International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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Artificial Intelligence, Machine Learning, and Business Intelligence: Embedding Intelligent Analytics into Enterprise Data Ecosystems

Author(s) Naresh Reddy Telukutla
Country United States
Abstract The convergence of Artificial Intelligence (AI), Machine Learning (ML), and Business Intelligence (BI) defines one of the most consequential shifts in enterprise data strategy of the current decade. Professionals who combine deep cloud engineering expertise with applied AI/ML capabilities and enterprise BI design are increasingly driving the intelligence layer that sits atop modern data ecosystems. This profile is exemplified by the dual certification credentials of Azure AI Engineer Associate and Azure Data Scientist Associate, combined with hands-on proficiency across Azure Machine Learning, Azure Synapse Analytics, Snowflake, and Power BI — positioning such practitioners as experts who translate technology into measurable outcomes. An engagement at a leading consulting organization, where the practitioner served as subject matter expert for Wealth Management and Regulatory Reporting, generated high value new business opportunities, attesting that technical excellence, when paired with domain knowledge, delivers measurable organizational value. Python-based data science capabilities using NumPy, Pandas, Matplotlib, and Seaborn further bolster a complete, end-to-end analytics capability spanning data ingestion, feature engineering, model prototyping, and executive-level reporting. This article synthesizes the technical architecture, platform strategy, and business implications of building intelligent, cloud-native analytics capabilities in financial and enterprise environments — drawing directly from this professional profile to illuminate practical pathways for embedding AI and ML into enterprise data ecosystems.
Keywords Machine learning platforms | Cloud analytics | Business intelligence | Data science | Azure AI | MLOps | Financial services analytics | Cloud-native architecture
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-11
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.74467
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E-ISSN 2582-2160
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