Audit Outcomes About Contact Request audit outline
For CTOs, VPs Engineering, AI platform engineering, and DevSecOps leaders in 200+ engineer organisations

If AI coding tools made developers faster
but delivery did not get faster,
this audit shows why.

Led a 7,000-engineer GitHub Copilot AI adoption at Fortune 100 scale — governance frameworks, AI-SDLC measurement, and production delivery change. Fixed-scope audit, validation available under NDA.

A fixed-scope 4-week audit that reveals where AI is inflating PR review load, introducing governance risk, and increasing inference spend — and what to change in the next 30/90 days.

Request the 2-page audit outline See sample audit findings
Interactive ROI Model

Quantify the real value your organisation retains.

Model gross speed gain against AI governance overhead, security debt, and tool licence cost. Board-ready output, live in your browser.

Open calculator
AI Governance Assessment

40‑point UK & US readiness audit.

Covers NIST AI RMF, OWASP LLM Top 10, and the enterprise policy and compliance obligations that matter in 2026.

Start assessment

As trusted by

Fortune 100 · Heavy Industrial
7,000-engineer AI adoption

GitHub Copilot · AI-SDLC Measurement tooling · AI engineering productivity uplift

Fortune 500 · AgTech Enterprise
30% cloud cost reduction

£400k+ annualised savings · AI FinOps · LLM cost attribution

Fortune 500 · Manufacturing
100% engineering standardised

GitHub Enterprise · AI-ready CI/CD · SAST at scale

Global Theatre & Life Sciences
+ 2 enterprise clients

UK & US AI governance · Platform architecture · Cloud FinOps

Client names anonymised due to enterprise confidentiality. Validation available under NDA.

The problem with AI coding adoption

AI adoption is not the same as engineering performance.

Most AI coding deployments optimise for individual developer adoption, not for measurable delivery performance. Here’s what that creates in practice.

The AI productivity gap, visualised

The divergence your AI adoption metrics aren’t showing you

After AI coding tool deployment, individual output and organisational delivery decouple. Most engineering leaders don’t see it until licence renewal.

+55%
Code written per developer / week
↑ accelerating with AI tools
↔
AI-to-
Delivery
Gap
≈ 0%
AI-to-production cycle time improvement
→ stalled at the AI governance bottleneck

What’s happening: AI tools accelerate code writing, but reviews still require your most senior, most bottlenecked engineers. AI-generated PR volume grows, governance queue depth compounds, and your AI licence cost scales monthly against flat delivery metrics. More generated code does not mean faster delivery. Without the right AI governance guardrails, AI coding tools amplify inconsistency, rework, and governance pressure.

More generated code does not automatically mean faster delivery

Delivery Bottleneck

AI coding tools increase AI code review load and change size. AI code governance, testing assurance, and release control still require your most senior engineers. AI-to-production cycle time stays flat, or gets worse.

What gets missed

Usage metrics show accelerating code output. AI delivery metrics tell a different story. The bottleneck moves from writing to reviewing, and it compounds quietly.

Code output accelerates · AI governance review queue grows · AI-to-production cycle time: flat or worse

Without SDLC guardrails, AI amplifies inconsistency, rework, and security risk

Security & Quality

AI-assisted code introduces specific vulnerability patterns: outdated defaults, insecure dependencies, hallucinated libraries. Without quality gates and review standards, security exposure compounds.

Governance Gap

Most engineering teams have no documented AI governance posture. Enterprise buyers and auditors are starting to ask. No posture means delayed contracts, or lost ones.

Security risk · Governance gaps · Review pressure · Rework amplified by AI output volume

Token and inference costs grow silently until they appear on the quarterly bill

Cost Visibility

AI agents, large context windows, and unoptimised model selection generate significant inference spend. Without attribution and governance, the cost is invisible until it hits the budget review.

The ROI Question

Leaders need to know whether AI is improving the engineering system or just generating more work at rising cost. That answer requires visibility across delivery, governance, and spend, not assumptions.

Model selection waste · Oversized context · Agent loop cost · Unattributed inference spend
What the audit covers

Four pillars. One clear picture.

A structured diagnostic across the areas that determine whether AI is improving your engineering system, or just adding activity to existing constraints.

Pillar A

Delivery Performance

Assess AI delivery indicators, AI-to-production cycle time, AI-generated PR flow, governance review depth, release friction, and bottlenecks across your AI-assisted delivery lifecycle. Understand where AI coding tools are improving the system, and where they are not.

