Practically every enterprise is seeking to figure out where AI fits in – and how the tech will augment its workforce.

Data published last week by Hiring Lab, Indeed’s economic research arm, found that generative AI roles have grown rapidly in recent years, with many companies specifically seeking consultants with expertise in the technology. Management consultant roles accounted for 12.4% of all generative AI roles as of January 2025, up from 0.2% in January 2024.

That’s more than double the percentage of roles for machine learning engineers, software architects and data scientists, which each made up between 4.4% and 5.4% of all generative AI job postings.

Many businesses understand the transformative potential of AI, but have little idea where to start, said Larry Kodali, CEO and founder of AI hiring firm OptimHire.

• “At every step, they’re trying to see how they can optimize with AI, how they can improve efficiency, where they can plug in different AI elements and engines into it,” said Kodali. “We are in this phase of investigation.”
• Rather than hire teams to help them integrate AI or build models from the ground up, many enterprises are opting for consultants to come up with the “blueprint” for where it works best, he said.

If your enterprise is lost amid the overwhelming and rapid progression of this tech, hiring an in-house team to work on AI may not be the right place to start, said Kodali, and could potentially add to already-distended AI budgets. Outsourcing AI integrations to consultants could help businesses hone in on operations to figure out where AI optimization will go farthest.

“To be honest, almost every company should hire them,” said Kodali. “There is a lot of process optimization that can be done at every step of the way. It’s good to hire multiple consultants for different sections of the workflow process.”

And in this market, businesses might be hard pressed to find excellent AI talent looking to go in-house, said Kodali. “There’s a lot of entrepreneurship happening,” he said. “If you’re really talented, there are too many opportunities for you right now.”

But AI isn’t a set-it-and-forget-it kind of technology. Once the groundwork is laid by the consultant during the so-called investigation phase, Kodali said, it’s time to start hiring and upskilling your workforce.

As AI confusion and uncertainty hampers adoption, training and hiring staff with generative AI expertise can’t be ignored: A December study by enterprise software firm Slack of more than 17,000 workers found that while 76% said they wanted to become competent at using AI, only 7% considered themselves expert users. Some 30% of workers say they’ve had no AI training at all.

“People would like to hire consultants on a temporary basis – get them in, improve their optimization,” said Kodali. “Once these processes are added into the system, then they will probably try to hire people who know how to use GenAI.”

It’s no secret that data centers are power hogs. Going nuclear might be part of the solution.

Small modular nuclear reactors, or reactors that are significantly smaller than your average nuclear power plant, could provide relief for the stress that data center demand poses on an already-weary power grid. But given that only three of these reactors are actually in operation, scaling them is slow-going, and likely won’t be commercially viable for at least five years.

In the meantime, microgrids could offer support to ease the burden on the grid without slowing down AI growth, said Michael Stadler, chief technology officer of Xendee.

“It’s really important to start thinking about what we can do now about this power problem,” said Stadler. “We are thinking about what could happen in five years or 10 years, but I think we have to start acting now.”

Microgrids connect a patchwork of distributed energy resources, such as generators, batteries or solar, that operate within a set boundary to power a single facility. These systems can be connected to the power grid or act in an isolated “island mode,” said Stadler.

While microgrids have been around for decades, they’re garnering more attention as AI and data centers increase pressure on utilities: Goldman Sachs Research predicts that global power demand from data centers could jump 150% by 2027.

• In the context of a data center, a microgrid would simply expand on the generators that many data centers already have for backup power. Using a microgrid could allow data centers to harness renewable power, battery storage and grid power depending on immediate needs, reducing emissions and costs, said Stadler.
• “This is now why we are getting into the data center space, because data centers have high power demand, and utilities are really far behind in upgrading the system,” said Stadler. “So why not install something on-site?”

And how does nuclear come into the picture? Because these microgrids are made up of several different interconnected energy sources, once small modular reactors are available, these grids could be augmented to use them, said Stadler.

A recent white paper by Xendee performed cost modeling for potential microgrids on data centers in Santa Clara, California, and Ashburn, North Carolina. The paper forecasted that implementing microgrids that give way to small modular reactors by 2035 could result in operational expenditure savings of nearly 80% and 60%, respectively.

“It’s basically a stepwise approach which allows you to adapt to changes in the future,” he said.

Data centers are only getting more complex. IBM is seeking ways to make upkeep easier.

The tech firm filed a patent application for “data center guide creation and cost estimation” using augmented reality headsets to display a “guidance script” and monitor the user during data-center maintenance, watching for mistakes, deviations and total time spent on the task. Before the task starts, the system would develop an estimated maintenance cost based on things like labor and resources, then adjust those costs based on user activity data collected from the headset.

IBM’s patent application adds to several inventive solutions for data center maintenance and architecture that we’ve seen in filings. For instance, Nvidia previously sought to patent a system for self-driving, robotic server rack components, and Google filed a patent application for drones that monitor these facilities with aerial imaging and environmental sensing.

Amid the pressure to build bigger and better AI, data center demand and growth has taken center stage. And costs for these facilities are skyrocketing

• Gartner predicts data center costs to grow more than 23% in 2025, largely as a result of AI upgrades.
• Google and Microsoft have pledged $75 billion and $80 billion, respectively, to AI data centers, and data-center expansion is part of Apple’s $500 billion AI push.

With so much pressure and money going into the data center ecosystem right now, the tech in these patent filings could limit human error in individual facilities as much as possible. They give the applicants a leg up in designing the future of the data center, no matter how outlandish.