On July 22, a seven-month-old startup called Arrakis came out of stealth with $38 million in funding. Blossom Capital led the round. Accel participated. The Datadog CEO put in personal money. So did OpenAI’s head of business products.

In 2026, Arrakis is pointing its AI agents at aerospace, energy, logistics, and manufacturing: the 70 percent of the economy that, in its account, has received far less AI investment than desk work.

CEO Rafael Quintanilla told Fortune the thesis plainly: “Most AI investment to date has targeted the 30% of workers behind a desk. The real ROI lies in the 70% running industrial operations.”

His argument is worth examining through the way Arrakis deploys its agents.

The Spreadsheet That Already Existed

Here is how Arrakis landed one of its first customers, a publicly traded shipping company in New York.

The goal was cash-flow visibility. The company had monthly snapshots. Arrakis was hired to get that to daily. The engineering approach most AI companies would take: build a new dashboard, connect a dozen APIs, deploy a custom model, train the team on new software.

Arrakis did something different. They rebuilt the spreadsheet the operators already used. Same layout. Same columns. Same workflow. The only change was that AI populated the data instead of a person manually pulling it from three systems. As operators corrected entries, the system learned their logic and started codifying their knowledge.

The operators kept their familiar interface and routine, opening the same file they opened yesterday with the data already populated.

That design choice gave operators a familiar way to use the technology and reduced the effort needed to adopt it.

Why Complexity Keeps Losing

Arrakis reported a 90 percent reduction in procurement cycle times for its customers. Its deployment approach focuses on removing friction between the AI and the person doing the work.

This pattern keeps showing up, and most organizations keep missing it.

The instinct when deploying AI is to build something new. New platform. New workflow. New training program. New role to manage it all. Every layer adds cost, adds timeline, and adds one more reason for the people who actually do the work to not use it.

The companies getting results are finding the existing workflow, the spreadsheet or checklist or routing process that someone already does forty times a week, and making AI do the manual part of it. They keep the familiar process while removing the repetitive effort.

Quintanilla’s playbook starts at headquarters, proves the value with one team, then expands to field operations when people on the ground ask for it after seeing what it did for the first group.

Evidence gives the next team a reason to try it, with a familiar workflow keeping the barrier to entry low.

The Model-Agnostic Bet

Arrakis is also model-agnostic. They start with OpenAI or Anthropic, then shift customers to open-source alternatives and cut token costs by roughly 70 percent. One Swiss executive told Quintanilla: “Everyone told us we had to be on Copilot. Then we went to OpenAI. Now it’s Anthropic. My head is going like this.”

That executive’s frustration captures the cost of repeatedly reconsidering a model choice. Treat the model as an input you can change, and judge each deployment by how well it fits the team’s work.

The organizations spending months evaluating which model to standardize on are solving the wrong problem. The organizations deploying in weeks by plugging AI into existing workflows are getting the results.

What This Means for Your Team

Arrakis has five customers. It is six months old. Its ability to scale across industries remains unproven. The broader pattern has appeared across dozens of companies and sectors.

Make the deployment accessible: understandable to a new hire in an hour and maintainable by the team that uses it. A familiar, almost boring system can deliver considerable value.

If your AI initiative requires a change management program to get people to use it, check whether it solves a problem the team actually asked you to solve.

Start with the simplest useful change, prove it works, and let the results give the next team a reason to adopt it.

Arrakis’s spreadsheet example gives that approach a concrete starting point.