personalised offers and maintaining control over their personal information.

The study titled “Consumer Engagement via Interactive Artificial Intelligence and Mixed Reality” by Sung, Bae, Han, and Kwon (2021) investigates how interactive artificial intelligence (AI) and mixed reality (MR) technologies influence consumer engagement in a retail/entertainment complex in South Korea. The research focuses on understanding the impact of AI quality and MR experiences on consumer behavior, particularly purchase intentions and brand endorsement. The study is set within a $17 million AI-embedded MR exhibit located in a retail/entertainment complex in South Korea. This environment combines advanced technological entertainment with retail shopping, providing a unique setting to explore consumer interactions with AI and MR technologies. The research adopts the Stimulus-Organism-Response (S-O-R) framework, a theoretical model that explains how external stimuli (e.g., AI and MR technologies) affect internal states (e.g., immersion, enjoyment) and subsequent behavioral responses (e.g., engagement, purchase intentions). This approach allows for a structured analysis of the psychological processes underlying consumer engagement in technologically enhanced retail environments. A quantitative research design was employed to test the proposed hypotheses derived from the S-O-R framework. Participants completed structured questionnaires on tablet PCs immediately after experiencing the AI-embedded MR exhibit. The surveys measured variables such as AI quality, MR immersion, enjoyment, perceptions of novel experiences, consumer engagement, purchase intentions, and intentions to share experiences. Target Population included Consumers who visited the MR retail/entertainment complex, specifically those who attended the MR media art show featuring interactive storytelling with 3D MR environments, interacted with AI technologies within the exhibit and those who Visited the associated retail merchandise shop. Participants were recruited on-site through voluntary participation and were compensated with a gift. The sampling was purposive, targeting individuals who had the complete experience of the MR exhibit and retail interaction. Partial Least Squares Structural Equation Modeling (PLS-SEM) was utilized to analyze the data and test the hypothesized relationships within the S-O-R framework. This method is suitable for complex models and allows for the assessment of both direct and indirect effects among variables. The study conceptualizes consumer engagement as a multifaceted construct influenced by the quality of AI and the immersive experiences provided by MR technologies. High-quality AI, characterized by effective speech recognition and synthesis, enhances MR immersion, enjoyment, and perceptions of novelty, which in turn foster greater consumer engagement. This engagement is hypothesized to lead to increased purchase intentions and positive word-of-mouth behaviors.

​The study titled “Customer Experiences in the Age of Artificial Intelligence” by Ameen, Tarhini, Reppel, and Anand (2021) investigates how AI integration in retail services influences customer experiences, focusing on factors like trust, perceived sacrifice, and relationship commitment. Set within the beauty retail sector, the research examines AI-enabled services offered by a beauty brand, aiming to understand how such technologies affect customer interactions and satisfaction. The study is grounded in the trust-commitment theory and the service quality model, exploring how trust and commitment influence customer experiences in AI-enabled services.  The study employed Quantitative research design utilizing a survey-based approach. The researchers carried out an online survey targeting customers who have used AI-enabled services from a beauty brand. Target Population included Customers with experience in AI-enabled beauty services. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze 434 responses, assessing relationships among variables like trust, perceived sacrifice, personalization, and service quality. The study conceptualizes AI-enabled customer experience as influenced by Perceived Convenience that is the ease and efficiency provided by AI services, Personalization, tailored experiences facilitated by AI and AI-Enabled Service Quality looking at the Overall quality of AI-driven services. These factors are mediated by trust and perceived sacrifice, with relationship commitment playing a significant role in shaping the customer experience.

Summary Table on Gaps (Optional for Visual Learners):

(Researcher, 2025)

Critical Review of gaps on Automated Customer Support and Personalized Marketing as an AI Customer Engagement Strategies for Business Growth

Objective 1: Automated customer support as an AI customer engagement strategy on business growth

