Published Paper Details:

The study explored the adoption and impact of Artificial Intelligence (AI) in the financial decision-making processes of Indian businesses, revealing several critical insights. Descriptive and inferential statistical analysis showed that AI adoption varies significantly by firm size and sector. Large organizations and technology-driven industries like IT, finance, and e-commerce reported higher AI integration--over 75% adoption--while smaller firms and traditional sectors like agriculture and manufacturing lagged behind with adoption rates under 35%. ANOVA tests confirmed significant differences in adoption levels across sectors (F = 18.7, p < 0.01), and regression analysis identified firm size, digital maturity, and industry type as strong predictors of AI usage. Paired t-tests showed a 28% average reduction in forecasting errors (p < 0.01), and decision-making time was reduced by 30% in AI-adopting firms, confirming that AI improves both accuracy and speed of financial decisions. In terms of perceptions, survey responses from 250 financial professionals indicated that 76% believe AI enhances decision quality, while 75% acknowledged improved operational efficiency. However, 62% expressed ethical concerns, particularly regarding algorithmic bias and data privacy, while 54% feared job displacement. Chi-square and ordinal logistic regression analyses supported these mixed perceptions, showing that while AI is valued for efficiency and accuracy, professionals remain cautious due to potential risks. Factor analysis identified key enablers of effective AI-based financial decision-making as data quality, user training, and system transparency. These findings underscore that while AI offers transformative benefits for financial operations, its successful adoption depends heavily on sectoral readiness, firm capacity, and ethical governance.