The convergence of Software-as-a-Service (SaaS), artificial intelligence, and cloud computing has transformed the experiential foundations of modern hospitality and digital commerce. Contemporary service ecosystems no longer rely primarily on human intermediaries but increasingly on algorithmic interfaces, recommender systems, mobile platforms, and cloud-native data architectures that continuously sense, predict, and shape user behavior. This article investigates how SaaS-driven experience architectures operationalize personalization, engagement, and value co-creation across hospitality and e-commerce environments, with particular emphasis on cloud-based, AI-enabled service orchestration. Drawing on an extensive interdisciplinary literature base, including hospitality transformation studies, recommender system research, mobile personalization, AI-driven customer engagement, and ethical frameworks for algorithmic governance, the paper develops an integrated theoretical and methodological account of SaaS-enabled experience personalization.

The hospitality sector, historically organized around concierge-style human mediation, has increasingly been reconfigured through cloud platforms that embed artificial intelligence into every stage of the customer journey, from discovery and booking to in-stay interaction and post-service engagement. Goel’s (2025) account of SaaS-driven hospitality transformation serves as a central anchor in this analysis, illustrating how cloud-based platforms replace fragmented service layers with continuous, data-driven experience flows. These flows are not merely technological but socio-economic assemblages in which customer emotions, behavioral data, recommender algorithms, and organizational strategies are mutually constitutive. Similar dynamics can be observed in SaaS-based e-commerce, digital marketing, mobile advertising, and platform-mediated content ecosystems, where personalization is increasingly understood as a strategic infrastructure rather than a discrete feature (Smith, 2019; Balasubramanian, 2024; Oyewole et al., 2024).

Methodologically, this study employs a qualitative meta-analytic synthesis of existing SaaS, AI, and personalization scholarship, integrating conceptual modeling with interpretive analysis. Rather than aggregating numeric datasets, the research examines how diverse scholarly traditions conceptualize engagement, loyalty, trust, fairness, and value within algorithmically mediated environments. Findings indicate that SaaS-driven personalization operates through three interlocking layers: datafication of user behavior, algorithmic inference through machine learning models, and cloud-native service orchestration that delivers real-time adaptive experiences. These layers collectively produce what may be termed an “experience fabric,” a continuously updated representation of each customer that guides content delivery, pricing, communication, and service design (Arora & Khare, 2024; Curiskis et al., 2023; Ma et al., 2019).

The results further demonstrate that while AI-powered personalization significantly enhances engagement and conversion rates, it simultaneously introduces ethical, governance, and trust challenges. Issues of algorithmic opacity, data privacy, and potential discrimination complicate the deployment of cloud-based recommender systems and predictive analytics (Dwork & Roth, 2014; Pasquale, 2015; Binns & Veale, 2017). In hospitality contexts, where emotional labor and trust are central to service quality, the replacement of human concierges with digital agents intensifies these concerns, even as it expands scalability and consistency (Goel, 2025; Mkhize et al., 2024).

The discussion advances a theoretical framework that situates SaaS-driven experience architectures at the intersection of service-dominant logic, platform economics, and algorithmic governance. By comparing hospitality with e-commerce, mobile advertising, and SaaS business intelligence, the paper demonstrates that personalization is evolving into a form of infrastructural power that shapes not only what users see but how they feel, decide, and remain loyal to digital platforms. The study concludes that future research and practice must move beyond purely technical optimization toward ethically grounded, transparent, and participatory models of AI-enabled service design.

SaaS ecosystems, AI-driven personalization, cloud computing

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