The existing literature on AI adoption is limited by several methodological, contextual, conceptual, and analytical gaps. Methodologically, studies predominantly rely on cross-sectional and survey-based approaches, which restrict causal inference and temporal understanding. McKinsey (2021) and AfDB (2021) use structured surveys, which are efficient but lack interpretive depth, while the overuse of online surveys introduces sampling bias, excluding less-digitized organizations. Nkosi and Moyo (2023) offer a mixed-methods approach but rely on purposive sampling, which may skew results, and their unclear operationalization of AI metrics undermines construct validity. Otieno and Wambua (2022) lack qualitative triangulation, limiting their study’s depth. Contextually, the studies lack geographic, sectoral, and organizational diversity. McKinsey (2021) overlooks Africa, AfDB (2021) focuses on limited sectors, and both Nkosi and Moyo (2023) and Otieno and Wambua (2022) restrict their scope to specific countries or industries, missing broader socio-economic and infrastructural realities. Conceptually, AI adoption definitions are often simplistic; McKinsey (2021) defines it as implementation in a single business function, AfDB (2021) treats automation only as an efficiency tool, and Nkosi and Moyo (2023) fail to explore AI subcomponents, while Otieno and Wambua (2022) narrowly define brand reputation. These conceptual oversights highlight the need for multidimensional, theory-driven models of AI engagement. Analytically, most studies lack longitudinal perspectives and robust statistical modeling. McKinsey (2021) and AfDB (2021) offer descriptive insights with limited inferential power, while Nkosi and Moyo (2023) do not control for confounding variables, and Otieno and Wambua (2022) apply regression without diagnostics or time-based evaluations. These analytical gaps emphasize the need for more sophisticated tools, including sectoral comparisons and pre-post adoption assessments.

Objective 2: Personalized Marketing as an AI customer engagement strategy for enterprise growth

Liu and Zhang (2024) examined AI-driven personalization in digital marketing in Sweden, revealing key gaps in methodology, context, concept, and analysis. Their mixed-methods approach, while rich in data, suffers from non-probability sampling, limiting generalizability, and lacks a longitudinal perspective to assess evolving perceptions of AI personalization. The study’s geographic focus on Sweden raises questions about its applicability to countries with weaker digital infrastructure, and it neglects the experiences of minority populations. Conceptually, the study focuses on ethics and privacy but overlooks emotional impacts like digital fatigue and consumer agency. Analytically, it fails to quantify privacy trade-offs and misses cross-analysis between professional and consumer perspectives. Canhoto, Keegan, and Ryzhikh (2023) explored AI personalization in UK retail through the Regent Street App but encountered similar limitations. Their qualitative approach, relying on a small, homogenous sample, lacks behavioral data triangulation and fails to account for diverse retail environments or stakeholders. The study’s focus on privacy neglects factors like brand loyalty, social influence, and AI literacy, and its thematic analysis lacks sophistication in quantifying trade-offs. Sung, Bae, Han, and Kwon (2021) studied AI and Mixed Reality in South Korean retail entertainment, but their study is methodologically limited by its cross-sectional design and reliance on quantitative data, omitting qualitative insights. The research’s high-tech context and focus on fully engaged visitors restrict its applicability, while the concept of AI quality is narrowly defined. Analytically, the study misses opportunities to explore interaction effects, such as the role of consumer tech-savviness. Ameen, Tarhini, Reppel, and Anand (2021) studied AI in beauty retail services, but their findings are constrained by a narrow industry focus and reliance on self-reported survey data. The study lacks geographic clarity and fails to address cultural diversity or key constructs like AI acceptance and perceived fairness. While using PLS-SEM, it misses mediation and moderation effects and overlooks segmentation analysis, assuming homogeneity in customer experiences. Together, these studies reveal significant gaps across various dimensions, highlighting the need for more comprehensive and diverse approaches to AI personalization research.

CONCEPTUAL FRAMEWORK

Fig.1: Conceptual Framework

METHODOLOGY

This study employed a desktop research methodology, focusing on a comprehensive review of relevant literature to examine AI-driven customer engagement strategies and their impact on enterprise growth. The research involved an in-depth analysis of peer-reviewed journal articles, industry reports, and case studies that explore the mentioned AI strategies. By synthesizing insights from multiple studies across different industries and geographical regions, this approach allowed for a comparative understanding of how AI enhances customer engagement and drives business growth. The findings provide a broad perspective on emerging trends, key success factors, and potential challenges in AI-enabled customer engagement, offering valuable implications for businesses seeking to leverage AI for competitive advantage.

FINDINGS

Automated customer support as an AI customer engagement strategy on business growth

The findings on the The State of AI in 2021 were that of these1,843 participants, 56% of respondents reported AI adoption in at least one function, up from 50% in 2020.  AI is commonly used in service operations, with top use cases including service-operations optimization and contact-center automation. 27% of respondents attributed at least 5% of their organizations’ earnings before interest and taxes (EBIT) to AI, indicating a growing financial impact.

