AI adoption, cloud architecture and the delivery systems behind both.
I help engineering teams adopt AI in everyday delivery and solve the cloud, platform and CI/CD problems around it. Bring me a defined project or add experienced, hands-on support to your team on a part-time basis.
Available for part-time consulting and scoped projects. We agree priorities, hours and working arrangements around the problem you need to solve.
Start with your project, your team and the help you need. We agree the scope and availability before work begins.
Or explore the experience and approaches and validate the background on LinkedIn.
Matt Drankowski, AI-SDLC Consultant & Cloud 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.
Examples of work we can scope together, depending on your priorities and environment.
Start with a team that needs shared practices for AI-assisted work. Configure repository instructions, agree review expectations and run practical sessions using the team's own tasks.
A first scope: one team, reusable guidance and an agreed way to review changes and track adoption.
Choose a repeatable engineering task. Define the context, permissions and acceptance checks, then configure a workflow with bounded retries and human escalation.
A first scope: one repository, one workflow and recorded results to decide what to improve or extend.
Explore the workflow →Bring an AWS architecture, cloud cost, CI/CD or platform migration problem. Review the design, agree a change and work with your engineers to implement and verify it. Cloud and platform work can be a standalone engagement.
A first scope: one documented problem, an agreed change and checks against the required behaviour.
We agree scope, access, paid hours and acceptance criteria before starting. You can hire me directly for this work without a prior assessment.
Work with me on a defined project or bring me in for regular hands-on support. We agree the scope, hours and working arrangements before starting.
Work directly with me on AI tooling, developer enablement, cloud architecture or delivery improvements. We agree priorities, a limit on paid hours, meeting time and working windows before starting.
Hourly · agreed availability Regular hands-on supportRate and hours agreed for your scopePut a defined change into practice: a governed agent workflow, Copilot adoption, an engineering platform improvement or an architecture change. Agree deliverables, access and acceptance criteria up front.
Defined project · clear deliverables A focused delivery scopeTimeline and commercial terms agreed together Explore an AI engineering pilot →Enterprise delivery experience
Copilot · Champions · custom agents · measurement
Azure DevOps to GitHub · enterprise delivery
AWS · DevSecOps · FinOps · platform leadership
AI workflows depend on the platform underneath. Identity, CI/CD, security and cost controls make a workflow maintainable. I connect AI adoption with the engineering systems your team already runs.
Project summaries focus on my contribution and delivery outcomes. Further details or references are subject to client permission.
Two parts of my enterprise experience: a completed developer-platform migration and the organisational capabilities built around AI-assisted delivery.
Azure DevOps was the starting platform. The migration moved more than 14,000 users to GitHub, establishing the platform foundation for the subsequent AI-SDLC work.
My enterprise platform work included this migration. It sits alongside my experience in cloud architecture, DevSecOps and developer enablement: moving a platform and helping engineering teams work with it.
I lead the AI-SDLC transformation, connecting Copilot adoption, governance, developer enablement, custom agents and measurement.
Adoption gained named owners and an AI Champions community. Engineering groups gained tailored sessions, reusable instructions and a controlled path for distributing agents.
Practical methods for a new engagement, illustrated separately from the delivery experience above.
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.
Planning and cycle time baselined before the work starts, then tracked per workflow rather than per seat.
Planning time · cycle timeChange failure rate and review load watched together, so speed gained upstream is not paid back in escaped defects.
Change failure rate · review loadPolicy, review gates and security posture evidenced well enough for regulated buyers, reviewers and legal teams.
Gate coverage · policy conformanceSeat spend set against measured delivery gain, so renewal becomes a decision with evidence behind it.
Cost per seat · delivery gainAutonomy granted one workflow at a time, behind human approval gates that keep accountability where it belongs.
Autonomy stage · gate pass rateCapacity released across the estate and reported as value per workflow, not as story points.
Value per workflowStart with a concrete engineering problem. Agree the support you need and a manageable first scope.
We agree the problem, success criteria and review boundaries, then put a focused change into practice. For AI-assisted work, we track delivery time, review effort and quality before deciding what to expand.
The Transformation Assessment reviews AI adoption, delivery bottlenecks, governance and measurement. You leave with a pilot design and a costed roadmap your team can use.
4 weeks · fixed scope · from £18,000
Illustrative example. This fictional scenario shows the format and decision logic. It is not a client document or evidence of a delivered result.
For CTOs, engineering leaders and platform owners who have a concrete challenge and need experienced support. The scope can be a single team or an enterprise programme, with a named owner and agreed outcomes.
CTOs, VPs of Engineering, Heads of AI Transformation, and senior leaders accountable for moving from individual AI use to organisational capability.
Teams adopting or already using AI coding tools, and organisations that need practical cloud, platform or delivery architecture support.
Developers feel faster. Assistants are in the IDE. There is no shared measurement system, no governed agent path, and no honest view of maturity.
How we work: a defined scope, a responsible owner in your team, and agreed access and working hours. Consulting is delivered directly by me; larger programmes also need capacity from your own engineering team.
I help turn existing tools into repeatable team practices: useful instructions, developer enablement, review gates and measurement. We can start with a specific workflow or adoption problem. An assessment is an option when the priorities need a wider review.
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.
It depends on the work. Architecture advice or an assessment can often start with delivery metadata, documentation and interviews. Implementation usually needs access to the relevant repository and tools. We agree the minimum access needed before starting.
You can review my professional background on LinkedIn. Further project details or references may be available, subject to client permission and confidentiality obligations.
Email me with the problem, your team and your preferred timing. We agree fit, scope, rate, a limit on hours, paid meeting time and availability before work starts. For a defined project, we also agree deliverables and acceptance criteria. If you use the optional diagnostic, you can separately request the measurement framework by email.
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 can start directly with part-time consulting or a scoped implementation. The four-week Transformation Assessment is a separate option for organisations that need an independent baseline and roadmap before a larger investment.
This is direct specialist consulting. If you need a fully outsourced delivery team or continuous operational cover, that requires a different staffing arrangement. We should agree the ownership and capacity your team can provide before taking on the work.
Three self-serve assessments and frameworks drawn from the same work. No call required.
A 40-point self-assessment informed by NIST AI RMF 1.0, OWASP GenAI LLM Top 10 2026, ICO guidance, and practical delivery controls.
Run the assessment →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 →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 →Tell me about the problem, your team and your preferred timing. We can discuss regular consulting, a focused implementation or an assessment when you need a wider roadmap.
[email protected] · Scope and availability agreed before starting