Audit Sample output About LinkedIn Download the outline
For CTOs, VPs of Engineering, Heads of AI Transformation, and software engineering leaders in 200–5,000 engineer organisations

Your developers are
writing code faster.
Your delivery system
is not.

Led a 7,000-engineer GitHub Copilot adoption at Fortune 100 scale. Fixed-scope AI Delivery System Audit. Validation available under NDA or via LinkedIn.

I run fixed-scope AI Delivery System Audits for engineering organisations using Copilot, Cursor, Claude Code, Codex, and internal agents. The audit shows whether AI is improving cycle time and delivery outcomes, or just increasing PR review load, governance risk, and spend.

From £18,000 fixed fee · 4 weeks · 50% on signature, 50% on delivery

No form, no signup. The outline opens instantly. You can also inspect sample findings before booking.

Matt Drankowski
UK-registered AI-SDLC consultancy for UK, EU and US engineering leaders

Matt Drankowski, AI-SDLC Architect

Principal-led AI delivery diagnosis. You work directly with me on every engagement: no account managers, no junior consultants, no retainer pressure.

More about how I work →

Proof focused on AI-assisted delivery

Fortune 100 · Industrial Systems
7,000-engineer Copilot adoption

Governance · AI-SDLC measurement · production delivery change

Global B2B SaaS Platform
Review-pressure diagnosis

AI-generated PR flow · reviewer load · cycle-time bottlenecks

UK Fintech Scale-up
Board-ready AI governance

Risk posture · review controls · expansion readiness

Built for mid-market engineering orgs. Validated at enterprise scale. Fixed-fee, principal-led, async-first.

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

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.

Scale
Enterprise AI adoption experience applied to mid-market audit decisions
~55%
Observed local code output increase from AI assistants; AI-to-production cycle time can stay flat without AI-SDLC redesign
40-70%
LLM inference cost reduction via workload right-sizing and spend attribution
30/90
Roadmap horizon for scale, fix, or stop decisions after the audit
Flagship Engagement

The 4-week AI Delivery System Audit

A fixed-scope AI-SDLC diagnostic that maps whether AI coding tools and agents are improving organisational delivery or just increasing code volume, review load, governance pressure, security exposure, and spend. This is not a DevOps maturity assessment, cloud modernisation programme, or generic AI strategy engagement.

  • Executive summary: CTO / VP Engineering version of the evidence, risks, and recommended decisions
  • Delivery 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
From £18,000
Fixed fee · 50% on signature, 50% on delivery
Download the 2-page outline
Week 2 progress check. If by the end of Week 2 the baseline and bottleneck analysis have not surfaced at least 3 quantified findings, you can exit the engagement and owe only for the time invested.

No form. Opens instantly. Print/PDF-ready. Or request principal review if you would like a tailored response.

You work directly with me, not a team handed off after the sale.

The principal who led enterprise-scale AI adoption is the principal who delivers your audit. No account managers. No junior consultants. No retainer pressure. One person, end-to-end, from intake to roadmap.

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 the delivery system, where it is creating drag, 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
Sample audit artefact

What a board-ready finding looks like

The audit does not stop at "AI usage is up". It turns delivery evidence into a concise decision view: where the bottleneck moved, what risk is exposed, and what leadership should scale, fix, or stop next.

Redacted example. This mock artefact shows the format and decision logic. Client-specific data, repo names, and internal metrics are removed or validated under NDA.
AI Delivery System Audit / Bottleneck Map
Executive decision pack excerpt
Redacted
Finding
AI-assisted PRs are larger than team norms and waiting on the same small reviewer pool.
Fix
Impact
Code output increased, but AI-to-production cycle time did not improve because review capacity stayed unchanged.
Stop
Control Gap
Secure-use policy exists, but AI-specific review standards and provenance checks are inconsistent.
Fix
Decision
Scale usage only after PR sizing, reviewer routing, and AI-ready quality gates are standardised.
Scale
Who this is for

Built for senior engineering leaders at 200–5,000 engineer scale

The audit is designed for organisations where AI coding assistants and agents are already deployed, and where leadership needs evidence before renewal, rollout expansion, board reporting, or governance decisions.

Roles

Engineering and AI Delivery Leadership

CTOs, VPs of Engineering, Heads of AI Transformation, Engineering Directors, Heads of Software Engineering, and senior leaders accountable for AI-assisted delivery outcomes.

Organisation

200+ Engineers, AI Tools Already Running

Engineering-heavy organisations already using or piloting GitHub Copilot, Cursor, Claude Code, Codex, ChatGPT Enterprise, internal agents, GitLab Duo, JetBrains AI, or similar coding 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.

