AI-SDLC Transformation for engineering organisations already using coding AI
From individual AI coding adoption to governed, agentic delivery.
Turn AI tooling spend into measurable engineering capability.
Your engineers already have Copilot, Cursor, Claude Code, or internal agents. Leadership still cannot prove what that changed in cycle time, review load, risk, or licence waste. I build the operating model and the measurement system that answer it.
Leading AI-SDLC transformation in a Fortune 100 environment, supporting 10,000+ engineers.Copilot governance, AI Champions, custom agents and measurement, built on a GitHub platform migration of 14,000+ users. Validation available under NDA or via LinkedIn.
DevOps is the technical foundation. The work is integrating AI into real enterprise delivery systems, with review gates, a measurement system, and a controlled path toward higher autonomy.
UK-registered consultancy for UK, EU and US engineering leaders
Matt Drankowski, AI-SDLC Transformation Architect
Enterprise AI operating models, not tool rollouts. You work directly with me: no account managers, no junior consultants, and no retainer required to start.
Enterprise AI delivery, not a tooling rollout. Validated at Fortune 100 scale. Principal-led, fixed-fee, async-first.
Client names anonymised due to enterprise confidentiality. Validation available under NDA.
The commercial case
Turn AI tooling spend into measurable engineering capability
The operating model below is the method. These are the outcomes it is accountable for, measured against your own delivery data rather than vendor benchmarks.
Faster delivery
Planning and cycle time baselined before the work starts, then tracked per workflow rather than per seat.
Planning time · cycle time
Less rework
Change failure rate and review load watched together, so speed gained upstream is not paid back in escaped defects.
Change failure rate · review load
Controlled AI risk
Policy, review gates and security posture evidenced well enough for regulated buyers, reviewers and legal teams.
Gate coverage · policy conformance
Return on AI licences
Seat spend set against measured delivery gain, so renewal becomes a decision with evidence behind it.
Cost per seat · delivery gain
Safe agentic automation
Autonomy granted one workflow at a time, behind human approval gates that keep accountability where it belongs.
Autonomy stage · gate pass rate
Higher throughput
Capacity released across the estate and reported as value per workflow, not as story points.
Value per workflow
Phase 1: Transformation Assessment sets the baseline and costs everything that follows.
4 weeks · fixed scope · from £18,000, fixed before signature
One system for maturity, measurement, governance and controlled autonomy.
The method separates four questions that tool dashboards usually blend together: where AI is used, what changed in delivery, which controls make it safe, and which workflow is ready for more autonomy.
01
Locate maturity
Place the organisation on a seven-stage ladder from licensed seats to increasingly autonomous workflows with measurable controls.
02
Measure delivery effect
Connect usage and acceptance to planning time, cycle time, review load, rework, security and value per workflow.
03
Define governance
Give every AI-assisted workflow an owner, policy boundary, review gate and inspectable quality or security outcome.
Three pieces of work. Everything else is supporting evidence.
Enterprise AI adoption, agentic mainframe modernisation, and the governed multi-agent operating model. Client names stay confidential; validation is available under NDA.
Story 01 · Enterprise AI adoption
10k+
Engineers supported by the AI-SDLC transformation
Situation
Copilot was arriving as individual productivity. Leadership needed an operating model: communities, enablement, custom agents, and a governed rollout that would hold at organisational scale.
What changed
Adoption programmes, AI Champions, tailored sessions for engineering groups, reusable instructions, and a governance path from personal use to organisational capability.
A mainframe-modernisation planning exercise was expected to take about a year. The constraint was specialist analysis and sequencing, not the willingness to start.
What changed
Agentic planning and specialist AI assistants compressed the work to weeks, with human validation and approval gates on every material decision.
Assistants were general-purpose. Work quality depended on whoever wrote the prompt. There was no shared context, no handoff, and no agreed limit on autonomy.
What changed
Planner, architect, implementer, tester, reviewer, security auditor, DevOps engineer, and documentation roles, with model selection, gates, and intentional limits.
From ad-hoc AI coding to a governed, measurable, AI-enabled software delivery model. Three phases, entered one at a time. You commit to the next only after the previous one has shown its work.
Phase 1: Transformation Assessment
Baseline the organisation against the seven-stage maturity framework, find where AI is adding work rather than removing it, and define the target operating model. Ends with a board-ready decision pack and a costed roadmap for the phases below.
4 weeks · fixed scopeFrom £18,000fixed before signature
Phase 2: Transformation Build
Implementation, not recommendations. Governed pilots on real workflows, role-based agent patterns, review gates and policy, the measurement system wired into your delivery data, and the first cohort of team leads trained to run it.
8–12+ weeks · scoped in Phase 1Quoted on the roadmapfixed fee per stage
Phase 3: Scale & Optimise
Optional quarterly transformation oversight after the build: review the evidence, correct governance drift, choose the next workflow, and coach the champions who carry the system across the organisation.
