From Business Intelligence to Intelligent Automation: Integrating Adaptive Data Engineering and Large Multimodal Models for Competitive Advantage
Keywords:
Business Intelligence, Adaptive Data Engineering, Large Language Models, Intelligent AutomationAbstract
Background: The landscape of Business Intelligence (BI) is undergoing a paradigm shift, moving from historical data visualization to predictive, generative insights powered by Artificial Intelligence. While large enterprises leverage these tools for competitive advantage, the integration of Large Language Models (LLMs) and Large Multimodal Models (LMMs) into standard data pipelines remains a complex engineering challenge, particularly regarding Extract, Transform, Load (ETL) automation.
Methods: This study employs a comprehensive synthesis of recent literature (2020-2025) and theoretical modeling using the TOE-DOI framework. We analyze the efficacy of Adaptive Data Engineering (LLM-ADE) and compare intelligent automation tools (Dataverse vs. TPOT) in the context of optimizing BI workflows.
Results: The analysis indicates that traditional BI success factors are being superseded by the capability to automate unstructured data processing. The integration of LLM-ADE reduces data preparation latency by significant margins, enabling real-time competitive bidding advantages. Furthermore, the adoption of LMMs facilitates a deeper understanding of complex, multimodal market signals that traditional algorithms miss.
Conclusion: We conclude that the future of competitive advantage lies not merely in data access, but in the intelligent automation of the data lifecycle. Organizations that transition to adaptive, LLM-driven architectures will dominate proprietary information landscapes, though they must navigate new risks related to algorithmic hallucinations and cost management.
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Copyright (c) 2025 Dr. Talvion , A. Strelnikov
This work is licensed under a Creative Commons Attribution 4.0 International License.
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