Dive Brief:
- Global AI spend is forecasted to total $2.7 trillion this year, representing a 49.5% increase year over year, as demand for AI infrastructure remains strong, according to a Gartner report published Wednesday.
- Tech providers represent the largest percentage of AI spend at 35% as they purchase AI-optimized servers, IaaS and chips that are needed to build, deploy and run AI models and agents, John-David Lovelock, distinguished VP analyst at Gartner, told CIO Dive.
- “The amount of money that is going into AI infrastructure — the chips that are being made, the servers that are being bought, the data centers that are being built, the power, utilities, cooling that are going along with it — represent the largest infrastructure project humanity has ever undertaken,” Lovelock said.
Dive Insight:
As AI spend increases, the technology is becoming a cost CIOs can’t always avoid. While companies are intentionally buying some AI products and features, other products come pre-embedded with the technology.
“There’s a bit of rebranding of the CIO’s dollars to be AI,” Lovelock said. “They’re not intentionally going out and buying it, they just can’t get out of the way easily.”
Amid enterprise adoption efforts, tech providers are moving full steam ahead with integrating AI into products and services.
Salesforce on Tuesday unveiled AIforce, an interface layer for its agentic enterprise, that combines model intelligence with the CRM company’s data context to work with core enterprise platforms. Earlier this month, Broadcom introduced the VMware Private AI Cloud and its software-defined foundation VMware AI Factory, which automates infrastructure deployment capabilities.
The foundation is being built for the new era of intelligence, and enterprises are already starting to bear the price, Lovelock said. Memory costs are rising as tech providers snap up AI-optimized servers, he said. Meanwhile, software is becoming more expensive as providers add AI capabilities.
“It is hitting the CIO in areas that they choose — I’m going to do an AI project, I’m going to look at agents and do automation, I’m going to look at large language models to improve quality, consistency, reliability and process — but they’re also getting it in areas where they don’t intend to or want to,” Lovelock said.
Though tech providers are creating new waves of AI as they take on the infrastructure work, CIOs can still start projects at their own pace, especially as difficulties measuring and demonstrating AI’s value mount, he added.
CIOs are more often adopting smaller, incremental AI use cases rather than taking on larger scale AI projects as a result, he said.
Leaders should start “building up the muscle memory and the skills for bringing AI from a concept — bringing AI from the lowest level of expectations — to building value,” Lovelock said.
