MEASURING RETURNS ON GENERATIVE AI INVESTMENTS THROUGH EMPLOYEE PRODUCTIVITY, IMPLEMENTATION COSTS AND SERVICE QUALITY ANALYTICS

Generative artificial intelligence is transforming organisational operations by automating knowledge intensiveactivities, supporting employee decision making, and enhancing customer service delivery. However, increasingenterprise investment has intensified concerns regarding financial returns, implementation expenditures,workforce adaptation, and the sustainability of operational improvements. Conventional return on investmentassessments often emphasise immediate cost savings while overlooking productivity variations, hiddenintegration expenses, and changes in service quality. This study proposes an integrated analytical framework forevaluating generative AI investments through employee productivity measurement, comprehensiveimplementation cost analysis, and multidimensional service quality assessment. Employee performance indicatorsencompass task completion time, output accuracy, workload distribution, and operational throughput.Implementation costs incorporate technology licensing, infrastructure, system integration, workforce training,governance, and ongoing maintenance. Service quality analytics examine response times, customer satisfaction,error frequencies, and service consistency. Difference in differences estimation and cost benefit modelling providecomplementary approaches for attributing operational changes to AI adoption and calculating risk adjustedfinancial returns. The framework supports evidence based investment decisions, identifies conditions influencingvalue realisation, and establishes measurable criteria for evaluating long term organisational performance acrossdiverse business functions and implementation environments.