AI workload placement has quietly become one of the biggest challenges customers face in their deployments. It’s not just about getting a project off the ground anymore; it’s about where that workload lives. That one decision ends up dictating costs, security, control, power, and latency.
Nearly every organization surveyed (94%) reports regrets regarding their initial AI infrastructure strategy, according to a survey produced by Cisco in collaboration with Omdia.
Key takeaways:
· The research suggests the issue was less about AI technology failing and more about early decisions moving faster than teams could fully understand the economics, security requirements, and workload realities. While 96% of organizations shared that they have already adopted a hybrid approach—across cloud, on-premises data centers, and edge infrastructure—getting that mix right is proving to be a significant challenge.
- 34% of respondents wanted more rigorous cost analysis before the first deployment.
- 33% said they would have invested in their own infrastructure earlier.
- 28% felt they should have pushed back harder on the pace of AI adoption.
- 54% of organizations cite workload-specific optimization as the top reason for adopting a hybrid strategy.
- 67% of the respondents expecting their network to hit its limits due to the deluge of AI traffic within 12 months.
