2026· International journal of research and scientific innovation· Vol 13, pp. 246-274· 0 citations
TL;DR
The findings indicate that AI adoption for business decision-making is at a nascent stage, with 80% of respondents reporting either no or minimal usage, and AI has an overwhelmingly positive impact on decision quality and speed.
Abstract
This study investigates the impact of Artificial Intelligence (AI) on business decision-making within selected insurance companies in Kitwe District, Zambia. Employing a quantitative research design, the study collected data from 50 respondents across three insurance companies.
The findings indicate that AI adoption for business decision-making is at a nascent stage, with 80% of respondents reporting either no or minimal usage. AI application is highly concentrated in core operational areas such as risk assessment (80%), claims processing (70%), and regulatory compliance (70%). Conversely, AI is entirely absent in customer-facing and strategic functions, including customer service, marketing, investment and finance, and product development. Individual AI tool usage is notably low, with 70% of respondents indicating infrequent or no personal use, highlighting a disconnect between company-level adoption and individual utilisation.
Despite limited adoption, AI has an overwhelmingly positive impact on decision quality (80% reporting improvement) and speed (70% reporting acceleration), with no respondents indicating any negative impact. While a large majority (80%) express equal confidence in AI-supported decisions compared to traditional methods, the perceived impact on error reduction is limited. The most significant implementation challenges are unclear return on investment (98%), integration issues with legacy systems (95%), data privacy and security concerns (90%), and a lack of skilled personnel (80%). Other challenges include high implementation costs (60%), employee resistance (60%), and lack of strategic direction (50%). The study recommends that insurance organisations develop comprehensive AI strategies aligned with business objectives, invest in data infrastructure and governance, address skills gaps through training and development, and adopt phased implementation approaches.
The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.
Peter Stone· Research Journal in Business...· 0 citations
Artificial Intelligence (AI) has become a game-changing technology in finance. It greatly affects how financial institutions and individuals make decisions. By integrating AI into banking, investment management, insurance, digital payments, and advisory services, organizations have improved decision-making in terms of accuracy, efficiency, and speed. AI technologies like machine learning, predictive analytics, robo-advisors, and fraud detection help institutions examine large amounts of data, spot patterns, assess risks, and offer tailored financial solutions. As AI advances, it's crucial to understand users' awareness, use, and thoughts about these technologies for the ongoing growth of the financial sector.
This study aims to look at awareness, use, benefits, challenges, and the future of Artificial Intelligence in making financial decisions. It uses a descriptive research design with both primary and secondary data. We collected primary data from 100 respondents through a structured questionnaire using convenience sampling. Secondary data came from books, research articles, journals, reports, and other trustworthy sources. We analyzed the gathered data using percentage analysis and presented it in tables and graphs to show respondents' views.
The findings show that most respondents are aware of AI applications in financial services and often use AI-enabled platforms for banking, investments, and digital transactions. Many believe that AI improves financial decision-making by increasing accuracy, reducing human errors, enabling quicker decisions, and enhancing customer experience. Investment management, fraud detection, and financial forecasting were highlighted as the main areas where AI has the most significant impact. However, respondents also raised concerns about data privacy, cybersecurity, ethical issues, and limited human judgment in AI-driven financial systems.
The study concludes saying that Artificial Intelligence is now a vital tool for modern financial decision-making and will continue to influence the financial industry’s future. The study suggests that financial institutions should strengthen cybersecurity, improve transparency in AI systems, promote responsible governance, and raise customer awareness to ensure ethical, secure, and sustainable use of AI-driven financial services.
