- AI infrastructure growth is increasingly constrained by skilled labor, according to executives at Schneider Electric and Iron Mountain
- Data center operators are expanding partnerships with colleges, trade schools and veterans programs
- Growing community concerns are putting sustainability, water use and power consumption under the spotlight
As AI infrastructure deployments accelerate, data center operators are confronting an unexpected constraint: a shortage of skilled workers.
Data center operators face shortages of electricians, mechanical engineers, cooling specialists and technicians needed to build, commission and maintain facilities. The workforce challenge is emerging alongside well-publicized constraints around power availability and supply chains.
Schneider Electric, a major supplier of data center power, cooling and infrastructure management technologies, says the pace of AI-driven construction is outstripping the industry's ability to develop skilled workers.
"The number of trained people — that's a big constraint right now globally," said Rob Bunger, Schneider Electric's global director of data center solution architecture, in an interview on the sidelines of the AMD Advancing AI 2026 conference in San Francisco this month.
Iron Mountain, which operates roughly 30 data centers globally, reports similar dynamics.
"We don't have a talent development system that matches the increase in needed skills," said Mark Kidd, EVP and GM of Iron Mountain's data center and asset lifecycle management businesses.
The labor shortage presents a counterpoint to a common narrative that AI will broadly eliminate jobs. And Schneider Electric and Iron Mountain are not alone presenting that narrative in a skeptical light. In a report entitled "AI Will Reshape More Jobs Than It Replaces", Boston Consulting Group estimated 50% to 55% of U.S. jobs will be transformed over the next several years while a much smaller percentage faces outright elimination. The report argues that workforce development and reskilling will be essential to realizing AI's benefits.
In data centers, the demand is not centered on knowledge-worker positions. Instead, the fastest-growing needs are in skilled trades and hands-on technical roles, including electrical and mechanical personnel responsible for cooling systems, power distribution and facility operations, as well as technicians who rack, stack, cable and maintain IT equipment, Kidd said.
Workforce pipelines become strategic priority
The hiring challenge is pushing vendors and operators into workforce development efforts.
Schneider Electric is partnering with community colleges and other training institutions to develop workers capable of supporting AI infrastructure deployments. The company has also participated in industry programs targeting veterans and other groups as potential talent pipelines.
Iron Mountain sees similar efforts emerging across the sector.
"That's why you see a lot of folks really starting to work with universities to actually develop the right technical skills so that folks can do those jobs," Kidd said.
The workforce issue adds momentum to the growing emphasis on standardization and prefabrication. Modular designs can reduce on-site labor requirements while speeding deployments, Bunger said. "The more standard that we can get, the easier it is for the workforce to go install and maintain," he said.
That thinking underpins efforts such as Schneider Electric's recently announced collaboration with AMD on a validated Helios AI infrastructure blueprint. The companies said the reference architecture is designed to accelerate deployment of high-density AI facilities supporting rack densities up to 246 kW while reducing implementation risk and complexity. The design covers power, cooling, facility layout and lifecycle management.
Community concerns move to the forefront
As AI-driven data center construction accelerates, operators are also facing rising scrutiny from local communities concerned about energy use, water consumption and land development.
To address those concerns, the industry needs to engage more directly with residents and local stakeholders, Kidd said. Conversations around zoning, water consumption, tax revenue and jobs are increasingly important.
Successful projects increasingly depend on education and transparency rather than assuming communities will automatically support new development, Kidd said.
Sustainability debate becomes more complicated
The industry's sustainability narrative has also become more contentious as AI workloads drive unprecedented power demand.
Public discussion often overlooks efficiency gains achieved over the past decade, including improvements in power usage effectiveness, renewable energy procurement and cooling technologies, Bunger said.
One area frequently misunderstood is liquid cooling. Bunger argued that liquid cooling can reduce water consumption compared with some traditional cooling approaches. "The more liquid cooling that you have in the data center, the less water or energy you have to use to reject that heat outside."
Because liquid-cooled AI systems can operate at higher temperatures than traditional air-cooled environments, they can often reduce cooling-related overhead and improve efficiency, Bunger said.
The broader challenge for operators is balancing explosive AI demand with public concerns about energy consumption, water use and environmental impact.
What's becoming clear is that the industry's future will depend on more than securing power and GPUs. The ability to recruit, train and retain skilled workers may prove equally critical in determining how quickly AI infrastructure can scale.
Read more about skills shortages on Fierce Network:
APAC telcos lead AI shift despite data and skills constraints
Schneider Electric and AMD release Helios reference design for 246 kW AI data center racks
Meta is training up a new fiber workforce for its data center boom
GPUs drive need to address shocking data center safety issue
The labor shortage is changing the way data center networks are built