Manuscript Number : SHISRRJ24781

Taxation Law Compliance and Corporate Governance: Utilizing Business Analytics to Develop Effective Legal Strategies for Risk Management and Regulatory Adherence

Authors(4) :-Bolanle A. Adewusi, Bolaji Iyanu Adekunle, Sikirat Damilola Mustapha, Abel Chukwuemeke Uzoka

As organizations strive to enhance efficiency, reduce operational costs, and deliver high-value products in competitive markets, the integration of Artificial Intelligence (AI) into the product lifecycle has emerged as a transformative strategy. This paper presents a conceptual framework for embedding AI models across the end-to-end product lifecycle spanning ideation, design, development, manufacturing, marketing, and after-sales service. The framework offers a structured approach to achieving cost reduction and process optimization by leveraging AI capabilities such as predictive analytics, machine learning, natural language processing, and intelligent automation. The proposed framework is organized into five interdependent layers: Data Acquisition and Management, Model Training and Deployment, Lifecycle Phase Integration, Feedback Loops and Continuous Learning, and Governance and Ethical Oversight. Each layer ensures AI functionality aligns with business goals, regulatory standards, and product quality expectations. AI-driven insights are used to detect design inefficiencies early, predict equipment failures, automate quality assurance, personalize user experience, and streamline supply chain operations. Furthermore, the framework emphasizes the use of digital twins, simulation, and demand forecasting to optimize resources and minimize waste throughout the lifecycle. A key contribution of this work is the integration of feedback mechanisms that enable real-time adjustments to AI models based on changing product usage patterns and market dynamics. The framework also incorporates explainable AI (XAI) and model monitoring to ensure transparency, trust, and continuous value creation. By embedding AI as an intelligent layer across lifecycle stages, organizations can move from reactive to proactive decision-making, achieving significant improvements in agility, product performance, and customer satisfaction. This conceptualization serves as a roadmap for product managers, data scientists, and operational leaders seeking to align AI investments with measurable business outcomes. It bridges technical design with strategic execution, ensuring scalable, ethical, and cost-effective AI deployment. The framework holds potential across sectors such as manufacturing, healthcare, consumer electronics, and automotive, where complex product ecosystems demand dynamic optimization.

Authors and Affiliations

Bolanle A. Adewusi
The Scholarship Whizz, Ontario, Canada The Innovative Woman Africa Initiative, Abuja, Nigeria / TELUS Communications, Toronto, Ontario, Canada
Bolaji Iyanu Adekunle
Department of Data Science, University of Salford, UK
Sikirat Damilola Mustapha
Montclair State University, Montclair, New Jersey, USA
Abel Chukwuemeke Uzoka
The Vanguard group Charlotte, North Carolina, USA

AI Integration, Product Lifecycle, Cost Reduction, Process Optimization, Predictive Analytics, Digital Twin, Intelligent Automation, Explainable AI, Model Governance, Continuous Learning.

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Publication Details

Published in : Volume 7 | Issue 6 | November-December 2024
Date of Publication : 2024-12-12
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 162-203
Manuscript Number : SHISRRJ24781
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

Bolanle A. Adewusi, Bolaji Iyanu Adekunle, Sikirat Damilola Mustapha, Abel Chukwuemeke Uzoka, "Taxation Law Compliance and Corporate Governance: Utilizing Business Analytics to Develop Effective Legal Strategies for Risk Management and Regulatory Adherence", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 7, Issue 6, pp.162-203, November-December.2024
URL : https://shisrrj.com/SHISRRJ24781

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