AI Coding ROI Audit
A fixed-scope 4-week diagnostic for engineering organisations already using AI coding tools and needing evidence on whether adoption is improving delivery, creating review drag, increasing governance risk, or wasting spend.
Questions the audit answers
Delivery
- Is AI improving lead time or only increasing code volume?
- Where are PR review queues, CI/CD, testing, or release controls absorbing the gain?
Governance and security
- Are AI-assisted workflows covered by enforceable code, data, and security controls?
- What would regulated buyers, auditors, or legal teams challenge first?
Cost
- Where are model choice, context size, agent loops, or tooling licences wasting spend?
- Can leadership attribute AI cost to useful engineering work?
Decision
- What should the organisation scale, fix, or stop before renewal or expansion?
- Which 30/90-day actions create the most retained value?
What you receive
Executive summary
CTO / VP Engineering view of evidence, risks, and recommended decisions.
Review bottleneck map
DORA baseline, PR flow, queue depth, reviewer load, and release friction.
Governance gap register
Policy, secure usage, quality gates, data handling, and auditability findings.
Inference waste snapshot
Model routing, context usage, agent loops, caching, and cost attribution opportunities.
30/90-day roadmap
Prioritised quick wins and structural fixes tied to measurable delivery outcomes.
Scale / fix / stop view
A leadership-ready recommendation for renewals, expansion, or remediation.
Process and expected inputs
Baseline
Tooling, DORA, PR flow, team structure, AI usage, and governance context.
Bottlenecks
Review queues, testing, release, security controls, and delivery constraints.
Risk and cost
Policy, quality gates, model selection, agent loops, and spend attribution.
Decision pack
Executive summary, findings, roadmap, and scale / fix / stop recommendation.
Fit criteria
Good fit
- AI coding tools are already deployed or expanding.
- Delivery metrics are not moving as clearly as adoption metrics.
- Leadership needs evidence before renewal, audit, due diligence, or board reporting.
Not a fit
- You need generic AI training or prompt workshops.
- You have not deployed AI coding tools yet.
- You want implementation before diagnosis or body-shopping.
Why this work is specific
Matt Drankowski architected and led a GitHub Copilot rollout across a 7,000-engineer Fortune 100 organisation, including governance, delivery measurement, and production operating change. The audit combines platform engineering, DevOps, DevSecOps, FinOps, and AI-assisted delivery design because the ROI problem sits across all of them.