M ajor technology companies have spent two years implementing generative AI ( Gen AI ) across their operations , deploying AI models for tasks ranging from customer service to code development . These implementations have revealed challenges in data quality , security , and workforce adaptation that must be addressed as enterprises move from experimental deployments to production systems .
The rapid advancement of AI capabilities – particularly in large language models that power tools like ChatGPT – has driven widespread adoption across industries . However , enterprises face obstacles in achieving measurable returns on their AI investments . These include data quality issues , skills shortages and governance