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An Examination of the Role of Artificial Intelligence in Business Process Automation: A Case Study of Manufacturing Firms in Plateau State

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  • NGN 5000

Background of the Study

Business process automation (BPA) is an essential strategy for companies seeking to improve efficiency, reduce human error, and lower operational costs. The integration of artificial intelligence (AI) into BPA has revolutionized the way manufacturing firms approach tasks such as inventory management, quality control, and production scheduling (Vashisht & Kumar, 2023). AI-driven systems enable the automation of complex decision-making processes, thereby improving process consistency, enhancing operational efficiency, and enabling real-time decision-making.

Manufacturing firms in Plateau State have increasingly turned to AI technologies such as machine learning, predictive analytics, and natural language processing to automate key operations. For example, AI can optimize supply chain management by predicting demand, detecting anomalies, and automating procurement decisions (Saini, 2024). Despite these advancements, the adoption of AI in business process automation remains slow, particularly in the manufacturing sector of Plateau State. Firms face challenges in terms of cost, lack of skilled labor, and integration with legacy systems. This study aims to examine the role of AI in automating business processes within manufacturing firms in Plateau State and assess its impact on operational efficiency.

Statement of the Problem

The manufacturing sector in Plateau State struggles with inefficiencies in business processes, ranging from production scheduling to inventory management. While AI presents an opportunity to streamline these processes, the adoption rate remains low due to several barriers such as high implementation costs, lack of expertise, and resistance to change. This study seeks to explore how AI technologies can be leveraged for business process automation, the challenges that firms face in adoption, and the benefits that can be gained by overcoming these challenges.

Objectives of the Study

  1. To explore the role of artificial intelligence in automating business processes in manufacturing firms in Plateau State.
  2. To assess the impact of AI-driven automation on operational efficiency in manufacturing firms.
  3. To identify the challenges and opportunities of implementing AI-based business process automation in manufacturing firms in Plateau State.

Research Questions

  1. What role does artificial intelligence play in automating business processes in manufacturing firms in Plateau State?
  2. How does AI-driven automation affect operational efficiency in manufacturing firms?
  3. What are the challenges faced by manufacturing firms in Plateau State when implementing AI-based business process automation?

Research Hypotheses

  1. AI-driven business process automation does not significantly improve operational efficiency in manufacturing firms in Plateau State.
  2. The adoption of AI technologies in business process automation does not significantly reduce operational costs in manufacturing firms in Plateau State.
  3. The challenges faced by manufacturing firms in Plateau State in implementing AI-based automation do not significantly affect the overall success of the adoption.

Scope and Limitations of the Study

This study focuses on manufacturing firms in Plateau State, specifically analyzing how AI is utilized in business process automation and its effects on operational efficiency. The main limitation is that it will only cover a subset of firms within Plateau State, which may not represent the entire manufacturing sector in Nigeria.

Definitions of Terms

Artificial Intelligence (AI): The simulation of human intelligence processes by machines, particularly computer systems, which include learning, reasoning, and self-correction.
Business Process Automation (BPA): The use of technology to automate repetitive tasks and processes in business operations to improve efficiency and reduce manual effort.
Operational Efficiency: The ability to deliver products or services in the most cost-effective manner without compromising quality.





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