# How LinearB empowers Kraken to operationalize AI at scale

> Kraken's engineering operations team uses LinearB as the data layer that turns AI experimentation into production results, and surfaces the bottlenecks slowing delivery down.

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# How LinearB empowers Kraken to operationalize AI at scale

Kraken's engineering operations team uses LinearB as the data layer that turns AI experimentation into production results, and surfaces the bottlenecks slowing delivery down.

## Industry

Financial Services

## Country

United States

## Company size

Enterprise

[Play video](https://www.youtube.com/watch?v=A6MTGtC90rE)

## Introduction

Kraken moves fast. The team prizes a lean, startup pace, and AI has put that speed in everyone's hands, including non-engineers. But shipping AI-powered ideas from pilot to production, and proving the spend pays off, is where most teams stall.

"Everyone at Kraken is using AI right now, whether we like it or not. It's been a complete game-changer for software engineering, but it's a partner, a tool, not a replacement."

![Photo of Nick Sudan](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/00fa26786970a655690b2979be83ccf71f38162f_ad758b10b5?_a=BAVMn6ID0)

### Nick Sudan

Engineering Operations Lead, Kraken

## Measuring whether AI is actually working

Adoption dashboards and seat counts look great, but they don't tell you whether the work got better. Kraken balances throughput, quality, and stability instead, and watches cost per contribution, or AI spend divided by impact. LinearB provides the high-level signals that anchor the picture, such as merge frequency, PR size, rework rate, and cycle time.  

"AI usage isn't a metric that's great for effectiveness. It's one signal across many, but by itself it doesn't prove anything."

## Finding the real bottleneck

Review time is the biggest constraint on cycle time at Kraken, and at most companies. To find out why, the team pairs the LinearB MCP server with their git provider data and lets AI move between the two. LinearB surfaces the trend and the relevant merge requests, repositories, and teams; the git layer fills in the detail.

"We assumed big, complex merge requests were slowing review time down, that's what most people say. But we joined up the data, and size and complexity accounted for maybe 5 to 10% of the slowdown. Seventy to eighty percent came from time zone differences. That's the hidden bottleneck these silos were masking. Once we could prove it, it stopped being a hunch and became a real, data-backed problem leadership could act on."

## The results

The fix, expanding the pool of code owners so a review never waits on a single time zone, came straight from the data. The bigger shift is cultural. Leaders now work from evidence instead of anecdote, and engineers and non-technical stakeholders share one language for the health of the org.

"If you're not measuring AI effectiveness, adoption, and cost, and relating that to the quantitative engineering data like code throughput and DORA, then it's all worthless. And this has to happen on day one."

### Nick Sudan

Engineering Operations Lead, Kraken

Improve developer productivity with LinearB 

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