AI Operating Model Sprint

What this typically looks like:

• Developers using AI differently across the same team
• AI-generated code requiring rework during PR review
• QA cycles repeating due to inconsistent outputs
• Some individuals moving faster, but team output unchanged
• No shared understanding of when AI should be used
• Inconsistent results across workflows

AI is present. But it’s not improving delivery in a reliable and predictable way.

In most teams:

• Usage is individual, not standardised
• Workflows are undefined or inconsistent
• Patterns are not shared across developers
• Output quality varies depending on who is using AI

This creates friction:

• More rework during PR review
• Repeated QA cycles
• Inconsistent delivery outcomes

Adding more tools or prompts does not solve this.

This is a focused, fixed-scope engagement designed to:

• Understand current AI usage
• Identify where it should create value
• Define practical workflow patterns
• Establish a clear model for adoption

This is not a generic AI strategy exercise. It is a practical definition of how AI should be used in your environment.

• Defined AI workflow patterns
• Guidance on where AI should be used and where it shouldn’t
• A structured model for consistent usage
• Clarity on what needs to change

Everything is designed to be applied, not documented and ignored.

• Inconsistent PR quality across developers
• Repeated QA issues caused by AI output
• Duplicated effort instead of shared patterns
• Lack of trust in AI-generated work

This is where AI starts improving team performance not just individual output.

Most teams need support in applying what is defined.

This typically leads to:

• Embedding workflows into delivery
• Improving consistency across teams
• Refining usage over time

• Fixed-scope
• Fixed-fee
• Structured engagement