Balancing AI Efficiency and Ethics for Long-Term Business Sustainability

Abstract

Artificial Intelligence (AI) integration reshapes organisational decision-making, yet its sustainability implications remain underexplored. This study examines the impact of AI on productivity, economic viability, and ethical considerations in mid-sized companies across the healthcare, finance, and manufacturing sectors. Using a mixed-methods approach, data were collected from 50 AI-integrated firms through surveys and stakeholder interviews. Quantitative analysis (using SPSS and R) revealed that AI reduced task completion time by 40% and error rates by 80%, resulting in a net annual benefit of $1.27 million and a payback period of one year. However, ethical challenges emerged, including algorithmic bias (detected in 12% of AI-driven decisions) and concerns about transparency, necessitating robust governance frameworks. A novel mathematical model incorporating a sigmoid function was developed to balance efficiency gains with ethical risks. Findings suggest that AI enhances operational efficiency and financial returns; however, its long-term sustainability depends on effective bias mitigation, regulatory compliance, and employee adaptation strategies. This research provides a comprehensive framework for sustainable AI adoption, guiding businesses in maximising efficiency while ensuring equitable and transparent decision-making. The study contributes to sustainability discourse by bridging AI-driven innovation with responsible governance, fostering resilient and adaptive business models.