So where are the roadblocks today? We dove into two primary questions with our network around bottlenecks and justification: What are the main bottlenecks for the wider adoption of/budgeting for AI? (5 = Highly Problematic, 1 = Not too problematic) - Concerns About Trust, Safety and Security - Lack of Talent to Build and Deploy Solutions – Data Privacy - Lack of Budgets to Implement Gen AI at Scale - Regulations - Lack of Value to Justify Investment So, the challenges today lie in talent and security, with potential lack of value as the least likely reason respondents came back with. The next question we wanted to understand was how teams are justifying spend in a budget year that may be flat for many teams: What is the primary way your company will justify spending on new generative AI use cases? No new budgets, the money will have to come from existing funding Budgets will mostly from employee productivity savings New budgets will be created based on the gen AI needs of the BUs Costs will be passed to consumers, with superior added functionality Results were somewhat varied, with similar numbers of respondents reporting they’d be expanding budget, displacing budget, or hoping for productivity increases. Most, however, seemed to think it’s too early to pass on costs to customers just yet.