A Critical Analysis of the Application of Artificial Intelligence in SME Growth Prediction: A Study of Startups in Kano State
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- NGN 5000
Background of the Study
Artificial intelligence (AI) is increasingly being used by small and medium-sized enterprises (SMEs) to predict growth trajectories and inform decision-making. AI-driven growth prediction models analyze factors such as market trends, customer data, and operational metrics to forecast business outcomes and guide strategic planning (Yakubu & Suleiman, 2024).
In Kano State, startups face unique challenges such as resource constraints, market volatility, and limited access to advanced technology. The application of AI offers these startups an opportunity to overcome these challenges and drive growth. This study critically analyzes the role of AI in predicting and enhancing SME growth in Kano State.
Statement of the Problem
Many startups in Kano State struggle with limited resources and unpredictable market conditions, making it difficult to achieve sustainable growth. Traditional methods of growth prediction are often unreliable, lacking the precision and scalability offered by AI (Bello & Musa, 2025).
Despite the potential of AI in growth prediction, its adoption among startups in Kano State is limited due to factors such as high implementation costs, lack of technical expertise, and inadequate infrastructure. This study explores the application and challenges of using AI for growth prediction in this context.
Objectives of the Study
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To evaluate the adoption of AI-driven growth prediction models among startups in Kano State.
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To analyze the impact of AI on SME growth and decision-making.
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To identify challenges and propose strategies for improving AI adoption in startups.
Research Questions
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How widely are AI-driven growth prediction models adopted by startups in Kano State?
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What impact does AI have on SME growth and decision-making?
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What challenges hinder AI adoption in startups, and how can they be addressed?
Research Hypotheses
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AI-driven growth prediction models have no significant impact on SME growth.
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The adoption of AI does not significantly improve decision-making in startups.
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Addressing challenges has no significant effect on the adoption of AI-driven growth prediction models.
Scope and Limitations of the Study
The study focuses on startups in Kano State and their use of AI for growth prediction. Limitations include access to proprietary business data, variability in AI expertise, and the rapidly evolving nature of AI technologies.
Definitions of Terms
Artificial Intelligence (AI): The simulation of human intelligence by machines to perform tasks such as prediction and decision-making.
Growth Prediction: The process of forecasting a business's growth trajectory using data and analytics.
Startups: Newly established businesses typically focused on innovative solutions.
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