Authors: Assistant Professor Gangawane Anand Bhagwat
Abstract: We live in a digital age when artificial intelligence (AI) and machine learning (ML) are changing the face of business. When organizations use a vast array of data to run their business — from customer interactions to sensor signals — they can automate things, see actionable patterns, and improve their decision making. But many companies fail to scale their pilot projects into companywide benefits, crippled by data silos, skills gaps and ethical concerns. Objectives: This study explores the extent and use of AI/ML for the critical business value drivers, including process automation, decision support, customer engagement, supply-chain availability, and human resources management. Its objectives are to assess efficiency and accuracy gains of the solution and to understand organizational drivers and barriers, and to suggest a human-centred framework for responsible AI/ML adoption. Methods: We conducted a systematic literature review that included peer-reviewed articles, conference papers, and industry reports published from 2014 to 2022. Searches on “AI business operations” and “machine learning adoption” were conducted in Scopus, Web of Science, and Google Scholar. Two authors screened more than 1200 records independently and used the inclusion criteria to identify 75 highly relevant articles. Data extraction used a template tested with a pilot of studies and recorded intervention types, outcomes, and implementation questions. Qualitative metrics and narrative insights were reduced using thematic analysis. Results: All AI/ML implementations delivered double-digit enhancements: cycle times were reduced up to 60 60%, forecasting errors were reduced by 50%, cross-sell rates increased by 25%, stockout frequency dropped by 67%, while time-to-hire fell by 70%. Case studies in manufacturing, retail, finance, and telecom showed that the key to success manifests through solid data pipelines, tight stakeholder involvement, and a strong human-machine collaboration. Conclusion: AI/ML value will not be realized if implementations cannot be treated as socio-technical change. Businesses that marry technical proficiency with ethical stewardship, clear performance measurements, and ongoing employee upskilling will be most adept at leveraging AI/ML as strategic disruptors for agility, efficiency innovation.
