Artificial Intelligence (AI) and Machine Learning (ML) have become foundational technologies transforming marketing and service ecosystems through predictive intelligence, automation, personalization, and generative capabilities. Recent significant advancements in deep learning, large language models (LLMs), real-time analytics, and AI governance frameworks have reshaped how organizations create value. This paper presents a systematic Review and Taxonomy of Applications and Artificial Intelligence and Machine Learning in Marketing and Service Ecosystems. The study develops a taxonomy of AI applications across five domains: (1) customer intelligence, (2) decision support systems, (3) operational automation, (4) innovation capability, and (5) financial and strategic performance impact. The review identifies growing adoption of generative AI in marketing content creation, AI-driven service robots in frontline services, predictive analytics in financial services, and AI-enabled customer journey orchestration. Despite measurable performance gains, persistent challenges remain in algorithmic transparency, ethical governance, data quality, workforce transformation, and regulatory compliance. A conceptual framework linking AI capabilities, organizational readiness, adoption intensity, and performance outcomes is proposed. Future research directions include responsible AI governance, SME-focused adoption models, and human–AI collaboration in service ecosystems.
Kondraguntla et al. (Thu,) studied this question.