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Are you ready to scale
AI-assisted Coding?

A short, practical information and confidence check for engineering leaders and practioners scaling AI-assisted coding.

Code-generation

Getting the full value from AI-driven coding

Enterprises have already invested in AI coding assistants. But will they realize theirfull, expected productivity? This document will give you context and a simpleself-check on what might be "a missing piece".

 

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What's inside the document

Our product team created a simple context and awareness document to help you reflect on your path to scaling AI coding.

What challenge arise when scaling AI-assisted coding?

As more teams get involved and complexity is growing are the expected productivity gains at risk?

Is there an urgeny for organizations moving from AI coding adoption to scale?

Are the key questions around introducing AI coding essentially the same as those for scaling it?

Is action required?

How to quickly determine the own exposure and discuss it to reach an initial assessment?

Lead Magnet AI Coding-1

AI Coding is everywhere.
Scaling it is the next challenge.

AI coding assistants are now mainstream. Most organizations already use them and see strong productivity gains at the individual or task level.

The next challenge is scaling those gains across teams and projects. But this step is not automatic.

In multi-team, multi-project environments, uncoordinated AI usage can reduce productivity instead of increasing it. Inconsistent outputs, architectural drift, repeated rework, and rising coordination costs can quickly offset early gains.

Analyst insights and multiple developer surveys already point to this emerging risk.

From our perspective

AI-assisted coding can sustain productivity at scale. What it needs is a design-aware add-on.

Frequently Asked Questions

Why is scaling AI Coding a business priority?

Most organizations have already invested in tools and see impressive local productivity gains. The next step is organization-wide adoption to scale the productivity gains.
Who should be concerned with AI Coding at scale?

This perspective is relevant for anyone responsible for scaling AI-assisted software development beyond first experiments, especially:

  • CTOs & Engineering Leaders
    who need AI coding to work reliably across teams, projects and platforms.
  • Heads of Architecture & Principal Engineers
    who want to prevent architectural drift, rework and inconsistent AI output.
  • Platform, Enablement & DevEx Teams
    responsible for standards, tooling, and sustainable developer productivity.
  • Organizations moving from pilots to scale
    where AI coding is no longer an experiment, but part of day-to-day delivery.
What is the purpose of this AI Coding 'Confidence Check'? This document is not an assessment or scoring model. It brings together a small set of focused questions to support reflection on what to consider when moving from selective, focused AI coding usage to enterprise-wide adoption; especially when AI-assisted development needs to work reliably at scale.