Work Approach Transformation About
AI-SDLC Transformation Architect

From individual AI coding adoption
to governed, agentic delivery.

Turn AI tooling spend into measurable engineering capability.

I help large engineering organisations move from individual AI coding adoption to governed, measurable, increasingly agentic software delivery.

Built the AI operating model inside a Fortune 100 engineering environment of 14,000+ engineers. Copilot adoption, AI Champions, custom agents, and governed rollout. 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.

Read the 2-page assessment outline

Or see the three principal stories and validate the background on LinkedIn.

Matt Drankowski
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, no retainer pressure.

More about how I work →

Three principal stories

Enterprise AI adoption
14,000+ engineer environment

Copilot · Champions · custom agents · governed rollout

Flagship AI-SDLC example
Mainframe planning: ~1 year to weeks

Agentic planning · specialist assistants · human gates

Operating model
Governed multi-agent AI-SDLC

Eight roles · handoffs · review gates · limited autonomy

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

Read the 2-page assessment outline
Enterprise AI operating model

From licensed seats to a governed delivery system.

Individual Copilot use is not an operating model. These are the capabilities I put in place so AI becomes organisational, measurable, and safe to scale.

01

Copilot adoption and governance

Licence activation is not adoption. I design the policy, safe-use standards, and rollout model that turn Copilot from a personal tool into an organisational capability.

02

AI Champions and engineering communities

Enablement that sticks is peer-led. I build Champion networks and specialist communities so practices spread through engineering, not through a one-off training day.

03

Role-based custom agents

Planner, architect, implementer, tester, reviewer, security auditor, and documentation agents with explicit responsibilities, model choices, and boundaries.

04

Governed agent distribution

Reusable instructions and agents are published through a controlled path. Teams do not invent shadow agents that bypass review, security, or accountability.

05

Human-review and approval gates

Autonomy is granted only where the gate is defined. Humans remain accountable for architecture, security, and release decisions.

06

Progression toward higher autonomy

Organisations move stage by stage: from user-initiated assistance to agent-initiated work with human gates, then to higher autonomy with measurable controls.

07

Security, quality and accountability

Every agentic workflow has an owner, a review standard, and a quality or security outcome that can be inspected. Speed without accountability is not transformation.

AI-SDLC maturity framework

How far has AI actually entered the delivery system?

I evaluate organisations across seven stages. Most large engineering groups are licensed and partially activated. Very few have agent-initiated work with human gates and measurable controls.

Stage 1

Licensed

Seats exist. Policy may exist. Daily engineering work has not changed.

Stage 2

Activated

A cohort has signed in and tried the assistant. Usage is uneven and mostly personal.

Stage 3

Regularly adopted

A meaningful share of engineers uses an assistant as part of weekly work, not as a novelty.

Stage 4

Used across multiple assistant surfaces

IDE, chat, pull-request, and planning surfaces are in play. Context is still fragmented.

Stage 5

Embedded into workflows

AI is inside planning, implementation, review, and documentation paths, with shared instructions.

Stage 6

Agent-initiated with human gates

Specialist agents start work. Humans approve architecture, security, and release decisions.

Stage 7

Increasingly autonomous with measurable controls

Higher autonomy is granted only where quality, security, and intervention frequency are measured.

AI value-measurement approach

Measure the AI system, not adjacent operational noise.

Licence counts and developer sentiment are not a value system. These are the families I use to tell whether AI is becoming delivery capability.

Licence activation and active usage

Who has access, who actually works with the assistant, and who has gone quiet.

Adoption by assistant surface

IDE completions, chat, pull-request review, planning, and custom agents, tracked separately.

User-initiated versus agentic execution

How much work still starts with a person typing, versus an agent opening the task.

Acceptance and completion rates

Suggestions accepted, tasks finished, and work abandoned after AI involvement.

Cycle-time or planning-time improvement

Time from intent to approved plan, or from change to production, attributed to the workflow.

Quality and security outcomes

Defects, rework, policy violations, and security findings on AI-touched work.

Autonomy level and human intervention

How often a human must stop, correct, or approve the agent before the work can proceed.

Business value produced per workflow

What each governed workflow actually returns: compressed planning, cleared backlog, reduced risk.

Principal stories

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
14k+
Engineers in the Fortune 100 environment
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.

Read the adoption story →
Story 02 · Flagship AI-SDLC example
~1y
Planning exercise compressed to weeks
Situation

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.

Read the flagship case →
Story 03 · Operating model
8
Role-based agents in one AI-SDLC system
Situation

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.

See the agent operating system →
The engagement model

AI-SDLC Transformation

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 scope From £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 1 Quoted on the roadmapfixed fee per stage

Phase 3: Scale & Optimise

Rollout across the engineering organisation through trained champions, with the operating model, metrics review, and coaching cadence that keep it running once I am no longer in the room.

Ongoing · reviewed quarterly Quoted on the roadmapcapacity-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
Read 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 to read it. Opens instantly, print/PDF-ready. When you want the call instead, the fit check 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 pressure. 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.
Read the assessment outline
AI-SDLC Assessment / Maturity and bottleneck map
Executive decision pack excerpt
Redacted
Finding
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
Maturity Stage located
Governance Gates defined
Roadmap 30 / 90-day actions
Who this is for

Built for senior engineering leaders at large-organisation scale

Leaders who already have coding AI in the building and need an operating model, a maturity reading, and a governed next step, not another tool rollout.

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

Large engineering organisations 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.

Thought leadership and delivery credibility

The work is asked for again. The talks follow the work.

Evidence from enterprise sessions, communities, and invited platforms. Talk titles stay off this page unless they are on the public record.

Enterprise

Large-enterprise AI and Copilot sessions

Specialised sessions for engineering groups, with repeat requests for deeper, role-specific training rather than a single awareness day.

Community

AI Champions and engineering communities

Peer-led networks that carry adoption, custom agents, and review standards after the initial enablement wave.

Platforms

Toptal, GitHub, and selected YouTube

Two Toptal conference appearances, GitHub’s interest as a potential speaker, and selected YouTube material on AI-assisted engineering.

Selected YouTube briefings

Before you book

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.

If there is a fit, we agree scope, data access, stakeholders, and timeline. Phase 1 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.

Availability last updated: August 2026 · 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 for a fit review? Start with the material.

Three self-serve assessments and frameworks drawn from the same work. No call required.

Assessment

UK & US AI Governance Readiness

A 40-point readiness assessment covering NIST AI RMF alignment, OWASP LLM Top 10 controls, IP exposure, data governance, vendor risk, and incident response.

Run the assessment →
Model

AI Value Measurement Model

Model activation, usage, and retained workflow value against the AI-SDLC measurement families, including the review-cost drag most ROI cases leave out.

Open the model →
Framework

Spec-Driven Engineering Framework

How to close the AI production gap: eliminating the ambiguity tax, handling context rot, and specifying work so agentic delivery stays reviewable.

Read the framework →

Move from individual AI adoption
 to a governed delivery system.

Confirm whether the AI-SDLC Transformation fits your organisation and decision timeline. The check takes about 60 seconds; the call takes 15 minutes.

Read the 2-page assessment outline

Phase 1 assessment · from £18,000 · 4 weeks · fixed scope