Part 8/11:
Additionally, decision-making processes within companies—such as approvals, budgeting, and change management—further delay implementation. When critical systems are involved, the risk of disrupting core operations makes companies cautious, sometimes opting to wait for proven stability rather than rushing to adopt cutting-edge AI.
Infrastructure and Cost Constraints
Another layer of complexity is infrastructural capacity. Even if major players like OpenAI, Google, or Microsoft are eager to scale AI services to meet global demand, current hardware limitations and energy constraints act as bottlenecks. Building the necessary data centers and ensuring reliable energy supply is no small feat, and such infrastructure requires significant time and investment.