You’re accountable for AI you can’t fully see
Before you change tools or rewrite procedures, you need to know what is already happening. In most engineering organizations, AI use runs ahead of the rules: some teams use approved tools; others use personal accounts; code and documents are uploaded to public AI tools; and AI-written code reaches production without anyone tracking it.
Data Exposure
Which code, documents, and customer data go into prompts today, and where that conflicts with your security and data policies.
SDLC Fit
How your current software development lifecycle fits once AI is part of the work, and which stages, from planning to release, need to change.
Team Readiness
Which teams are ready to scale their AI use and which need coaching first, based on which tools your developers actually use and how.
A structured review
Week 1, Lifecycle and policies
We work with your IT and engineering leaders to understand your current lifecycle, your security and data policies, and the rules you answer to. This is the groundwork, and it takes real time to get right.
Week 2, How teams actually work
We go into your teams to see how they build today, which AI tools they use, and where their real ways of working differ from what's written down.
Week 3, Gap analysis
We line up what's actually happening against your lifecycle, your policies, and the rules you answer to, and mark where the gaps are.
Week 4, Leadership workshop
You and your engineering leadership get the findings in a working session, sorted into quick fixes and longer pieces of work.
A smaller engineering organization can run in two to three weeks. We agree the exact scope and timeline with you before we start.
A sequenced plan for your engineering teams
AI usage map
Where AI is used across your teams today, on which tools and accounts, and where it isn't used at all. Where we can measure it, we also show token usage and where it can be optimized.
Risk and gap report
Where current AI use conflicts with your policies or regulatory obligations, ranked by exposure.
Target lifecycle outline
A first view of how each step of your software development lifecycle changes with AI in the work.
Prioritized action plan
Quick fixes your team can make this quarter, separated from the procedure and coaching work that follows.
Next steps for your engineering teams
Write the rules
Turn the findings into procedures your teams and auditors can follow, through the AI SDLC Playbook & SOPs.
Change how teams work
Bring an AI Engineer Coach into the teams that are ready to scale.