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@article{209085,
        author = {Ashish Kalaskar},
        title = {From Pilots to Production: Enterprise Genai Integration Across Media and Financial Services},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {780-794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209085},
        abstract = {Despite corporate investments exceeding $30–40 billion in Generative Artificial Intelligence (GenAI), enterprise deployments exhibit a profound failure rate. As documented in the landmark MIT NANDA State of AI in Business 2025 report, 95% of enterprise AI pilots fail to yield measurable P&L return. The root cause is rarely model capability; rather, it stems from a systemic "learning and integration gap"—a failure to architect AI agents directly into core Enterprise Systems-of-Record (SoR) with automated guardrails, context-grounded telemetry, and closed feedback memory loops
This research paper presents a blueprinting framework designed to cross the "GenAI Divide" by analyzing two starkly contrasting enterprise paradigms: Media, Marketing & Advertising (characterized by high velocity, rapid iteration, and real-time bid optimization) and Retail & Commercial Banking (characterized by rigid regulatory governance, zero-trust data privacy, and heavy financial liability). By conducting a workflow-by-workflow architectural audit across eight high-impact sub-functions, this paper establishes the structural protocols, governance mechanisms, and human-in-the-loop (HITL) escalations required to transition brittle "Shadow AI" prompts into scalable, enterprise-grade AI systems.},
        keywords = {.},
        month = {September},
        }