Findings on the impact of automated customer support on business operations across Africa, focusing on the banking, telecommunications, and retail sectors are that  Automation has allowed businesses to provide uninterrupted 24/7 Service delivery, meeting customer expectations for constant availability, Customers report higher satisfaction levels due to quicker response times and consistent service quality and businesses have experienced decreased operational costs by minimizing the need for extensive human resources in customer service roles

Findings on integration and impact of Artificial Intelligence (AI) within South Africa’s e-commerce sector are that there is enhanced Customer Experience where AI integration led to more personalized and engaging shopping experiences, increasing customer satisfaction. There is Improved Operational Efficiency with  AI applications in logistics and inventory management streamlined operations, reducing costs and delivery times and Companies adopting AI technologies gained a competitive edge, attracting and retaining more customers.

Findings on the role of AI-driven chatbots in enhancing customer service within Kenyan commercial banks are that Chatbot adoption accounted for 35.2% of the variation in customer satisfaction, indicating a significant positive impact. The use of chatbots explained 28.1% of the variation in customer trust and confidence levels. Chatbot implementation contributed to 18.6% of the variation in perceived service quality among customers. These findings suggest that AI-driven chatbots play a substantial role in shaping customer perceptions and enhancing brand reputation in Kenyan commercial banks

Personalized Marketing as an AI customer engagement strategy for enterprise growth

The study titled Can Artificial Intelligence (AI)-Driven Personalization Influence Customer Experiences? found that AI-driven personalization significantly enhances customer experience by providing more relevant and timely content. However, it also revealed growing consumer concern over data usage, privacy, and algorithmic transparency. Industry professionals acknowledged these challenges and stressed the need for ethical frameworks to guide AI application in marketing. A key insight was the tension between personalization and privacy—a balance that organizations must manage carefully. The research calls for greater transparency, consumer education, and the implementation of ethical AI guidelines to foster trust.

Findings on the study “Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment “were that there was Gratifications where Participants appreciated relevant, timely, and location-specific offers, particularly those providing discounts on desired items. Despite the benefits, participants expressed discomfort with the extent of data collection, especially regarding location tracking and the perceived intrusiveness of unsolicited offers. Consumers sought greater control over the personalisation process, including the ability to manage preferences and data sharing. participants were wary of the privacy implications, highlighting the complexity of consumer attitudes towards AI-EP.

Findings on “Consumer Engagement via Interactive Artificial Intelligence and Mixed Reality” were high-quality AI interactions significantly enhanced MR immersion, enjoyment, and perceptions of novel experiences among consumers. The enriched experiences facilitated by AI and MR technologies led to higher levels of consumer engagement, characterized by emotional and cognitive investment in the experience. Increased consumer engagement positively influenced behavioral intentions, including a greater likelihood of purchasing merchandise and sharing the experience with others, effectively serving as unpaid brand endorsement

The findings on “Customer Experiences in the Age of Artificial Intelligence “are Trust and Perceived Sacrifice both serve as mediators between perceived convenience, personalization, service quality, and customer experience. Relationship Commitment has a significant direct effect on AI-enabled customer experience and Perceived Convenience and Personalization Positively influence trust and reduce perceived sacrifice, enhancing the overall customer experience.

CONCLUSIONS

Automated customer support as an AI customer engagement strategy on business growth in Kenya

AI-powered automated customer support significantly boosts operational efficiency and enhances customer service in Kenyan enterprises. Studies by McKinsey & Company (2021), the African Development Bank (2021), and Otieno and Wambua (2022) highlight improvements such as faster response times, fewer errors, and scalable interactions—especially in the banking and telecom sectors. One key advantage is 24/7 service accessibility, which improves customer satisfaction and loyalty. AI also strengthens brand reputation, as customers associate seamless digital experiences with professionalism and innovation. These enhancements contribute to enterprise growth by improving service delivery and reducing operational costs. Additionally, automation enables businesses to scale efficiently, manage larger customer bases, and enter new markets. However, effectiveness depends on aligning AI strategies with sector-specific needs. For example, the success of intelligent support tools in South Africa’s e-commerce sector (Nkosi & Moyo, 2023) shows the value of tailoring automation to industry contexts. Overall, well-deployed AI customer support drives enterprise growth by enhancing efficiency, trust, and scalability.

