AI-SDLC Transformation Assessment
A standalone, fixed-scope four-week engagement for engineering organisations that need an independent view of AI adoption, governance and delivery before a larger investment. It ends with a pilot design and a costed roadmap you can implement with your own team or with my support. Part-time consulting and scoped implementation are also available directly.
Questions the assessment answers
Maturity
- Where is the organisation on the seven-stage AI-SDLC ladder?
- Is use still individual, or is it embedded into workflows?
Governance and agents
- Are custom agents distributed through a governed path with human gates?
- What would block a safe move to agent-initiated work?
Measurement
- Can leadership see activation, surface adoption, and user-initiated versus agentic work?
- Which workflows produce business value, and where do humans still have to intervene?
Decision
- What should the organisation scale, fix, or stop?
- Which governed pilot is the next safe increase in autonomy?
What you receive
Maturity assessment
Placement on the seven-stage AI-SDLC ladder, with evidence.
Bottleneck analysis
Where AI is adding work rather than removing it from the delivery system.
Governance and risk
Policy, review gates, security, and accountability gaps.
Agentic opportunity map
Role-based workflows that can run with human gates.
Measurement framework
The eight AI-value families, not adjacent operational noise.
Governed pilot + roadmap
One safe next workflow, plus 30/90-day scale / fix / stop actions.
Process and expected inputs
Maturity baseline
Adoption, surfaces, communities, agents, and current gates.
Bottlenecks
Where AI work stalls: review, security, accountability, missing operating model.
Opportunity and measurement
Agentic workflows, human gates, and the AI value system.
Pilot and roadmap
Governed pilot design, executive pack, 30/90-day actions.
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.
Optional follow-on work
Transformation Build
- Governed pilots run on real workflows, not sandboxes.
- Role-based agent patterns, review gates, and policy put in place.
- Measurement wired into delivery data; first team leads trained to run it.
- 8–12+ weeks, scoped and costed by this assessment.
Scale & Optimise
- Rollout across the engineering organisation through trained champions.
- Operating model, metrics review, and coaching cadence established.
- Principal-led, delivered through your own engineers, not consultant headcount.
- Optional quarterly scope after the build; no default retainer.
Fit criteria
Good fit
- AI coding tools are already deployed or expanding.
- Delivery metrics are not moving as clearly as adoption metrics.
- Leadership needs evidence before renewal, audit, due diligence, or board reporting.
Not a fit
- You need generic AI training or prompt workshops.
- You have not deployed AI coding tools yet.
- You want body-shopped delivery capacity rather than an operating model your own engineers run.
Why this work is specific
Matt Drankowski is an AI-SDLC Consultant & Cloud Architect with 15+ years across software delivery, platform engineering, DevSecOps and cloud. His delivery experience includes enterprise AI adoption and a completed Azure DevOps-to-GitHub platform migration. The modernisation and agent-workflow materials describe approaches he can apply to a new engagement. The assessment turns your own delivery evidence into priorities, a pilot design and a costed roadmap.
Ready to test fit?
View an illustrative assessment finding, or answer four questions in about 60 seconds to see your result immediately. Leave your email if you would like the matching framework: I will review your answers and send it within one working day. No call required.