Not For

Outside the Audit Scope

Not for cloud migration, managed DevOps, platform rebuilds, generic AI strategy workshops, or teams that have not yet deployed AI coding tools.

How I integrate

Zero-Friction Operational Framework

The audit is designed to work from existing delivery, review, governance, and cost evidence with minimal calendar drag.

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 work from the systems you already use for delivery evidence: Jira, GitHub Enterprise, GitLab, CI logs, review metadata, AI tool telemetry, governance artefacts, and policy documents.

Proof of work

AI-SDLC proof, not adjacent consulting

Representative work from AI-assisted delivery, review-flow, and governance engagements. Clients anonymised at their request.

Enterprise AI Adoption
AI
Copilot adoption with governance and measurement
The Situation

A large engineering organisation with AI tool licences deployed, individual coding speed improving, and unresolved questions about governance, review flow, delivery measurement, and production adoption.

The Work

Architecture and delivery of the enterprise Copilot adoption operating model: AI-SDLC measurement, governance frameworks, review expectations, safe-use controls, and production delivery change.

The Outcome

Full production at enterprise scale with a defensible governance posture and delivery measurement model. Validation available under NDA.

See sample findings →
Global B2B SaaS Platform
40%
More senior review time exposed in AI-assisted flow
The Situation

AI adoption looked healthy in usage dashboards, but delivery leaders could not explain why cycle time had not improved in line with developer sentiment.

The Work

AI-SDLC measurement surfaced larger PRs, concentrated reviewer load, and weak attribution between generated code, review effort, and production outcomes.

The Outcome

The renewal conversation moved from seat activation to retained delivery value, review policy, and the specific changes needed before scaling usage further.

See sample findings →
UK Fintech Scale-up
NIST
AI governance posture prepared for board review
The Situation

Engineering leaders were using AI coding tools, but legal and information-security stakeholders needed a clearer posture before approving broader use.

The Work

Created a practical governance gap register covering acceptable use, data handling, review expectations, provenance, vendor evaluation, and auditability controls.

The Outcome

Leadership moved from "we use Copilot" to a documented risk posture and board-ready control story for AI-assisted software delivery.

See sample findings →
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 have been hired to close at enterprise scale.

Sources: Gartner, 2024-2025 enterprise AI surveys · CIO.com, 2025 analysis of AI pilot-to-production rates · industry range 80–90% of AI pilots fail to reach sustained production

Industrial Systems · Safety-Critical Environments

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.

Enterprise AI Adoption · Full Production · Sustained Delivery Change

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 have taken enterprise AI adoption from rollout to production, and I know where these deployments break.

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.

Optional post-audit support

Implementation support after the diagnosis

The audit is the starting point. Once the bottlenecks are visible, any follow-on work is scoped from the evidence, not chosen from a generic service menu.

How this works: The AI Delivery System Audit surfaces exactly where to focus. Follow-on support is optional and evidence-led: governance, review flow, agentic workflow design, or AI spend attribution.

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 From £15,000 · or £4,000/mo rolling

AI-SDLC Adoption Strategy

Design a structured AI coding adoption strategy: deployment planning, usage standards, enablement, and adoption metrics that connect to delivery outcomes rather than seat activation.

4-6 Weeks From £12,000 · fixed fee

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 From £10,000 · fixed fee

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 From £18,000 · fixed fee

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 From £8,000 · fixed fee

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 From £3,500/mo · 1 day/month

AI Spend Attribution Advisory

Make coding-agent and LLM spend visible by team, workflow, feature, or task. Covers model routing, caching, context discipline, and dashboards that connect AI cost to shipped outcomes.

3-4 Weeks From £8,000 · fixed fee
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
Enterprise 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
Matt Drankowski, AI-SDLC Architect
GitHub Copilot Enterprise AWS Solutions Architect Pro GitHub Advanced Security AI Delivery Governance
The person behind the work

I help engineering leaders prove whether AI is improving delivery, not just increasing code volume.

I’m Matt Drankowski, AI-SDLC Architect. I operate through a UK-registered company and am personally based in Poland, working remotely with engineering leaders across the UK, Europe and the US. I help 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 GitHub Copilot adoption at large-enterprise scale. That work proved technical rollout 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.

The lens is AI-SDLC. I focus on how AI coding tools and agents change review flow, delivery measurement, governance posture, and spend attribution. My background in enterprise software delivery helps me read the system, but the front-of-house offer is a fixed-scope AI delivery diagnosis.

13+
Years enterprise delivery experience
Scale
Enterprise AI adoption experience
UK / US
Primary markets
100%
Remote & async-first

Validate my background on LinkedIn before booking a fit review.