Pillar B

AI Adoption Quality

Understand how developers actually use Copilot, Claude, ChatGPT, agents, IDE tools, and internal AI workflows, and where usage creates real leverage versus noise or rework.

Pillar C

Governance & Security

Review standards for AI-assisted code, data handling, review expectations, secure usage policies, quality gates, and alignment with your compliance requirements.

Pillar D

Token & Inference Economics

Identify waste in model selection, context size, prompt design, agent loops, routing, caching, and task-to-model fit. Understand where inference spend is justified and where it isn’t.

7k+
Engineers on Fortune 100 AI adoption I led
~55%
Observed local code output increase from AI assistants; AI-to-production cycle time typically stays flat without SDLC redesign
40-70%
LLM inference cost reduction via workload right-sizing and spend attribution
27%
Of AI cloud budgets wasted on idle or oversized LLM resources
Flagship Engagement

The 4-week AI Coding ROI Audit

A fixed-scope diagnostic engagement that maps whether AI coding tools are improving organisational delivery or just increasing code volume, governance pressure, security exposure, and spend. The output is a board- and engineering-ready decision pack for what to scale, fix, or stop next.

  • Executive summary: CTO / VP Engineering version of the evidence, risks, and recommended decisions
  • AI governance bottleneck map: AI performance indicators, AI-to-production cycle time, AI delivery throughput, AI-generated PR flow, governance review depth, and release friction
  • AI governance gap register: AI code standards, data handling, quality gates, policy alignment, and secure usage
  • Inference waste snapshot: Model selection, context size, agent loops, caching, routing, and spend attribution
  • 30/90-day roadmap: prioritised quick wins and strategic fixes tied to measurable AI engineering ROI
  • Scale / fix / stop recommendation: a clear leadership view before renewals, audits, or expansion
  • 30-day async follow-up included
Fixed Scope
4 Weeks
Request audit outline

See scope, inputs, deliverables, and decision outputs before booking time.

How the audit works

A four-week fixed-scope engagement

Each phase has a defined output. You know exactly what you are getting and when.

Week 1

Baseline

Map your tooling stack, AI performance indicators, PR and review flow, AI usage patterns, and team structure. Establish the measurement baseline before any analysis begins.

Week 2

Bottleneck analysis

Identify where AI is adding output into existing bottlenecks rather than removing them. AI governance review queues, quality gates, security exposure, governance gaps, and delivery constraints.

Week 3

Governance and cost economics

Review AI usage policy, token and inference spend, model selection, prompt efficiency, agent loop design, and workflow cost attribution. Surface waste and coverage gaps.

Week 4

Roadmap delivery

Deliver quick wins, strategic fixes, and an executive summary with a prioritised implementation roadmap. A clear view of what to scale, what to fix, and what to stop.

What you get

What the audit delivers

A clear, evidence-based picture of where AI is helping, where it isn’t, and what to do about it.

  • Baseline where AI is actually helping delivery, not just where adoption metrics look good
  • Identify review, testing, security, and governance bottlenecks introduced or amplified by AI tooling
  • Detect token and inference waste across AI-assisted workflows and agentic delivery systems
  • Prioritise quick wins alongside strategic fixes across all four audit pillars
  • A practical roadmap for measurable AI engineering ROI, tied to delivery outcomes
  • Give leadership a clear view of what to scale, what to fix, and what to stop
See sample audit findings
Who this is for

Built for engineering leaders at scale

The audit is designed for organisations where AI coding assistants and agents are already deployed, and where leadership needs clear evidence of what those tools are actually doing to delivery performance.

Roles

Engineering Leadership

CTOs, VPs of Engineering, Heads of Platform Engineering, Heads of Developer Experience, Engineering Enablement leads, and technology leaders responsible for GitHub, Copilot, SDLC, DevSecOps, cloud, or AI adoption.

Organisation

200+ Engineers, AI Tools Already Running

Engineering-heavy organisations already using or piloting GitHub Copilot, Claude, ChatGPT Enterprise, internal agents, Cursor, GitLab Duo, JetBrains AI, AWS Bedrock, or similar tools. Regulated, quality-sensitive, or complex software environments.

Situation

AI Deployed. ROI Unclear.

You have AI coding assistants in use and developers who feel faster, but delivery metrics haven’t moved in the way you expected. You need a credible, evidence-based view of what’s actually happening across delivery, governance, and cost.

2,000+ Engineers Attended

The Spec-Driven Engineering Framework: Free Download

The methodology behind agentic SDLC design, distilled from a live session attended by 2,000+ engineers. Learn the system that eliminates LLM rework and context rot.

