Do organizations reorganize teams around AI?

“We are refining our operating model using a jobs to be done framework, mapping critical work to a combination of highly focused teams and increasingly capable AI agents. Focused teams aligned to top strategic priorities are allowing us to move faster, execute more efficiently, and better serve our community, partners, and advertisers.”

SnapPublished by the organization itself (regulatory filing), no named author

“normally that would have taken 40 or 50 people about a year to do. And we took five really smart people, AI forward thinking people building on agentic coding tools. And those five people rebuilt it in 65 days.”

AmazonPublished by the organization itself (earnings call), no named author

“What the working model that we've used for all of our internal agent development is basically a tripod. It's a GTM engineer, a data scientist, and then the best person, the best subject matter expert in that function. And they sit down shoulder to shoulder and document the best practice for that particular function and then encode that in workflows that ultimately form an agent. We then keep a human in the loop so that human will basically QA the agent. The agent won't be allowed to autonomously execute its action. And then over time as the subject matter expert has less feedback for the agent, we finally pull that person.”

Jeanne DeWitt GrosserChief Operating Officer, Vercel · Executive, Vercel

“And sometime last year, we knew, or I say we, but the bedrock team knew that they were going to need to build a new inference data plane. But they had estimated it at 30 people over 18 months. This is a big, big service, and it was … take time to build the new one, migrate customers over, migrate models over, and they decided to take a step back. They took six people and they built it in 76 days with …”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“Normally, this sort of activity might take a team of 40 people about a year to carefully build. Instead, the Bedrock team spun up a separable group of six very skilled engineers who were excited about starting over and building on our agentic coding service (Kiro)”

AmazonPublished by the organization itself (post), no named author

“The only way for us to keep accelerating is to make sure we’re surrounded by people with a broad range of experience… Roughly one-third of my team is experienced, scaled operators who understand how the Cisco engine works. The second third are systems thinkers from the market who bring the outside-in perspective. And the final third is made of entrepreneurial founders, CEOs, and engineers who are AI-native who operate in founder mode.”

Jeetu PatelPresident and Chief Product Officer · Executive, Cisco

“if you align the org chart too much to the individual projects, you're going to end up re-orging every two weeks, which would be a total nightmare. … And there is the concept of a bet lead or a directly responsible individual. But the interesting thing is that they don't manage usually any of the other people, which … breaks the … previous way in which a lot of these things were done. But I think it leads us to be really flexible when you say, OK, actually, this project is not going to work out. Let's disband and keep going, and it's not a big deal.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“But especially in a labs-type group where our whole cadence is two-week reviews where every project goes up for, we call it persevere or pivot. So basically, every project is up for review, and either it's time to keep going, … or it's time to pivot it, or even shut down. And we shut down projects basically every single one of those cycles. And the more you do it, the less it's just like, oh, no, my project is shut down. I failed. It's like, no, that is definitely the intention of the lab team is to prototype quickly, try to ship internally, maybe get it to early access, and if it doesn't work, wind it down.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“In Consulting, AI is both a growth driver and a productivity engine. Our experts are helping clients design and execute AI strategies by leveraging the IBM Consulting Advantage platform, an AI delivery platform designed to implement solutions at scale, transforming how our consultants work. As agents take on more work, delivery becomes faster, more software driven, and more scalable.”

IBMPublished by the organization itself (regulatory filing), no named author

“Workers pick up tasks and are solely responsible for driving them to completion. They're unaware of the larger system. They don't communicate with any other planners or workers. They work on their own copy of the repo, and when done, they write up a single handoff that the system submits to the planner that requested the task.”

Wilson LinResearcher, Cursor (author of the Towards self-driving codebases post) · Practitioner, Cursor (Anysphere)

“I don't know if that's surprising, but it is, you know, as I said, a lot of ex-founders doing a lot of bottoms up, you know, very exciting ideas. You know, for example, decisions API, you know, came together very quickly. We realized like we have a really good model with Luna. We can just do some, you know, constraint sampling and then, you know, have it, you know, shipped as like a different shape to the Responses API. And then, you know, when we do that, you know, it's like, it's faster and it's quite delightful for some cases. And it's just, if you were to like, … the hood, you realize like, okay, there's this … channel that came together. You know, initially, it was like four people kind of hacking on it on the weekend.”

Thibault SottiauxHead of ChatGPT and Codex, OpenAI · Leader, OpenAI