Enterprise AI Adoption in Infrastructure Development

Across the energy, real estate and data center sectors, executive teams are confronting a practical question: how should AI fit into the development process?

What is becoming clear is that there is no single correct model. Different firms are experimenting with different paths depending on their culture, risk tolerance, internal capabilities and speed of decision making.

Across the industry, three broad approaches are emerging.

1. The Opportunistic Approach: Team-Level AI Experiments

In many organizations, AI adoption begins informally.

A site origination lead uses an AI tool to compare development costs across several potential sites. A permitting lead uses it to review planning commission minutes and flag projects that faced opposition. A project manager experiments with it to summarize contractor bids or draft internal reports.

These use cases spread quickly. Teams discover tools that save time and begin integrating them into their daily work.

The advantage of this approach is speed. Teams can move immediately and begin improving productivity without waiting for formal approval or long technology evaluations.

The trade-off is fragmentation. Different teams adopt different tools, often without coordination. Leadership may have limited visibility into where AI is being used or how data is flowing through those systems. Over time, organizations can end up with a patchwork of tools that are difficult to manage centrally.

2. The Centralized Strategy: Enterprise AI Programs

Some companies take a more structured approach.

Leadership establishes an internal AI initiative, often led by a task force or innovation team. The group maps existing workflows, evaluates vendors and develops guidelines for how AI tools should be deployed.

This model allows companies to maintain stronger oversight. Security policies, procurement standards and data governance can all be incorporated from the beginning.

However, this approach often moves more slowly. AI technology is evolving rapidly and internal approval processes can take months. By the time tools are fully evaluated and deployed, the landscape may have changed.

Adoption can also be uneven. If the tools selected by a central team do not match how teams actually work, employees may continue experimenting with their own approaches in parallel, recreating the same fragmentation the program was meant to avoid.

3. The Builder Model: Training Teams to Create Their Own Agents

A third model is beginning to appear as tools such as Claude Cowork make it easier for non-engineers to build AI workflows.

Instead of relying primarily on external platforms or centralized programs, companies train their teams to build their own AI agents.

In infrastructure development firms, this can be powerful. The people closest to the work often understand the problems best. A site origination team understands how to screen land. A permitting specialist understands which regulatory records matter.

When these domain experts are able to build AI agents themselves, tools can be created that are tightly aligned with real workflows.

But this approach also has limitations.

Not every employee wants to become a builder. Training takes time. Agents built by different individuals may vary widely in quality.

How PermitPal Fits

At PermitPal, we see companies adopting AI through all three of these models.

Some teams want ready-to-use agents that can immediately be used to replace manual work. Clara, our AI permitting agent, is designed for exactly this use case, helping development teams quickly understand the permitting and regulatory landscape for a potential project.

Others want custom-built agents designed around their specific development workflows. In those cases, at PermitPal, we begin by mapping the organization’s operating process. How are sites screened? What documents are reviewed during early diligence? Where do teams spend the most time gathering information? From there, we design agents that support those specific steps in the development process.

And some organizations are training their teams to build their own agents. At PermitPal, we run hands-on workshops that teach development, site selection and permitting teams how to design simple agents using tools like Claude. The goal is to help domain experts build practical workflows for day-to-day tasks without needing engineering support.

Infrastructure development is a highly specialized industry. The most effective AI systems combine technical capability with a deep understanding of the people and processes that move projects from site selection to approval.

If your team is exploring how AI could support site screening, permitting research or regulatory diligence, we would be happy to share what we are seeing across the industry.

Email us at emily@permitpal.ai if you would like to discuss how AI agents could fit into your development workflow.