The demand for automated customer support approaches in customer-centric environments has increased significantly in the past few years. Natural Language Processing (NLP) advancement has enabled conversational AI to comprehend human language and respond to enquiries from customers automatically independent of the intervention of humans. Customers can now access prompt responses from NLP chatbots without interacting with human agents. This application has been implemented in numerous business sectors, including banking, manufacturing, education, law, and healthcare, among others. This study reviewed earlier studies on automating customer queries using NLP approaches. Using a systematic review methodology, 73 articles were analysed from reputable digital resources. The evaluated result offers an in-depth review of prior studies investigating the use of NLP techniques for automated customer service responses, including details on existing studies, benefits, and potential future study topics on the use of NLP techniques for business applications. The implications of the results were discussed and, recommendations made.

This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro-enterprises (SMMEs), highlighting how interconnected autonomous agents improve operational efficiency and adaptability. Using the PRISMA 2020 framework, this study systematically identified, screened, and analyzed 66 studies, including peer-reviewed and credible gray literature, published between 2019 and 2024, to assess agentic AI frameworks in SMMEs. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to advanced technologies, the review synthesizes existing research to highlight the characteristics, implementations, and impacts of agentic AI in task automation, decision-making, and ecosystem-wide collaboration. The results demonstrate the potential of agentic AI to address technological, ethical, and infrastructure barriers while promoting innovation, scalability, and competitiveness. This review contributes to the understanding of agentic AI frameworks by offering practical insights and setting the groundwork for further research into their applications in SMMEs’ dynamic and resource-constrained economic environments.

This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro enterprises (SMMEs), emphasising the transformative potential of interconnected autonomous agents in enhancing operational efficiency and adaptability. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyzes) framework, this study rigorously identified, screened, and analyzed 63 peer-reviewed studies published between 2019 and 2024. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to advanced technologies, the review synthesizes existing research to highlight the characteristics, implementations, and impacts of agentic AI in task automation, decision-making, and ecosystem-wide collaboration. The findings underscore the role of agentic AI in fostering innovation, scalability, and competitiveness while addressing barriers such as technological, ethical, and infrastructural challenges. By advancing the understanding of agentic AI frameworks, this research provides actionable insights and sets a foundation for future explorations into their implications within resource-constrained and dynamic economic landscapes.

Agentic artificial intelligence (AI), characterized by autonomous, goal-oriented systems capable of inter-agent collaboration, is rapidly gaining traction as a transformative paradigm in the context of small, medium, and micro enterprises (SMMEs). The conceptual foundations and applied models of agentic AI have been thematically reviewed, but the structural and intellectual evolution of this domain needs further exploration. This bibliometric review builds on a previously conducted systematic literature review (SLR) of 66 studies and expands the analysis with an updated dataset of 214 papers published between 2019 and 2025, sourced from Scopus, Google Scholar, and credible gray literature repositories. The study uses VOSviewer and the Bibliometrix R package to create scholarly trend maps, identify prominent contributors, thematic clusters, and existing gaps in the literature. Findings reveal a sharp post-2022 surge, with 162 publications from 2023 to mid-2025, driven by developments in large language models (LLMs) and multi-agent systems (MAS). Key themes include foundational AI techniques such as LLMs and reinforcement learning (RL), strategic applications of agentic AI in SMMEs, decision-support systems, simulation-based modeling, and industrial implementations including Industry 4.0 and cyber-physical systems. Geographically, the majority of agentic AI research originates in the United States, China, and the United Kingdom, indicating a concentrated intellectual influence in affluent nations. In contrast, contributions from regions such as Africa and Southeast Asia remain limited. Substantial gaps persist, including limited empirical implementations in micro enterprises, underdeveloped AI governance frameworks, a lack of region and sector-specific adaptations, insufficient analysis of adoption and, fragmented interdisciplinary collaboration, guiding future research and policy in resource-constrained SMME contexts.

Responding to enormous comments on social media platforms is one major challenge facing businesses in recent times, especially when dealing with irate consumers. Customers have increasingly adopted social networks as a platform for expressing their concerns and posting comments on business pages, posing a great challenge for customer support agents and digital marketers alike. Analyzing and responding manually to these enormous comments is a time-consuming task, necessitating the adoption of Artificial Intelligence (AI) tool that can complete the task swiftly— automatic comprehension of social media posts for comment generation. In this paper, we present algorithms and a tool for the automatic comprehension of customer tweets and generation of responses to these tweets. This was done in two-fold: using existing Natural Language Processing (NLP) libraries to preprocess and tokenize these tweets, and secondly, using rule-based algorithms to find a matching response to each customer, based on the array of extracted tokens from the customer’s tweet. This was built into a tool called Comment-Synthesizer. This tool takes unfiltered tweets as input, preprocesses the tweets, and matches the tweet with predefined responses using a rule-based algorithm with a success rate of 76%. This tool, if implemented in a desktop automation application, can be used to respond automatically to a large volume of customers’ social media comments/posts.

Agentic artificial intelligence (AI) systems are increasingly deployed in retail and supply chain operations, yet the empirical evidence for their efficacy has never been systematically quantified. This meta-analysis synthesizes 19 studies (total $$N = 5{,}292$$ ) to quantify the impact of agentic AI on operational performance and examine how contextual factors shape outcomes. Using a random-effects model with restricted maximum likelihood (REML) estimation and the Hartung-Knapp (HK) adjustment for confidence intervals, we found near-total heterogeneity across the included studies ( $$I^2 = 99.5\%$$ , $$\tau ^2 = 7.18$$ ), with a 95% prediction interval $$[-4.20,\ 7.36]$$ indicating that the true effect in any new setting could range from substantial harm to exceptional benefit. Against this backdrop of extreme variability, the pooled Hedges’ $$g = 1.58$$ (95% CI $$[0.26,\ 2.89]$$ , $$p = 0.022$$ ) should not be read as a general implementation expectation but rather as an average masking fundamentally divergent context-specific effects. Subgroup analysis by operational domain (retail, supply chain, e-commerce, inventory) identified domain-level differences under the common-effect model ( $$Q = 935.45$$ , $$df=3$$ , $$p < 0.001$$ ), though these differences were not statistically significant under the more appropriate random-effects model ( $$Q = 6.73$$ , $$df=3$$ , $$p = 0.081$$ ); domain estimate based on three or fewer studies (e-commerce, $$k = 3$$ ) are descriptive only and do not support inferential conclusions. The limit meta-analysis adjusted the estimate downward to $$g = 1.26$$ ( $$p = 0.066$$ ). These findings show that the current evidence, while statistically promising on average, is highly heterogeneous and subject to meaningful selective reporting. Practitioners should prioritize domain-specific pilots, distinguish simulation-based evidence from real-world evidence, attend to AI architectural differences, and interpret published effect sizes with considerable caution. Future research must prioritize real-world field experiments, architecture-specific evaluations, and publication of null results, along with the adoption of validated quality frameworks, to strengthen the reliability and credibility of the evidence base.