Paper Title

Harnessing Artificial Intelligence for Business Transformation in Traditional Industries

Article Identifiers

Registration ID: IJNRD_226980

Published ID: IJNRD2307490

DOI: Click Here to Get

Authors

KUMAR KODYVAUR KRISHNA MURTHY , A RENUKA , PANDI KIRUPA GOPALAKRISHNA PANDIAN

Keywords

Harnessing Artificial Intelligence for Business Transformation in Traditional Industries

Abstract

Traditional industries are being transformed by AI, which is changing company operations and strategy. AI technologies like machine learning, natural language processing, and predictive analytics may boost productivity, save expenses, and generate new income. This transition is especially significant in labor-intensive industries like manufacturing, agriculture, logistics, and retail, which have been hesitant to adopt new technology. These sectors are streamlining operations and reinventing their business models using AI to compete in a digital economy. AI-driven automation and predictive maintenance boost production efficiency and reduce downtime. Machine learning systems can forecast equipment breakdowns, enabling prompt maintenance and eliminating expensive disruptions. Real-time data analytics enhance production processes, quality control, and waste reduction in smart manufacturing, enabled by AI. Industry 4.0 is enabled by networked systems and AI-driven insights, making production more flexible, efficient, and responsive. AI is changing agriculture, another conventional business. Precision agriculture, enabled by AI, helps farmers monitor crops, anticipate yields, and optimize water and fertilizer usage. AI-driven drones and sensors generate massive volumes of data that may improve agricultural management, food security, and environmental impact when examined. This change is essential to feeding a rising global population sustainably. AI helps logistics companies optimize supply chains and delivery efficiency. AI algorithms improve route planning, fuel efficiency, and delivery time prediction. AI-driven demand forecasting improves inventory management, lowering costs and enhancing customer happiness. AI in logistics is improving operations and allowing new business models like on-demand delivery to meet customer expectations. Retail, typically reluctant to change, is using AI to improve customer experience and revenue. AI-powered recommendation engines, chatbots, and tailored marketing are changing business-customer interactions. AI can forecast purchase behavior, customize marketing, and improve price by evaluating consumer data. Personalization allows organizations to provide more focused and relevant goods and services, enhancing consumer loyalty and income. However, conventional sectors have hurdles while adopting AI. Job displacement, data privacy, and AI ethics must be addressed. As AI evolves, organizations must carefully negotiate these obstacles to ensure that AI serves the enterprise and society. In a fast-changing world, conventional sectors must use AI to innovate and flourish. AI, Business Transformation, Traditional Industries, Machine Learning, Predictive Analytics, Smart Manufacturing, Precision Agriculture, Logistics, Retail, Industry 4.0, AI-driven Innovation.

How To Cite

"Harnessing Artificial Intelligence for Business Transformation in Traditional Industries", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 7, page no.e746-e761, July-2023, Available :https://ijnrd.org/papers/IJNRD2307490.pdf

Citation

Issue

Volume 8 Issue 7, July-2023

Pages : e746-e761

Other Publication Details

Paper Reg. ID: IJNRD_226980

Published Paper Id: IJNRD2307490

Downloads: 000121146

Research Area: Engineering

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2307490.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2307490

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 8 | August 2025

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