Truell said the company had already automated roughly 80% of its customer support tickets with the help of the technology. He said the company had also implemented an internal AI-powered communication system that allows employees to query information across the organization. “We’ve actually done a lot of work internally on customizing that setup,” he said.

Cursor also uses AI for internal communications, he said. “We have a system where folks can ask any question about the company and get it answered by an AI,” Truell said, as well as an project with “a few forward deployed engineers internally embedded throughout, building custom tooling right now for operations, for sales and experimenting,” he said. 

Across the enterprise software landscape, some larger organizations are increasingly coming up against adoption challenges when attempting to integrate AI into workflows. 

Data silos—where information is trapped in disconnected systems—prevent AI tools from accessing the full context they need to be useful, and technical sprawl—the accumulation of disparate tools and platforms over years of growth— can create integration issues. Many organizations are finding they need more dedicated technical expertise to help tailor AI models to specific business needs.

Engineers are seeing productivity gains

Cursor, which is valued at $29.3 billion, said last month it had crossed $1 billion in annualized revenue and now has more than 300 employees. The company has seen rapid growth since it was founded by a team of four MIT graduates in 2022. The company’s AI coding tool, which first launched in 2023, has been popular with software who use it to help both generate and edit code. 

There has been some conflicting research about how helpful AI tools actually are for software engineering. A July 2025 study by the nonprofit research group METR found that experienced developers working on large, mature codebases actually took 19% longer to complete tasks when using AI tools such as Cursor and Claude, despite believing they had worked 20% faster. The researchers attributed the slowdown to time spent prompting AI, waiting for responses, and time reviewing generated code.

A recent study conducted by University of Chicago found that teams using Cursor’s AI coding assistant in large companies merged 39% more pull requests (PRs) compared to non-users. The research also showed that senior developers created more detailed plans before writing code and demonstrated greater skill working with AI agents.

“A lot of folks think that junior developers get the most out of AI,” Truell said. But “when these academics went in and looked at the data, it looked like senior engineers actually were more effective in using the tools and were accepting code at higher rates and were getting more value from that.”

Truell noted that this surprised him as well: “We want to dig into to understand exactly why that’s the case.”