Vithan A. H., Kiran Giddappagol· International Journal For Mu...· 0 citations
The study aims to examine the impact of artificial intelligence (AI) on the quality of financial decision-making of investors in Maharashtra, India. Specifically, it examines the impact of AI predictive analytics (AIPA), data processing speed (DPS), bias reduction (BR), and cost savings (CS) on financial decision-making quality (FDMQ) and the moderating effect of investor experience (IE). Data was gathered from 228 investors in Maharashtra through a closed-ended questionnaire administered through Google Forms and WhatsApp. Structural Equation Modeling (SEM) was employed using SmartPLS 4 to examine the hypothesized relations. The research finds that AIPA, DPS, BR, and CS considerably enhance FDMQ. Moreover, investor experience positively moderates these connections and strengthens the role of AI on decision quality. This research confirms the utility of incorporating AI into investment decisions aimed at enhanced precision, speed, and justice of decisions. Moreover, experience-led training and tactful usage of AI aids become necessary to benefit from various profiles of investors. This study contributes to the current body of research on AI in finance and provides actionable recommendations for investors, financial institutions, and policymakers with empirical evidence from one of India's leading financial hubs.
C. Anirvinna, Jyoti Ranjana, Babita Jha et al.· International Journal of Inn...· 0 citations
This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.
Denise Nalini, Dr. S.Barathi, Dr. Rubidhadevi· The Journal of Theoretical A...· 0 citations
Artificial Intelligence (AI) is transforming investment management by enabling investors to make faster, data-driven, and informed financial decisions. This study examines the impact of Adoption of AI-Based Investment Tools, AI-Assisted Investment Decision-Making, Sustained Use of AI-Based Tools, AI-Enabled Mitigation of Behavioral Biases, and Future Intention to Use AI-Based Tools on the Investment Performance of individual investors in Jamshedpur. The study adopts a quantitative research approach using a structured questionnaire to collect primary data from 385 individual investors selected through convenience sampling. The collected data were analyzed using SPSS Statistics 17.0 and SmartPLS 4. The measurement model confirmed satisfactory reliability and validity, while the structural model revealed that all five independent variables have a positive and statistically significant impact on investment performance. Among the predictors, AI-Assisted Investment Decision-Making exhibited the strongest influence, followed by Sustained Use of AI-Based Tools. The findings highlight the growing importance of AI-enabled investment technologies in enhancing investment performance and provide valuable implications for investors, financial institutions, fintech companies, and policymakers seeking to promote technology-driven investment practices.
Jay Kishor Prasad, Dr. Sayak Gupta· The Journal of Theoretical A...· 0 citations
Generative artificial intelligence (GenAI) has moved from experimental novelty to a central input in organizational decision-making, with McKinsey's Q1 2026 Global AI Survey finding that 65 percent of organizations now use generative AI in at least one business function, roughly double the adoption rate reported ten months earlier, and 72 percent report at least one AI workload in production. Despite this scale of adoption, the empirical record on business value remains sharply divided. This paper synthesizes recent academic and industry evidence, drawing on 24 sources published primarily between 2023 and 2026, to examine three interlocking questions: what theoretical frameworks currently explain GenAI's role in managerial and strategic decision-making, how enterprises are applying GenAI in practice across functional areas, and what structural challenges limit the translation of GenAI adoption into measurable business value. The paper synthesizes evidence from strategic management research on AI-assisted evaluation of business alternatives, organizational theory on GenAI's emerging roles in decision processes, and empirical field studies on ambiguity handling and sycophantic behavior in AI-generated business advice, alongside a widely cited 2025 MIT study finding that 95 percent of enterprise generative AI pilots fail to deliver measurable profit-and-loss impact. Findings indicate that GenAI functions most reliably as an augmentation tool that aggregates and structures diverse inputs for human judgment, rather than as an autonomous decision-maker, that single-model evaluations of strategic alternatives are frequently inconsistent and biased while aggregated multi-model evaluations approximate expert human judgment, and that the primary barrier to enterprise value is organizational and workflow integration rather than model capability. The paper concludes with a proposed decision-integration framework and implications for executives, AI governance functions, and researchers.
M.SANGEETHA, Una Suman Kumar Patro, K.ARPITHA et al.· International journal of com...· 0 citations