Quarterly scope · no default retainerQuoted on the roadmapoptional and capacity-limited
Phase 1 · How the transformation starts
The 4-week Transformation Assessment
The fixed-scope entry point to the transformation, for engineering organisations that already have coding AI and need a governed path from individual adoption to organisational, increasingly agentic delivery. It ends with a costed roadmap for the build, and you are free to stop there and run it yourself. DevOps is the integration foundation, not the subject of the assessment.
AI adoption and maturity assessment against the seven-stage framework
Delivery-system bottleneck analysis where AI is adding work rather than removing it
Governance and risk review of policy, review gates, security, and accountability
Agentic workflow opportunity mapping for roles, handoffs, and human gates
Measurement framework using the eight AI-value families, not adjacent operational noise
Governed pilot design for one workflow that can be run with review gates in place
Executive roadmap for what to scale, fix, or stop over 30 and 90 days
Fixed Scope
4 Weeks
From £18,000
Fixed before signature · varies with organisation scale and data-source complexity
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 to read it. Opens instantly, print/PDF-ready. For a reading on your own organisation, the diagnostic takes .
You work directly with me, not a team handed off after the sale.
The principal who designed enterprise AI operating models is the principal who delivers the transformation. No account managers. No junior consultants. No retainer required to start. Principal-led, delivered through your engineering organisation. I design the system, lead the first pilots, set the patterns, and train the champions and team leads who scale it across your teams.
Sample assessment artefact
What a board-ready finding looks like
The assessment does not stop at “usage is up”. It places the organisation on the maturity ladder, names the bottleneck, and recommends the next governed step toward agentic delivery.
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.
Organisation is regularly adopted in the IDE, but agentic work is still informal and ungated.
Fix
Impact
Planning and implementation sped up locally. Human intervention is still undocumented, so autonomy cannot be raised safely.
Stop
Control Gap
Custom agents exist in pockets. There is no governed distribution path or shared review standard.
Fix
Decision
Run one governed pilot with role-based agents and human approval gates before expanding autonomy.
Scale
Who this is for
Built for senior engineering leaders with coding AI already in the building
Leaders who need an operating model, a maturity reading, and a governed next step, not another tool rollout. Best fit is 50+ engineers. Smaller organisations work where coding AI is already deployed and one person is accountable for what it returns.
Roles
Engineering and AI transformation leadership
CTOs, VPs of Engineering, Heads of AI Transformation, and senior leaders accountable for moving from individual AI use to organisational capability.
Organisation
Coding AI already running
Engineering organisations of 50+ engineers using Copilot, Cursor, Claude Code, Codex, or internal agents, and ready to treat that stack as a delivery system rather than a licence programme.
Situation
Adoption without an operating model
Developers feel faster. Assistants are in the IDE. There is no shared measurement system, no governed agent path, and no honest view of maturity.
Not a fit: teams still deciding whether to try coding AI, organisations that want tool training or licence procurement only, and anyone looking for a delivery team to hand the work to. DevOps is the foundation this work sits on, not the engagement.
Before you get in touch
Frequently asked questions
Using an assistant is individual adoption. The assessment establishes maturity, governance, measurement, and whether you can move to role-based agents with human gates without losing accountability. The build phase then puts that in place.
Delivery metrics show outcomes. The AI value system measures licence activation, surface adoption, user-initiated versus agentic work, acceptance, planning or cycle-time change, quality and security, human intervention, and value per workflow.
No by default. The assessment works from delivery metadata, workflow data, PR/review patterns, AI-tool telemetry where available, governance artefacts, and stakeholder interviews. Source-code access is not required unless explicitly agreed.
Yes. Public claims are intentionally conservative because much of the relevant work was done inside large enterprise environments. Validation is available under NDA where appropriate.
You get the measurement framework for the pattern your answers indicate, sent by me, usually within a working day. If it applies to your organisation, reply to it and we take it from there: scope, data access, stakeholders, timeline. Phase 1 then runs for four weeks and ends with a board-ready decision pack and a costed roadmap. Whether you continue into the build is a decision you make with that roadmap in hand, not at signature.
Typical fit-review availability: within 5 working days
By not trying to reach every engineer personally. The transformation is principal-led and delivered through your own engineering organisation: I design the operating model, lead the first governed pilots, set the agent and review patterns, and train the team leads and champions who carry it into their teams. What scales is the system and the people you already employ, not consultant headcount. That is also why there is no delivery team to hand you off to.
No. You commit to Phase 1 only: four weeks, fixed scope, from £18,000, with the Week 2 exit clause. It ends with a costed roadmap for the build. Plenty of organisations take that roadmap and execute it with their own people, and that is a legitimate outcome. Phases 2 and 3 are scoped and priced from what Phase 1 finds, never before it.
It is not for teams still deciding whether to try coding AI, or for organisations that only want tool training. DevOps is the foundation this work sits on, not the engagement. It is for leaders who already have coding AI and need a governed operating model.
Resources
Not ready to get in touch? Start with the material.
Three self-serve assessments and frameworks drawn from the same work. No call required.
Move from individual AI adoption to a governed delivery system.
Four questions on AI adoption, measurement and evidence. You get a reading on where your organisation sits, and the measurement framework that goes with it. About 60 seconds, no call required.
