The “Swiss Army Knife” behind AI-powered solar design

While automation and artificial intelligence can generate and optimize layouts, human expertise remains essential in evaluating risk and constructability.

March 30, 2026 Phoebe Skok

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AI is quickly becoming the workhorse that’s accelerating the solar design process, but despite the technology’s speed and efficiency, it lacks one distinctly human trait: judgment.

While AI models can evaluate the feasibility and profitability of many possible locations for new build and can easily compare across multiple equipment types, locations or timelines, they can’t conduct site visits, talk to landowners or do the type of boots-on-the-ground work that’s often so vital for successful solar design and project deployment.

“Automation can optimize within a given framework, but only humans can decide where tradeoffs make strategic sense and where they introduce unacceptable risk,” explained Andy Lee, a sales manager at design software maker PV Farm. He told pv magazine USA that many AI tools rely on clean digital models and produce their outputs accordingly. In practice, this leaves the “messy real-world buildability” out of the process, which often “creates disconnects between design intent and construction reality” by omitting factors like site access, sequencing, labor and owner priorities

A practiced human design team is unlikely to face the same difficulties. Speed can be vital, particularly at earlier stages of the design process, but rarely is it a strong approach for effective risk evaluation.

Zak Grabowski, a sales engineer at PV Farm, told pv magazine USA that AI and automation software essentially remove the grunt work of the design process. Rather than spending weeks drafting a single project layout, those tools can allow designers to test many scenarios and to do so in a way that incorporates a dynamic supply chain and varied equipment availability.

They can’t, however, replace experience or intuition.

“That speed frees engineers to focus on what still matters most,” Grabowski said, which includes making realistic assumptions, understanding a particular site’s field constraints and balancing benefits and tradeoffs. While speed and automation are incredibly powerful, he noted that they have to earn trust from their users.

The game thus becomes finding a way to “automate the drafting, not the judgment,” he added.

In Grabowski’s eyes, that’s where cross-functional teams that work across disciplines can shine. As larger portions of the process become automated and AI helps surface deep insights, he said, designers benefit from acting like “Swiss Army knives” who can do a bit of everything instead of staying siloed in one part of the discipline.

“The machines generate and validate options, but human judgment remains essential to interpret results, weigh risk and decide which projects should move forward,” Grabowski said.

So, where does software find its main value-add in the solar design process? Accelerating scenario generation and pattern recognition across different terrains and layouts, particularly as AI may create certain pairings a human might not have tried.

“Visual, site-specific modeling turns abstract options into something tangible that everyone can evaluate,” added Brian Raboin, PV Farm’s CEO. He explained that the visual models can act as a type of “conversation currency” between the many actors that shape a solar project’s design. It makes it easier to stress-test different assumptions, he said.

“When developers, EPCs and manufacturers can see terrain, constraints, cut/fill, equipment footprints and yield impacts in a shared model, tradeoffs become clearer and alignment happens faster,” he noted.

At the end of the day, fears that AI and automation will put designers out of work and hyped-up claims that software can design projects itself are both likely overblown.

“Digital tools can over-optimize for what’s easiest to model rather than what matters most in the field, especially if they haven’t been validated against real projects,” Raboin said, as algorithms seek out the clean, quantifiable inputs that rarely exist outside of the digital world. “That’s why teams need a built-in ‘sniff test’ and a validation loop that treats software as decision support rather than decision authority.”

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