A few weeks back, this newsletter featured an analysis by Steve Leaden, founder of Leaden Associates and president of the Society of Communications Technology Consultants (SCTC), suggesting that enterprises consider hybrid cloud strategies as they move forward with communications architectures. As Steve wrote, “The question has shifted from, ‘Which platform should we standardize on?’ to, ‘Which deployment model best fits each workload?’ For large enterprises, the answer is increasingly hybrid.”
What’s true of communications platforms seems also to be true of AI capabilities. In a report commissioned by Cisco, analyst firm Omdia found that hybrid deployments make the most sense in many AI scenarios. (Omdia is owned by Enterprise Connect parent company Informa.) The Omdia report states:
“Ninety-six percent of organizations already run AI across a mix of cloud, on-premises, and edge infrastructure. That is not a transitional state; it is where AI infrastructure lives, and it is where it is likely going to stay. The data shows a slight expected shift toward public cloud, but the overall hybrid model stays dominant—and it is being actively rebalanced. In fact, 60% of organizations also shared they have already moved AI workloads from the cloud back to on-premises, a clear signal that early deployment decisions are being corrected.”
Omdia found, not surprisingly, that on-premises deployment is favored for models that use proprietary data, while cloud offers benefits including quicker implementation – albeit at the cost of increased cloud spending, a sensitive issue in many enterprises. On-prem also helps address the concern of the more than three-quarters of Omdia respondents who cited security and data sovereignty as major issues for AI deployments.
Omdia also validated Cisco’s public statements about AI driving large-scale network infrastructure loads – and therefore spending: Two-thirds of enterprises said their network is already an AI bottleneck or will be within a year. In addition, over half of enterprises said their servers are already experiencing bottlenecks as well. And the required new investment won’t be trivial: Omdia found 63% of enterprises expect network infrastructure spending to increase at least 26% in the next 12 months due to AI; more than one-third see increases of 50% of more. When it comes to server infrastructure spending, about two-thirds anticipate increases of at least 26%.
For a large global enterprise, a 26% increase on infrastructure spend in just one year is a massive resource investment. At some point, investment in AI capabilities is going to clash with the cost of being able to support those functions on the infrastructure. The two will likely impact each other, with infrastructure constraints slowing AI deployment, while AI deployment drives upward pressure on infrastructure budgets.
It’ll be interesting to see how this dynamic affects business leaders’ attitudes toward AI. When voice and video were being deployed over IP networks in the early 2000s, there was little of the ambient social pressure to deploy the new technology at scale, as we’ve seen at least among business leaders when it comes to AI. IT leaders could stage VOIP rollout to coordinate with infrastructure upgrades. In the era of AI, they may find this balance more difficult (and expensive) to strike.
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