Designing AI-Augmented Decision Systems Using Python and Power BI

Sandeep Parshuram Patil

Abstract: AI-augmented decision systems integrate predictive models with interactive analytics to raise the speed, accuracy, and accountability of enterprise choices. This article presents a repeatable blueprint that couples Python for data engineering, modeling, and governance with Power BI for visualization, scenario analysis, and policy execution. I specify requirements for data contracts, feature management, model lifecycle training, registry, deployment, and human-in-the-loop controls. The reference architecture spans batch and real-time scoring via ONNX Runtime and FastAPI, calibrated uncertainty estimates, and DAX-based decision policies with row-level security. Two production-like case studies demand forecasting with constrained allocation and early-warning risk triage demonstrate implementation patterns, including MLflow tracking, incremental refresh, drift monitors, and SHAP explanations. Across diverse workloads, the approach delivers 10-25% forecast error reduction, 25-45% precision improvement at fixed recall, and 30-60% shorter decision cycles, while preserving auditability through lineage and model cards. I discuss trade-offs between complexity and interpretability, governance and agility, and batch versus streaming, and outline cost/performance tuning for gateways and caches. The article contributes artifacts code, Open API schemas, DAX templates, and checklists that enable reproducibility and rapid adoption. Limitations and failure modes data drift, weak labels, UX debt are analyzed, alongside safeguards for fairness, privacy, and accountable overrides.

Keywords: Business Intelligence, MLOps, Explainability, ONNX, Python, Power BI

How to Cite?: Sandeep Parshuram Patil, "Designing AI-Augmented Decision Systems Using Python and Power BI", Volume 13 Issue 10, October 2024, International Journal of Science and Research (IJSR), Pages: 2070-2075, https://www.ijsr.net/getabstract.php?paperid=SR241022094012, DOI: https://dx.doi.org/10.21275/SR241022094012