Integration of Artificial Intelligence in Enterprise Resource Planning Systems: Opportunities, Challenges, and Future Research Directions

Authors

  • Sushil Kumar Sahoo 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela 769015, Odisha, India Author https://orcid.org/0000-0002-8551-7353
  • Dwarikanath Choudhury 3) Great Lakes Institute of Management, Gurgaon Campus, Delhi NCR, 122413, Haryana, India Author https://orcid.org/0000-0003-0977-5155
  • Prasant Ranjan Dhal 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela 769015, Odisha, India Author https://orcid.org/0000-0002-7402-2187
  • Supriya Sahu 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela 769015, Odisha, India Author https://orcid.org/0000-0002-1823-9568
  • Sudhakar Majhi 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela 769015, Odisha, India Author https://orcid.org/0000-0003-2213-8791
  • Ipsita Dhar 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela 769015, Odisha, India Author https://orcid.org/0009-0007-4086-8137

DOI:

https://doi.org/10.66972/iscis21202622

Keywords:

Artificial Intelligence, Enterprise Resource Planning, Intelligent Enterprise Resource Planning, Machine Learning, Natural Language Processing, Predictive Analytics, Bibliometric Analysis, Systematic Literature Review

Abstract

Enterprise resource planning (ERP) systems have come a long way since their inception in 1989 and have grown from transaction processing systems to intelligent enterprise systems with the help of artificial intelligence (AI). This study aims to conduct a systematic literature review and bibliometric analysis of the current literature on the integration of AI in ERP systems from 1989 to 2026. Based on the PRISMA framework, relevant publications are identified in the major scientific databases in the context of bibliometric and thematic analyses, which highlight trends, impactful participants, new technologies, application fields, and future research directions. The review shows that research on AI-driven ERP is growing at a rapid pace, especially in recent years due to the development of machine learning, deep learning, NLP, generative AI, intelligent automation, and predictive analytics. The primary applications include finance, supply chain management, manufacturing, human resources, customer relationship management, and decision support. The study also presents a set of key research gaps on data governance, cybersecurity, interoperability, sustainable ERP, and human-AI collaboration, and outlines a conceptual framework and future research agenda to guide the creation of intelligent, trustworthy, and autonomous ERP ecosystems.

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Published

2026-08-31

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Vol. 2 No. 1 (2026): International Scientific Spectrum

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Articles

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Copyright (c) 2025 Sushil Kumar Sahoo, Dwarikanath Choudhury, Prasant Ranjan Dhal, Supriya Sahu, Sudhakar Majhi, Ipsita Dhar (Author)

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How to Cite

Sahoo, S. K., Choudhury, D. ., Dhal, P. R., Sahu, S., Majhi, S., & Dhar, I. . (2026). Integration of Artificial Intelligence in Enterprise Resource Planning Systems: Opportunities, Challenges, and Future Research Directions. International Scientific Spectrum, 2(1), 123-152. https://doi.org/10.66972/iscis21202622