MEASURING RETURNS ON GENERATIVE AI INVESTMENTS THROUGH EMPLOYEE PRODUCTIVITY, IMPLEMENTATION COSTS AND SERVICE QUALITY ANALYTICS
Jumoke Eniola Famoroti · International Journal of Engineering Technology Research & Management · 3 Nov 2026
Read original sourceOpen access
Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
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.
About this publication
Unranked journal. A journal that is not on the economics or computer-science rankings this guide uses. That says nothing about one paper, but no ranking vouches for the journal’s review.
- Published
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
Reaches a different conclusion
Papers on the same question whose finding points the other way, as read from the abstracts by an AI model (AI-written). Why studies differ.
- Experimental evidence on the productivity effects of generative artificial intelligence
Productivity: its finding supports the claim · Shakked Noy and Whitney Zhang · 2023 - The simple macroeconomics of AI
Productivity: its finding goes against the claim · Daron Acemoğlu · 2024
Related papers
On the same debate question, key papers first; related by topic, not by method or conclusion. Browse all: Productivity.
- Generative AI at Work
Erik Brynjolfsson et al. · The Quarterly Journal of Economics · 2025 - The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
Erik Brynjolfsson et al. · American Economic Journal Macroeconomics · 2020 - Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence
Francesco Filippucci et al. · OECD · 2024 - AI and Growth: Where Do We Stand?
Philippe Aghion and Simon Bunel · Federal Reserve Bank of San Francisco · 2024
Overview assembled automatically from collected metadata. How this site is built.