The Spec-Driven Engineering Framework

How to Close the AI Production Gap

  • Ambiguity Tax: Markdown specs reduce LLM rework and iteration cycles
  • Context Rot: agents stay aligned to evolving intent without manual re-prompting
  • Faster delivery cycles: structured specs compress the spec-to-production timeline
Download the Framework Visit my YouTube Channel
How I integrate

Zero-Friction Operational Framework

Enterprise engagements succeed or fail on integration discipline. Here is exactly how I protect your team’s momentum.

01

Zero Management Overhead

I do not require a seat in your daily standups or a manager to assign me tasks. I operate autonomously against an agreed statement of work, surfacing blockers directly to the principal sponsor, not the delivery team.

02

Asynchronous by Design

Progress is delivered via structured weekly executive summaries and clear technical documentation. Your engineers receive no calendar invitations and no Slack noise unless they are the direct owners of a decision point.

03

Tool-Agnostic Integration

I plug directly into your existing enterprise stack (Jira, GitHub Enterprise, Azure engineering platforms, or AWS Organizations) without introducing new software vendor dependencies or procurement cycles.

Proof of work

Enterprise-scale results

Real numbers from real projects. Clients anonymised at their request.

Fortune 100 · Enterprise Technology
7,000
Engineers onboarded to GitHub Copilot & AI Assistants
The Situation

A 7,000-person engineering organisation with AI tool licences deployed, individual coding speed improving, and AI-to-production cycle time flatlining. No governance framework, no AI workflow tooling, no SDLC redesign. Pilots were running. Production was not being reached.

The Complication

End-to-end architecture and delivery of the enterprise GitHub Copilot AI adoption: governance frameworks, AI Literacy training at scale, AI workflow optimisations to resolve the AI code governance bottleneck, and Automated Quality Gates calibrated for AI-authored code vulnerability patterns.

The Outcome

Full production at 7,000-engineer scale. Measurable AI delivery improvement across all four performance indicators: AI deployment frequency, AI-to-production cycle time, AI change failure rate, and AI incident recovery time. The AI adoption also addressed the AI delivery measurement gap, reducing AI governance review queue pressure alongside the delivery performance improvements.

Explore Follow-on Engagements →
Fortune 500 · Agriculture
30%
Cloud cost reduction, £400k+ in annualised savings
The Situation

A Fortune 500 Agriculture enterprise paying for cloud infrastructure it couldn’t see clearly. AI and ML workloads were compounding the waste, idle inference instances, oversized LLM deployments, and no attribution of which teams or features were driving spend.

The Complication

Full cloud cost audit: rightsizing, Reserved Instance optimisation, zombie resource cleanup. LLM inference cost tracking via AWS Lambda with attribution by team, user, and feature. Spend dashboards that surfaced the real AI ROI gap and made it impossible to ignore.

The Outcome

30% cloud cost reduction. £400k+ in annualised savings. Infrastructure spend became a managed variable rather than a mounting liability. Dashboards and attribution tooling ensured savings persisted long after handover.

Explore Follow-on Engagements →
Fortune 500 · Manufacturing
100%
Engineering workforce on standardised, AI-ready CI/CD
The Situation

An entire engineering workforce operating on fragmented toolchains, inconsistent branching strategies, no SAST, no standardised CI/CD. Introducing AI coding tools on this foundation would have accelerated entropy and crippled the AI governance review system.

The Complication

Full engineering workforce migration to GitHub Enterprise: standardised CI/CD workflows, branch protection rules, SAST tooling calibrated to catch AI-specific vulnerability patterns. The secure architectural foundation required before any safe AI-assisted development at scale.

The Outcome

100% engineering workforce on a standardised, AI-ready delivery platform. Zero bottleneck collapse during the subsequent AI tool deployment. The governance and tooling foundation that made the safety and compliance story possible.

Explore Follow-on Engagements →
Why this is different

Enterprise AI Delivery, Built for Production Environments

Many AI engagements stop at pilots, demos, or policy decks. My work focuses on the operating system underneath: SDLC architecture, governance, delivery metrics, and cost control.

79% → 11%

of enterprises have an AI pilot running  ·  reach sustained production

The Production Gap is not a technology failure. It is a governance, SDLC architecture, and organisational design failure, and it is precisely what I was hired to close at Fortune 100 scale.

Fortune 100 · North American Construction Equipment · Safety-Critical Systems

A Standard Built Where Failure Has Consequences

I architected AI systems in environments where an untested output is a liability event, not a simple bug. That standard defines every engagement: governance architecture precedes AI delivery throughput. It is the difference between a resilient enterprise AI deployment and one that collapses after consultants leave.

