Agentic AI and robotics are among the most transformative opportunities for efficiency gains in modern manufacturing. But as automation scales, it raises a question that may not be top of mind for manufacturers: What are the energy costs?
According to a recent PwC survey, 81% of executives plan to increase artificial intelligence investments over the next three years, and 93% "believe America’s industrial advantage will be built on intelligent systems.” Moreover, 46% of energy and industrial professionals report that their firms are investing in renewable energy generation and storage, with roughly one-third expecting to achieve energy independence by 2030.
Meanwhile, agentic AI and robotics are becoming safer, smarter and more affordable. This comes as the manufacturing workforce faces an accelerating talent shortage driven by an aging labor pool and loss of institutional knowledge.
For companies in the early stages of AI and robotics deployment, energy costs can appear almost negligible.
Agility Robotics, which has deployed its humanoid robot Digit for clients including Amazon, GXO and Toyota Motor Manufacturing Canada, estimates the power cost runs about $1 per shift.
"The real complexity in physical automation isn't power — it's integrating into real-world workflows, safety systems, and operations on the floor," said Jonathan Hurst, cofounder and chief robot officer, via email.
That framing resonates with manufacturers newer to automation. But there may be another reality to consider for those that are looking to scale up their agentic AI or robotics quickly: increased energy use.
“As you scale up and these things become a bigger part of your production, it might be significantly more,” said Jackie Bakalarski, a principal focused on sustainability and ESG at Avetta.
Bakalarski recommends thinking through if low energy figures will still apply after factoring in your specific considerations.
"Once you normalize that cost across the infrastructure, the building, running your own smaller power plants, etc. That's more than probably a dollar a shift over time.”
Energy is no longer linear with output
Perhaps the most important insight for manufacturers is that the traditional relationship between energy consumption and production output has fundamentally shifted.
There was a time when energy would increase alongside productivity. That may no longer be the case, according to Bakalarski .
"Energy demand used to be very indexed to output," she said. "Now those things have shifted to a degree. So your output can go up a lot, while your energy demand only goes up maybe marginally.”
That decoupling is part of why some feel agentic AI is a compelling option for manufacturers.
"My personal goal is to bring at least 30% productivity of an employee,” said Anoop Mohan, chief product and technology officer at Augury. "And if we can do that, that's for me the role of what we call AI agents."
Augury's Mohan describes a world in which AI agents work continuously alongside reliability engineers, maintenance teams and operations staff. These agents can interpret sensor data, flag potential failures before they occur and recommend actions in real time. The results are less unplanned downtime and smoother production runs, which in turn carry an energy efficiency dividend: machines running at optimal performance draw less power than those operating in a degraded state awaiting repair.
Planning power as a parallel track
Where companies stumble, Bakalarski notes, is in failing to plan energy infrastructure alongside production changes.
"If you're planning to change your production, and you're adding robotics or going to be doing something that creates a bigger energy pool, you need to be planning your energy.”
Automotive manufacturers have historically led in this discipline by modeling production environments digitally — including energy load sequencing — before committing changes to the factory floor. As the AI industry evolves, manufacturing companies increasingly compete for energy resources with data centers, according to a recent AlphaStruxure survey. Risks resulting from resource constraints include energy shortages and even delays in opening new sites when energy is unavailable to support the facility.
Another factor is the promise of alternative sources of energy.
A recent Deloitte Energy and Industrials report, which focused on a broad range of sectors such as cleantech manufacturing, data centers and AI, said “renewables are in a race with other clean generation options to fill the resource gap.” A possible advantage of renewables is that they may help boost overall energy availability and eventually lower cost.
With all of these factors in mind, experts say the manufacturers best positioned to capture the productivity promise of agentic AI and robotics are those treating energy not as an afterthought but as a core input in the investment decision from day one.
