GitLab’s AI push reshapes teams, management, and workflows across enterprise software

GitLab’s restructuring is not just a headcount story

For a monday.com product manager or engineer, the real signal in GitLab’s latest move is not the voluntary separation window. It is the operating model: fewer handoffs, fewer layers, smaller teams, and AI doing more of the coordination work that used to sit between people and shipping.

Bill Staples said GitLab is beginning a restructuring process openly and plans to reduce the number of countries with small teams by up to 30%, flatten parts of the organization by removing up to three layers of management, and reorganize R&D into roughly 60 smaller teams with end-to-end ownership. He also said the company is rewiring internal processes with AI agents to automate reviews, approvals, and handoffs, while reaffirming Q1 and full-year FY27 guidance. His framing was blunt: the agentic era is the largest opportunity in GitLab’s history, and “software will be built by machines, directed by people.”

That line matters because it shows how fast AI has moved from feature language to org-design language. A year or two ago, many SaaS companies talked about copilots and workflow suggestions. Now competitors are talking about span of control, management depth, and how much internal coordination can be delegated to agents without breaking accountability.

Why this matters inside monday.com

monday.com has already moved in the same direction on the product side. In 2025, the company said its AI vision would center on AI Blocks, Product Power-ups, and the Digital Workforce. It then pushed further, saying it was moving from “work management” to “work execution” with monday magic, monday vibe, and monday sidekick.

That shift is more than branding. monday sidekick is described as a personalized, context-aware digital worker, which puts monday.com in the middle of the same debate GitLab is now surfacing internally: what happens when the software does not just help organize work, but starts carrying out pieces of it?

The company’s scale makes the stakes obvious. monday.com said it had more than 250,000 customers as of December 31, 2025, 4,547 customers with more than $50,000 in annual recurring revenue as of March 31, 2026, and a net dollar retention rate of 110% as of March 31, 2026. It also said it had 3,211 employees as of that date. In the first quarter of 2026, revenue reached $351.3 million, up 24% year over year, with record GAAP and non-GAAP operating income and record net adds of customers with more than $500,000 in ARR.

Those numbers point to a company that is no longer testing AI at the margins. It is trying to use AI to deepen usage, accelerate expansion, and keep growth efficient as the market gets more skeptical about traditional software layers. That is the same pressure GitLab is responding to from a different angle.

The roles that gain leverage, and the ones that get squeezed

GitLab’s plan is a useful map for where influence is likely to move across enterprise software. The people who gain power are the ones closest to decisions that travel across product, engineering, and revenue. That means product managers who can define outcomes instead of just writing requirements, engineers who can own a system end to end, and go-to-market leaders who can translate AI into measurable time savings or revenue lift.

The people most exposed are the ones whose work is mainly coordination. If AI agents can handle reviews, approvals, and handoffs, then the value of extra management layers drops quickly. So does the value of internal roles that exist mainly to move information from one queue to another. GitLab’s move to cut up to three layers in some functions is a warning shot to any SaaS company still running on dense approval chains.

For monday.com employees, that does not automatically mean downsizing. It does mean a revaluation of what good looks like. Sales can no longer rely on a generic AI pitch. Product needs to prove that automation improves customer outcomes, not just engagement. Engineering has to build systems that are observable and safe enough to let agents take on real work. And managers who survive the flattening trend will likely be the ones who can coach judgment, not just track status.

What to do if your team is being redesigned around AI

The cleanest career move in this environment is to become indispensable at the seam where human judgment meets machine execution. That usually means stepping closer to ownership, measurement, and customer impact.

• Own a workflow end to end, not just a feature inside it.

• Learn how agents fail, not just how they work when everything goes right.

• Make ROI visible. In a company with 110% net dollar retention and customers spread across a large base, the people who can connect AI to expansion and retention will matter more.

• Become fluent in both the product and the process. monday.com’s shift from work management to work execution only works if teams can show how the workflow changes in practice.

• Treat fewer handoffs as a design goal. The companies winning this phase are reducing friction between intent and execution, not layering on more reporting.

The broader context makes the direction hard to ignore. monday.com said it reached $1 billion in ARR in 2024, about a decade after launching Work OS and eight years after hitting $1 million in ARR. It also said monday vibe was the fastest product to surpass $1 million in ARR in company history, and that customers with more than $50,000 in ARR represented 41% of total ARR at year-end 2025. That is what a mature SaaS platform looks like when it tries to keep growing: more product surface area, more AI ambition, and more pressure to make every layer of the company work harder.

GitLab is showing one version of the future. monday.com is building another. In both cases, the real competition is no longer just between products. It is between operating models, and between the companies that can combine human judgment with agentic execution without losing control.