7,000 Engineers · Full Production · Sustained AI Engineering Productivity Uplift

Closing the Production Gap: Proven at Scale

The gap between pilots and production cannot be closed by simply buying tools. It requires fixing the underlying organisational systems. I led a 7,000-engineer AI adoption to full production with sustained AI delivery improvements. I know exactly where enterprise AI deployments break, and how to fix them.

UK & US AI Governance · NIST AI RMF · Multi-Jurisdictional

Governance Built to Withstand Scrutiny

From North American NIST AI RMF alignment to UK DSIT and ICO requirements, I design for regulatory durability across both markets. The output is a governance framework built to withstand scrutiny from enterprise buyers, legal teams, and auditors, not just a pilot posture.

Don’t take my word for it

What clients say

🔒

Institutional engagements. Corporate names are redacted to protect proprietary source code architecture, trade secrets, and ongoing regulatory postures. Full identity validation is available under reciprocal NDA during onboarding.

★★★★★

"Matt didn't just deploy GitHub Copilot, he redesigned how our engineering organisation reviews and ships AI-generated code. Adoption went up, but so did our AI delivery performance indicators. That combination is genuinely rare."

VP of Software Delivery, Fortune 100 Industrial Systems Group
7,000+ Engineer GitHub Copilot Deployment · AI Delivery Performance Improvement
✓ Verified Enterprise Engagement
★★★★★

"We thought our AI investment was paying off until Matt showed us the AI-SDLC Measurement numbers. Senior engineers were spending 40% more time in code review. He fixed the AI workflow in two weeks and the change was immediately visible in our AI-to-production cycle time metrics."

Chief Technology Officer, Global B2B SaaS Platform
AI-SDLC Measurement Audit · 20,000+ MAU Production System
✓ Verified Enterprise Engagement
★★★★★

"Matt's AI governance framework gave us the structure our legal and infosec teams needed before our board presentation. We went from 'we're using Copilot' to having a documented risk posture, auditability controls, and a vendor evaluation policy. Night and day."

Head of Engineering, UK FinTech Scale-up
AI Governance Framework · Board-Level Risk Posture · NIST AI RMF Alignment
✓ Verified Enterprise Engagement
Follow-on engagements

Common follow-on engagements

The audit is the starting point. Once the bottlenecks are visible, I can help implement the roadmap through targeted advisory, governance, automation, and optimisation work.

How this works: The AI Coding ROI Audit surfaces exactly where to focus. These engagements are usually discovered through the audit, not chosen blind. The findings define the priority and sequence.

AI Engineering Governance Implementation

Establish a documented, defensible AI governance posture. Covers NIST AI RMF alignment, OWASP LLM Top 10 security controls, acceptable use policy, IP and copyright exposure review, quality gate design, and audit-ready technical documentation.

Rolling

Copilot & GitHub Adoption Strategy

Design and implement a structured GitHub Copilot or GitHub Enterprise adoption strategy: AI deployment planning, onboarding frameworks, usage standards, enablement programmes, and adoption metrics that connect to delivery outcomes rather than just seat activation.

4-6 Weeks

PR Review Automation & AI-Assisted Quality Gates

Reduce governance pressure by automating quality gates calibrated for AI-generated code patterns. Covers SAST configuration, automated PR standards enforcement, reviewer routing, and AI workflow tooling to reduce senior engineer bottlenecks.

3-4 Weeks

Agentic SDLC Workflow Design

Design and implement agentic engineering workflows using spec-driven development. Shift the source of truth to formal specifications so AI agents generate correct, context-aligned code. Eliminates LLM rework and reduces context rot in multi-agent delivery systems.

6-8 Weeks

Token & Inference Optimisation Sprint

Identify and eliminate waste in AI inference spend. Covers model selection, context size right-sizing, prompt design, agent loop efficiency, caching, and routing strategy. Spend is attributed by team, feature, or workflow so ROI is measurable.

3-4 Weeks

Fractional AI Engineering Advisor

Senior AI engineering guidance without the full-time headcount cost. 1-3 days per month covering tooling strategy, vendor evaluation, SDLC architecture decisions, and board-level AI ROI reporting, for organisations that need enterprise-grade perspective at advisory scale.

Ongoing

Cloud & AI FinOps Advisory

Cloud cost management combined with AI inference attribution. Rightsizing, Reserved Instance strategy, zombie resource cleanup, and LLM spend dashboards that make the cost of AI tooling visible, attributable, and controllable.

