AI-SDLC Transformation Assessment
Phase 1 of the AI-SDLC Transformation: a fixed-scope 4-week engagement for engineering organisations already using coding AI and needing a governed path from individual adoption to organisational, increasingly agentic delivery. It ends with a costed roadmap for the build phase, which you are free to run with your own people.
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 no quantified findings are emerging by the end of Week 2, you can stop the assessment and only pay for time invested.
What follows the assessment
Phase 2: 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.
Phase 3: 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.
- Ongoing, reviewed quarterly; you can stop after Phase 1.
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 Transformation Architect. The work behind this assessment is enterprise Copilot adoption in a 14,000+ engineer environment, agentic mainframe-modernisation planning, and a governed multi-agent operating model. The engagement applies that same lens: maturity, gates, measurement, and a safe next step toward higher autonomy.
Ready to test fit?
View a redacted decision pack, or answer four questions, about 60 seconds, then direct calendar access for qualified engagements.