Personalized marketing as an AI customer engagement strategy on enterprise growth in Kenya

AI-driven personalized marketing plays a critical role in enhancing customer engagement and driving enterprise growth. Studies by Liu & Zhang (2024), Ameen et al. (2021), and others reveal that personalized emails, offers, and product suggestions improve customer satisfaction, loyalty, and perceived value. In Kenya’s competitive digital economy, such strategies help businesses stand out and foster deeper emotional connections with customers, encouraging repeat purchases. However, privacy and data ethics are major considerations—transparency and compliance with Kenya’s Data Protection Act are essential to maintain consumer trust. Personalized marketing also offers a competitive edge by enabling hyper-targeted campaigns and customer journeys, especially in fintech, retail, and mobile commerce sectors. The inclusion of interactive elements—such as recommendation engines and conversational AI—further enhances campaign effectiveness and conversion rates. Kenyan enterprises that responsibly adopt AI personalization can strengthen brand loyalty, increase engagement, and achieve sustainable growth in a rapidly evolving marketplace.

RECOMMENDATIONS

Automated Customer Support as an AI Customer Engagement Strategy on Business Growth

The study on “The State of AI in 2021” recommends that Organizations should strategically integrate AI into customer service functions to enhance efficiency and customer satisfaction. Implementing practices to mitigate AI-related risks is essential, as many companies’ AI efforts still fall short in this area and there should be regular assessment of AI’s impact on business functions can help in refining strategies and maximizing benefits.

The African Development Bank (AfDB) 2021 report that examined the impact of automated customer support on business operations across Africa, focusing on the banking, telecommunications, and retail sectors recommends  that  Investment in automation, businesses should continue to invest in automated customer support technologies to maintain competitive advantage,  employees should be trained to work alongside automated systems, ensuring seamless integration and service delivery and need for  regular assessment of automated systems to identify areas for improvement and adapt to changing customer needs.

The study on the integration and impact of Artificial Intelligence (AI) within South Africa’s e-commerce sector puts across a number of recommendations that includes Strategic AI Implementation. Businesses should develop clear strategies for AI integration, focusing on areas that directly impact customer satisfaction and operational efficiency. There should be Continuous Evaluation, this involves regular assessment of AI systems to ensure they meet evolving customer needs and market dynamics and Investment in Skills Development. Companies should invest in training programs to equip employees with the necessary skills to work alongside AI technologies effectively.

The study on the role of AI-driven chatbots in enhancing customer service within Kenyan commercial banks presents a number of recommendations that include Banks to focus on integrating chatbots across various customer touchpoints to provide a seamless and efficient service experience. Enhance Data Privacy Controls through Implementing robust data privacy measures and giving customers control over their data to build trust and confidence in chatbot interactions and have regular assessment of chatbot performance and customer feedback to refine chatbot functionalities and address emerging customer needs.

Personalized Marketing as an AI Customer Engagement Strategy for Enterprise Growth

The study on “Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment” recommends enhancement of transparency where retailers should clearly communicate how consumer data is collected and used, fostering trust and informed consent. Empower Consumers by Providing users with tools to control their personalisation settings can mitigate privacy concerns and enhance user satisfaction. Consider Contextual Relevance, ensuring that personalised offers are contextually appropriate and non-intrusive can improve consumer receptivity and the need to balance Personalisation and Privacy. Retailers must navigate the delicate balance between offering personalised experiences and respecting consumer privacy, akin to the dynamics of the “Snakes and Ladders” game referenced in the study.

The study on “Consumer engagement via interactive artificial intelligence and mixed reality “recommends retailers to invest in high-quality AI technologies that offer seamless and natural interactions to deepen consumer immersion and enjoyment. Incorporating MR elements that provide novel and engaging experiences can significantly boost consumer engagement and subsequent purchase behaviors.​Leverage S-O-R Framework through applying the S-O-R model can help retailers design environments that effectively stimulate desired consumer responses through carefully curated technological stimuli and lastly, encourage Word-of-Mouth.by creating memorable and shareable experiences, retailers can harness the power of consumer advocacy to promote their brand organically.​

The study on “Customer Experiences in the Age of Artificial Intelligence” recommends retailers to focus on building trust through transparent AI practices.​Reduce Perceived Sacrifice through Minimizing customer concerns related to AI usage, such as privacy issues and foster Relationship Commitment through developing strategies to strengthen long-term customer relationships through consistent and personalized AI interactions.

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