Generative Intelligence and AIOps for Autonomous Workflows in Data-Centric Hospitality and Agricultural Technology Platforms

  • Authors

    • Dr. Isabella Romano Biomedical Engineering, Mediterranean University of Rome, Italy. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-V9I1P106

    Published 01-09-2026

  • Generative AI Workflows, LLM-Based Automation, Compositional Network Architectures, Multi-Enterprise Platforms, Autonomous Task Planning, Data-Centric AI Systems, AIOps-Driven Operations, DataOps Foundations, Active Learning Engines, Intelligent Workflow Orchestration

    Issue

    Vol. 9 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    I. Romano, “Generative Intelligence and AIOps for Autonomous Workflows in Data-Centric Hospitality and Agricultural Technology Platforms”, IJAIDT, vol. 9, no. 1, pp. 41–57, Jan. 2026, doi: 10.67228/30713315/IJAIDT-V9I1P106.

  • Abstract

    Recent developments in Generative Artificial Intelligence (GAI) have enabled training of large language models (LLM) on domain and task-centric corpora. The functionality available through GAI Foundations facilitates autonomous, agile, and compositional solutioning of data-intensive tasks and offers new avenues for creativity in section content generation. However, application of Generative AI in operation of complex, data-intensive systems such as hospitality services and AgriTech platforms appears limited. A joint-use compositional-network architecture is proposed, enabling automatic generation and execution of GAI-prompted task plans, across multi-enterprise hospitality and AgriTech platforms. Two-way GAI-enabled workflow automation and autonomic technology adoption at accumulate-and-release PaaS joint clouds is explored for further business value creation, harnessing concepts from AIOps for two-sided data, process, and business capability flows. A major challenge lies in smart orchestration of parallel workflow automation technology, supported by emerging DataOps Foundations, Data-Centric AI, AIOps, and Platform Engineering constructs. Focused Data Stewardship across data consumers and providers, synchronizing Services, Data, and Firewall layers in a mirror-image architecture, is necessary to ensure data availability and quality. The advent of modern distributed processing engines makes operationalization of Active Learner-based Decision Engine architectures feasible. GAI applications are sensitive to prompt quality but can operate even when domain-centered corpora are sparse, as the learning objective is indirect. Recent GAI releases enable the use of simpler teaching prompts. Data summarization techniques help generate accurate question-answer pairs for Actively-managed Learning Engines. Autonomously optimized run-book automation is a vision rather than a present reality, as suitable near-real-time, training-support data remain scarce. Such virtual tutors leverage prompt-generic Teaching-by-Example approaches, enabling rapid testing by providing near real-time output summarization capability. GAI sites act as active learners for Data-Centric AI, augment-and-replace contemporaneous query capabilities for event-based processing, and offer simple authoring of Autonomic-run-book content.

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