A Study on the Role of AI Chatbots and Virtual Assistants in Influencing Customer Purchase Decisions in Nashik District

Abstract:

Small and Medium Enterprises (SMEs) generate an increasing volume of transactional and

operational data, yet many continue to rely on intuition-based rather than evidence-based

decision-making because enterprise-grade business-intelligence (BI) platforms have

historically been too costly and too IT-intensive for resource-constrained firms. The

emergence of self-service BI tools, and of Microsoft Power BI in particular, has lowered this

barrier by combining cloud-based deployment, native spreadsheet integration and a low-code

interface. This study examines the extent to which Power BI adoption is associated with

data-driven decision-making in SMEs. A structured questionnaire was administered to 132

SMEs drawn from the manufacturing, retail, services and IT/ITES sectors, and responses

were analysed using descriptive statistics, Pearson correlation and one-way ANOVA. The

results show that dashboard and reporting features are adopted far more widely than

data-modelling and natural-language-query features, and that the depth of Power BI adoption

is significantly and positively correlated with perceived decision-making speed (r = 0.58) and

accuracy (r = 0.52). Skill shortages, data-quality problems and licensing cost remain the

principal barriers to deeper adoption. The paper concludes that Power BI can meaningfully

strengthen data-driven decision-making in SMEs, but that the gains are presently concentrated

in descriptive reporting rather than predictive analytics, and offers recommendations for

capacity-building, data governance and phased adoption.