📋 Tender Overview
Perspectives from global IT decision makers
Today, AWS launched findings from its global Gen AI Adoption Index research. This study surveyed 3,739 senior IT decision-makers in nine countries across industries such as financial services, information and communications technology, manufacturing, and retail. Survey regions include US, Brazil, Canada, France, Germany, India, Japan, South Korea, and the United Kingdom (UK).To review the full report, click .Key insights uncovered in the research include:Establishing dedicated AI leadership positions to drive organizational-wide transformation As organizations scale their AI initiatives, leadership structures are evolving to meet new demands and creating new career opportunities for AI-skilled talent at the highest levels of organizations. While Chief Technology Officers (CTOs) and Chief Innovation Officers (CIOs) currently lead most IT transformation initiatives, 60% of organizations globally have appointed a dedicated AI executive, such as Chief AI Officer, to accelerate adoption and manage implementation complexity.
Rethinking gen AI talent strategyTo meet the growing demand for AI talent, organizations globally are adopting a dual strategy of developing internal workforce combined with external talent acquisition to accelerate enterprise-wide gen AI adoption. According to the study, organizations are planning significant hiring initiatives, with 92% preparing to recruit for roles requiring gen AI skills in 2025.
Adopting a blended approach to drive gen AI business transformationToday, as organizations are moving their gen AI workloads into production, they are increasingly combining their proprietary data with customization capabilities to achieve efficiency, scalability, and high performance, all while keeping their data safe. The research shows that while 40% of organizations plan to use AI models off-the-shelf directly, a majority are taking a hybrid approach with 58% planning to develop custom applications using pre-existing models, and 55% will build applications on fine-tuned models using proprietary data.
