AI-powered decision-making framework for team management and financial operations in corporate and public finance departments

Effective team management and financial operations are critical for the success of corporate and public finance departments. However, traditional decision-making processes often lack the agility and data-driven insights required to address complex challenges in dynamic environments. This study introduces an AI-Powered Decision-Making Framework (AI-DMF) that integrates artificial intelligence (AI) insights with leadership strategies to enhance team productivity, optimize financial operations, and support evidence-based decision-making. The proposed framework combines machine learning (ML) algorithms, natural language processing (NLP), and predictive analytics to process large volumes of structured and unstructured data, delivering actionable insights for team performance monitoring, financial forecasting, and strategic planning. By leveraging real-time data, AI-DMF enables managers to make informed decisions on resource allocation, performance evaluation, and risk management. Additionally, the framework incorporates human-AI collaboration mechanisms, ensuring that leadership strategies are guided by technological insights while maintaining adaptability and creativity. Key components of the AI-DMF include data integration modules for aggregating team and financial data, decision support tools for scenario analysis, and performance optimization algorithms for identifying inefficiencies and opportunities. Implementation of the framework is supported by customizable dashboards that provide stakeholders with clear visualizations of key performance indicators (KPIs), trends, and projections. Findings from case studies reveal that organizations adopting the AI-DMF report up to a 40% increase in team productivity and a 35% improvement in financial planning accuracy. Furthermore, the model enhances risk detection and response times, contributing to better compliance and operational resilience. This research offers a transformative approach to integrating AI into team and financial management practices, addressing the limitations of conventional methods while fostering innovation. The AI-DMF is designed to empower corporate leaders, public finance managers, and policymakers by enabling them to navigate complex decision-making environments effectively. Keywords: AI-Powered Framework, Decision-Making, Team Management, Financial Operations, Machine Learning, Predictive Analytics, Leadership Strategies, Corporate Finance, Public Finance, Productivity Optimization.

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Publication Details

Journal
Gulf Journal of Advance Business Research
Published
2025-02-23
DOI
https://doi.org/10.51594/gjabr.v3i2.107
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
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article

AI-powered decision-making framework for team management and financial operations in corporate and public finance departments

Ejuma Martha Adaga, Ngodoo Joy Sam-Bulya, Bamidele Michael Omowole, Ibidapo Abiodun Ogundeji
1 citations
Gulf Journal of Advance Business Research
Big Data and Business Intelligence
2.28
article

AI-powered decision-making framework for team management and financial operations in corporate and public finance departments

Ejuma Martha Adaga, Ngodoo Joy Sam-Bulya, Bamidele Michael Omowole, Ibidapo Abiodun Ogundeji
article en
1 citations

Abstract

Effective team management and financial operations are critical for the success of corporate and public finance departments. However, traditional decision-making processes often lack the agility and data-driven insights required to address complex challenges in dynamic environments. This study introduces an AI-Powered Decision-Making Framework (AI-DMF) that integrates artificial intelligence (AI) insights with leadership strategies to enhance team productivity, optimize financial operations, and support evidence-based decision-making. The proposed framework combines machine learning (ML) algorithms, natural language processing (NLP), and predictive analytics to process large volumes of structured and unstructured data, delivering actionable insights for team performance monitoring, financial forecasting, and strategic planning. By leveraging real-time data, AI-DMF enables managers to make informed decisions on resource allocation, performance evaluation, and risk management. Additionally, the framework incorporates human-AI collaboration mechanisms, ensuring that leadership strategies are guided by technological insights while maintaining adaptability and creativity. Key components of the AI-DMF include data integration modules for aggregating team and financial data, decision support tools for scenario analysis, and performance optimization algorithms for identifying inefficiencies and opportunities. Implementation of the framework is supported by customizable dashboards that provide stakeholders with clear visualizations of key performance indicators (KPIs), trends, and projections. Findings from case studies reveal that organizations adopting the AI-DMF report up to a 40% increase in team productivity and a 35% improvement in financial planning accuracy. Furthermore, the model enhances risk detection and response times, contributing to better compliance and operational resilience. This research offers a transformative approach to integrating AI into team and financial management practices, addressing the limitations of conventional methods while fostering innovation. The AI-DMF is designed to empower corporate leaders, public finance managers, and policymakers by enabling them to navigate complex decision-making environments effectively. Keywords: AI-Powered Framework, Decision-Making, Team Management, Financial Operations, Machine Learning, Predictive Analytics, Leadership Strategies, Corporate Finance, Public Finance, Productivity Optimization.

Gulf Journal of Advance Business ResearchVol. 3(2)
University of the Potomac (US)
Openalex Percentile: Top 15%
Big Data and Business Intelligence
2.28
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