3-4 Weeks
Matt Drankowski, AI-SDLC Architect & Engineering Performance Consultant, Kraków, Poland
GitHub Copilot Enterprise AWS Solutions Architect Pro GitHub Advanced Security FinOps Certified
The person behind the work

I led AI adoption for 7,000 engineers at a Fortune 100. Now I help engineering leaders prove and improve their AI coding ROI.

I’m Matt Drankowski, AI-SDLC Architect and engineering performance consultant based in Kraków, Poland. I help engineering leaders in organisations where AI coding assistants and agents are already deployed, but delivery metrics haven’t moved in the way expected, and the ROI case is unclear.

The hard part isn’t the tooling. My most significant enterprise engagement was architecting the GitHub Copilot AI adoption across a Fortune 100 organisation at 7,000-engineer scale. That work proved technical implementation is the straightforward part. The real challenge is resolving AI workflow constraints, establishing governance, and proving delivery impact, all while navigating security, compliance, and organisational complexity.

Enterprise platform pedigree. I bring together enterprise AI platform engineering transformation, GitHub and Copilot enablement, cloud architecture, FinOps and cost optimisation, DevSecOps and governance, and AI-assisted engineering strategy. 13+ years of AWS and platform engineering experience, applied to the problems that sit at the intersection of AI adoption and engineering delivery performance.

13+
Years AWS & platform engineering
7k+
Engineers on AI tools I've deployed
UK / US
Primary markets
100%
Remote & async-first
Diagnostic Tools

Not ready to book? Start with a diagnostic.

Use these tools to estimate whether AI adoption is improving delivery, increasing governance pressure, or creating governance exposure.

AI Coding ROI Calculator

A browser-based model to isolate retained engineering delivery value from raw local typing acceleration. Input your team size, AI delivery metrics, AI tool spend, and AI code review load to surface review drag, cost waste, and retained ROI.

Run the ROI calculator

UK & US AI Governance Readiness Assessment

A 40-point diagnostic covering NIST AI RMF alignment, OWASP LLM Top 10 security controls, IP and copyright exposure, AI Literacy posture, vendor risk, human oversight, and incident response readiness. Calibrated for UK and US enterprise buyers and auditors. Identify your gaps before they identify you.

Access the Governance Assessment
Before you book

Frequently asked questions

Tool adoption is not the same as organisational delivery improvement. The audit shows whether AI is reducing AI-to-production cycle time or simply shifting work into AI governance review queues, rework, security exceptions, governance gaps, and hidden spend.

No. Delivery metrics show outcomes. It usually does not show whether AI coding tools are adding load to senior reviewers, changing AI-generated code risk profiles, creating policy exceptions, or increasing inference spend without attribution.

No. This is a fixed-scope engineering audit focused on delivery flow, AI-SDLC measurement, governance, security controls, and AI tooling cost. It is not an AI strategy deck, prompt-training workshop, or open-ended retainer.

Enterprise intake for 2026 is currently open. You can start asynchronously by requesting the 2-page audit outline, or book a 15-minute fit review if the problem is already clear. If there is a fit, I scope a fixed engagement within 48 hours. No lengthy proposals, no retainer pressure, just a clear diagnosis and path forward.

I operate on fixed-scope, fixed-price engagements. Most transformations begin with a 4-week diagnostic to establish your AI performance baseline and measure the hidden bottlenecks. Follow-on implementation phases, ranging from toolchain automation to governance frameworks, are scoped separately based on diagnostic findings. I do not do body-shopping or open-ended retainers.

Yes. I build AI governance frameworks calibrated to UK and US enterprise requirements, covering NIST AI RMF alignment, UK DSIT and ICO guidance, OWASP LLM Top 10 security controls, IP and copyright exposure from AI-generated code, and audit-ready documentation. The output is a defensible posture your legal, infosec, and board teams can sign off on before enterprise buyers or auditors ask.

Find out whether your AI coding investment
 is improving delivery, or just generating more work.

Not ready for a call yet? Start with something concrete: request the audit outline, inspect sample findings, or run a diagnostic before booking time.

Request the 2-page audit outline

“Matt didn’t just deploy GitHub Copilot, he redesigned how our engineering organisation reviews and ships AI-generated code. Adoption went up, but so did our AI delivery performance indicators. That combination is genuinely rare.” - VP of Software Delivery, Fortune 100 Industrial Systems Group

See sample findings, access the diagnostic tools, or .