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MUHAMMAD NURU

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MUHAMMAD NURU

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  1. TITLE: LEVERAGING AI TO OVERCOME CHALLENGES IN ERP ADOPTIONSUBTITLE: EXPLORING COMMON BARRIERS AND INTELLIGENT SOLUTIONS IN IMPLEMENTATION OF ERPPRESENTED BY: 246360019 – ABDULKARIM MOHAMMAD NURUDEPARTMENT: M.SC. INFORMATION TECHNOLOGYDATE: 28/05/2025

  2. Introduction • ERP (Enterprise Resource Planning) systems are important and necessary for managing business activities. • Though beneficial to businesses, but implementations often face some challenges. • This research focus on exploring how AI can mitigate these challenges.

  3. Background of ERP Systems • Purposeand definition of Enterprises Resource Planning (ERP) • Core modules (Finance, HR, Inventory, CRM, etc.) • Significance/Importanceof ERP in digitization

  4. Importance of ERP Implementation • Process integration • Easy data access • Help in Decision-making process • Flexibility and automation

  5. Common Challenges in ERP Implementation • User resistance • Data migration difficulties • High cost • Lack of proper training • Improper change management • Poor leadership

  6.  Focused Barriers for Study • User resistance • Data migration Issues • Lack of Proper Change Management

  7. Research Problem Statement • Organizational aspects, technical challenges, and resistance problems lead to poor user adoption and acceptance of ERP resulting in either failure or prolonged overdue ERP projects. AI-integrated problem-solving tools to address these persistent issues are inadequately explored.

  8. Research Aim • To investigate how Artificial Intelligence (AI) can be leveraged to identify, analyze, and address the common challenges organizations face during ERP implementation.

  9. Research Objectives • Determine what the most common barriers to ERP implementation are. • Assess AI applicability in solving ERP implementation challenges. • Formulate AI-based recommendations for successful ERP implementation.

  10. Research Gap • AI’s contribution to effective ERP implementation is underexplored in the existing literature. • There is a lack of examination of contemporary approaches derived from research-dominated data • There is a gap in empirical focus on development accessible on AI solutions designed for small and medium enterprises in developing nations.

  11. Role of AI in ERP Implementation • Automation of workflows • Predictive analytics for project risks • User support through chatbots • Intelligent decision-making tools

  12. AI Tools Used in the Study • Natural Language Processing (NLP): Assessing online user feedback. • Machine Learning (ML): Making estimates for prospective risks and project failures. • Chatbots: Facilitates assistance during system implemention. • Decision Support Systems: Enables project team to utilize constructive information for decision making.

  13. AI Application in User Resistance • Sentiment analytics encompassing user feedback • Completing training sessions through AI-powered platforms • Round-the-clock (24/7) chatbots for assistance.

  14. AI Application in Data Migration • AI resources for cleaning, formatting and mapping • Detection of errors during transfer. • Data validation and verification using smart technology. • Models predicting risks for data migration.

  15. AI Application in Change Management • AI-driven insights on employee readiness • Virtual trainers for training and onboarding. • Assessing how different changes would affect workflows. • Marking and monitoring problems alongside progress in real time.

  16. Summary of Key Findings • AI tools provide flexibility and intelligent solutions • While deploying ERP system, there’s need for early integration of AI, because it helps in reducing risks • AI tools enhances user engagement, training, and decision-making

  17. Recommendations • AI integration should start in the early phase of the implementation. In the planning phase to be precise. • the use of AI tool for continuous monitoring and feedback is important. • To provide AI-powered training and support tools • Encourage leadership to invest in AI-based ERP strategies

  18. Conclusion • The adoption of ERP is complex but highly necessary. • The Integration of AI tools in ERP implementation process mitigates risks, improves efficiency, and enhances user acceptance. • This project highlights a modern, data-driven approach to successful ERP implementation and deployment.

  19. References • Olayemi, O. S., & Afolabi, B. (2017). Challenges of ERP implementation in Nigerian manufacturing companies. Journal of African Business, 18(4), 468–487. https://doi.org/10.1080/15228916.2017.1304090 • Oyedolapo, A., & Jimoh, R. G. (2021). Adoption of ERP systems in Nigeria: A study of SMEs in Lagos State. International Journal of Scientific and Research Publications, 11(3), 311–318. https://doi.org/10.29322/IJSRP.11.03.2021.p11140 • Eze, S. C., Chinedu-Eze, V. C., & Bello, A. O. (2018). Determinants of dynamic process capabilities in Nigerian SMEs: The role of cloud ERP systems and ICT. Journal of Enterprise Information Management, 31(1), 38–58. https://doi.org/10.1108/JEIM-04-2017-0050 • Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J. (2018). Performance analysis of IoT-based sensor, machine learning, and fog computing for predicting and classifying the risk of ERP implementation failure. Sustainability, 10(10), 3662. https://doi.org/10.3390/su10103662 • IBM. (2020). AI in ERP: Transforming enterprise planning. IBM Cloud Blog. https://www.ibm.com/cloud/blog/ai-in-erp

  20. References (Cond’t) • McKinsey & Company. (2021). The state of AI in 2021. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2021 • Al-Fawaz, K., Al-Salti, Z., & Eldabi, T. (2008). Critical success factors in ERP implementation: A review. In European and Mediterranean Conference on Information Systems (EMCIS). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=9ed47c8bd46a55e1876a98008d8ab81f3ac760e3 • Ojo, A., & Popoola, O. (2020). Artificial intelligence applications in Nigerian business systems: Challenges and opportunities. African Journal of Information Systems, 12(2), 59–74. https://digitalcommons.kennesaw.edu/ajis/vol12/iss2/4/ • Adesina, D. O. (2022). Developing an ERP adoption framework for the retail industry of a developing country: Case study of a Nigerian company [Master’s thesis, LUT University]. LUTPub. https://lutpub.lut.fi/handle/10024/165965

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