THE IMPACT OF ARTIFICIAL INTELLIGENCE ON STRATEGIC DECISIONMAKING IN BUSINESS MANAGEMENT

The accelerating integration of Artificial Intelligence (AI) into business ecosystems has
transformed traditional approaches to strategic decision-making, reshaping how organizations
perceive, process, and act upon complex information. This study investigates the impact of
Artificial Intelligence on strategic decision-making in business management within the Nigerian
context, emphasizing how AI-driven systems influence managerial efficiency, accuracy, and
responsiveness. Grounded in the Technology Acceptance Model (TAM) and Dynamic Capabilities
Theory, the research adopts a mixed-methods approach combining quantitative surveys and
qualitative interviews to generate empirical insights from 300 managers across banking,
telecommunications, manufacturing, and logistics sectors. The findings reveal that AI technologies
particularly machine learning, predictive analytics, and natural language processing significantly
enhance decision quality by improving forecasting accuracy, optimizing resource allocation, and
reducing cognitive biases in managerial judgments. Results further indicate that firms with
adaptive learning cultures and strong digital infrastructures demonstrate higher levels of AI
assimilation and strategic agility. However, infrastructural deficits, limited digital literacy, and
ethical concerns such as algorithmic bias and data insecurity continue to constrain AI’s full
potential in Nigeria’s corporate landscape. The study concludes that AI functions not merely as an
operational enabler but as a transformative strategic capability that redefines organizational
competitiveness, leadership reasoning, and long-term sustainability. It recommends targeted
investments in digital infrastructure, capacity development, and ethical AI governance to
strengthen Nigeria’s readiness for data-driven strategic transformation. The findings contribute to
the growing scholarship on digital innovation and strategic management in emerging economies,
offering theoretical and practical implications for policymakers, scholars, and business leaders
seeking to align technological adoption with sustainable competitive advantage.

Funding: This is self-funded research.

Conflict of Interest: The authors declare no conflicts of interest.

Ethics Approval: Not_applicable

Data Availability: Data will be made available upon reasonable request.

AI-Assisted Writing: No AI tools were used in the preparation of this manuscript.