[01]
AI IS BEING USED BUT NOT CONSISTENTLY ACROSS ENGINEERING TEAMS
What this typically looks like in practice:
- developers using AI differently across the same codebase
- AI-generated code requiring rework or validation
- inconsistent quality between developers
- unclear when AI should be trusted and when it shouldn’t
- teams duplicating effort instead of reusing patterns
Some developers move faster.
The team does not.
[02]
THERE IS NO SHARED WAY OF WORKING
In most engineering teams Rokkit200 works with:
- AI usage is individual, not standardised
- workflows are undefined or inconsistent
- patterns are not shared across the team
- outputs vary depending on who is using AI
This creates friction:
- more review cycles
- more rework
- less trust in AI output
Adding more tools or prompts does not solve this.
[03]
DEFINE HOW AI SHOULD ACTUALLY BE USED IN ENGINEERING
This is a focused, fixed-scope sprint that defines:
- how AI is used in code review
- how AI supports QA and testing
- how developers should work with AI in real delivery
- where AI adds value and where it doesn’t
This is not theoretical.
It is built around how your teams actually work.
[04]
WHAT HAPPENS DURING THE SPRINT
During the sprint, Rokkit200 works with your team to:
- map current AI usage across engineering workflows
- identify inconsistencies and friction points
- define clear, repeatable workflow patterns
- align how AI is used across developers
Focus areas typically include:
- code generation and refinement
- pull request review workflows
- QA and testing support
- working with large, complex codebases
[05]
CLEAR, USABLE OUTPUTS
- defined engineering AI workflows
- consistent patterns of use across the team
- guidance on where AI should be trusted
- reduced variation in output and quality
- a structured rollout approach
Everything is designed to be applied immediately in delivery.
[06]
WHERE TEAMS TYPICALLY SEE IMPROVEMENT
- inconsistent PR quality across developers
- repeated QA issues caused by AI output
- faster, more predictable code review cycles
- clearer expectations across developers
- duplicated effort across teams
- improved confidence in using AI
This is where AI starts improving team performance not just individual output.
[07]
THIS CAN BE A STARTING POINT OR A FOCUSED FOLLOW-ON
This sprint can be used:
- as a standalone starting point for engineering teams
- or alongside the AI Operating Model Sprint
It is particularly relevant when:
- AI usage is already present
- engineering teams are feeling the impact
- consistency is the main issue
[08]
A DEFINED, FOCUSED ENGAGEMENT
- fixed scope
- fixed fee
- short, structured sprint
- designed for fast clarity and application
If AI usage varies across your engineering team, start here
Rokkit200 helps you define how AI should actually work in delivery.
Get in contact with us