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@article{209840,
        author = {Dr.P.Vignesh},
        title = {Enterprise AI Transformation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {3516-3519},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209840},
        abstract = {This review paper synthesizes the enterprise AI transformation perspective presented in the source framework and organizes it as an academic-style conceptual review. The framework connects AI readiness assessment, strategic prioritization, architecture and delivery, governance and risk, leadership and workforce adoption, operational practices, and measurable business outcomes. Rather than treating AI transformation as a model-selection or technology-adoption exercise, the reviewed framework positions it as an organizational capability that must be assessed, governed, executed, measured, and continuously improved. The review highlights a lifecycle in which readiness establishes the baseline; strategy converts ambition into measurable outcomes and a prioritized portfolio; architecture and engineering provide secure and maintainable foundations; governance applies risk-proportionate controls; leadership and adoption enable organizational change; and performance management connects production evidence to business value. The paper also identifies the need to distinguish source-derived framework elements from empirical validation and proposes future research directions focused on comparative framework evaluation, maturity measurement, benefits attribution, and validation across industries.},
        keywords = {Enterprise AI; AI transformation; AI readiness; AI strategy; AI governance; Responsible AI; AI CoE; LLMOps; AgentOps; business value; AI maturity},
        month = {October},
        }