Additional resources

Secondary tools if you are still exploring.

These tools remain available for early self-assessment. The main path for outbound visitors is still the audit outline, sample findings, and fit review.

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.

No. This is not a DevOps maturity assessment, cloud migration, managed platform rebuild, or infrastructure cost programme. The audit measures how AI coding tools and agents change review flow, governance, delivery risk, AI-to-production cycle time, and system-level ROI.

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.

Availability last updated: June 2026 · Next open fit-review slot: within 5 working days

I operate on fixed-scope, fixed-price engagements. The front-end offer is a 4-week diagnostic to establish your AI delivery baseline and measure the hidden bottlenecks. Follow-on implementation phases are optional and scoped separately from the evidence. 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.

Yes, under reciprocal NDA. Most enterprise engagements are confidential due to proprietary source code architecture, ongoing regulatory posture, and competitive sensitivity. Two prior clients have authorised named references in the testimonials above; for the rest, identity validation and reference calls are available during onboarding once a mutual NDA is in place. If references are a hard requirement before booking the fit review, mention it on the call and I will arrange a redacted reference call with a comparable engagement profile.

UK-registered AI-SDLC consultancy for UK, EU and US engineering leaders. I’m personally based in Poland and work remotely with clients across the UK, Europe and the US. Most audit work is async-first, with scheduled leadership sessions in UK/EU/US-friendly time zones.

Default is read-only, least-privilege access scoped to the specific bottleneck under review. I do not require access to source code to deliver the audit — delivery flow, governance posture, and cost attribution can be assessed from PR metadata, automation logs, and AI tool telemetry. Where deeper code review is needed, it is conducted on a synthetic, redacted sample under NDA. Your IP stays yours; deliverables are produced for you, not derived from your assets.

Yes. The most significant enterprise engagement to date was in a safety-critical industrial environment with formal regulatory oversight. I am comfortable operating within ISO 27001, SOC 2, and sector-specific control environments (FCA / PRA for UK financial services, HIPAA-adjacent workflows for US healthcare, public-sector data handling requirements). Audit deliverables can be produced in formats compatible with your internal risk register, vendor assessment, and regulatory reporting cycles.

Yes — and that is the point. The audit is diagnostic, not advocacy. If your delivery performance, governance posture, and cost attribution are healthy, the deliverable will say so clearly with the evidence, and I will tell you which follow-on engagements are not worth commissioning. A clean audit result is a defensible board-level asset in its own right; an audit that always recommends more work is not an audit.

Disagreement between engineering, security, AI programme, and finance leadership is one of the most common engagement triggers — it usually means the bottleneck is organisational, not technical. The audit is designed to surface the evidence each stakeholder needs in their own language: delivery metrics for engineering, control and risk posture for security, spend attribution and unit economics for finance and the board. The 30-day async follow-up is explicitly available to support internal alignment off the back of the findings.

Yes. Every engagement is delivered 100% remote and async-first by design — no travel, no on-site requirement, no recurring meetings on your calendar. I operate in UK / EU / US timezones and overlap with all three for synchronous escalations when needed. The 15-minute fit review, the weekly executive summary, and the final readout are the only fixed synchronous touchpoints; everything else is documented and asynchronous. Your engineers receive no calendar invitations unless they own a specific decision point.

The 30-day follow-up is included with every audit. It covers async review of the implementation steps you take in the first month after delivery — typically clarifying findings, pressure-testing the prioritisation of the 30/90-day roadmap, and answering technical questions as your team begins executing. It is not a second engagement; it is structured protection against the most common failure mode, which is the audit landing cleanly and then stalling in implementation.

Three concrete differences. First, principal-led end-to-end: the person scoping the engagement is the person delivering it — no handoff to a junior team after the sale. Second, engineering-specific evidence base: the audit reads your actual delivery flow, PR metadata, governance queue depth, and AI tool telemetry rather than running stakeholder interviews and producing a strategy deck. Third, async-first delivery: no recurring weekly meetings, no junior-consultant slide production, and no retainer pressure after the engagement closes. If a Big 4 or strategy-house engagement is the right fit for your situation, I will say so on the fit review.

2-minute walkthrough

See what the audit actually looks like.

A short walkthrough of the methodology, the deliverables, and what a real AI delivery finding sounds like in a board conversation. If you prefer reading, the 2-page outline covers the same ground.

Visit the YouTube channel

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

If the problem is already clear, book a short fit review. If not, start with the outline or sample findings first.

“Matt didn’t just deploy AI coding tools, 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, industrial systems group

See the audit outline, inspect sample findings, or validate on LinkedIn.