The fixed-scope 4-week Transformation Assessment, Phase 1 of the AI-SDLC Transformation, for organisations already using Copilot, Cursor, Claude Code, Codex, or internal coding agents. See whether individual adoption can become a governed, measurable, increasingly agentic delivery system, and get a costed roadmap for building it.
Read the 2-page assessment outline or validate the background on LinkedIn. No form, no signup to view the decision pack. It opens instantly, print/PDF-ready.
Most AI coding programmes measure seats, usage, and code output. The expensive problems sit between code generation and production.
AI increases PR volume and surface area. Review queues grow, senior engineers carry hidden workload, and lead time stays flat.
Policy, secure usage, data handling, and code-quality gates are often less mature than the AI-assisted workflows already in use.
Large contexts, model mismatch, agent loops, and unowned experimentation quietly consume budget before anyone sees the quarterly bill.
Leadership is being asked harder questions before renewals, procurement reviews, customer due diligence, and board-level scrutiny.
A 4-week fixed-scope engagement for engineering organisations already using AI coding tools. The output is a maturity reading, an agentic opportunity map, a measurement framework, a governed pilot design, and a costed roadmap. The build and scale phases then execute it, with your people or with me.
Two ways to get it. Pick whichever fits your process.
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These are representative examples of the type of evidence the assessment surfaces. Client-specific findings are redacted or validated under NDA.
Usage dashboards showed strong adoption. Flow analysis showed the bottleneck had moved to senior review capacity, with larger AI-assisted PRs increasing queue depth.
Decision supported: change review policy, PR sizing, and ownership before expanding licences.
Teams had guidance for AI-assisted coding, but delivery controls, dependency scanning, and reviewer expectations were inconsistent across repositories.
Decision supported: standardise AI-ready delivery controls before regulated customer due diligence.
Teams could not separate user-initiated assistance from agentic execution, or say how often a human still had to intervene. Leadership had usage counts, not workflow value.
Decision supported: install the eight AI-value families before expanding agent autonomy.
Developers felt faster, but the evidence did not connect adoption to delivery outcomes. The assessment reframed the renewal conversation around retained value.
Decision supported: continue, expand, constrain, or redesign the AI coding programme with evidence.
The assessment is designed for busy engineering leaders. Most work is async, tool-agnostic, and based on existing delivery data.
Map tooling, DORA signals, PR flow, AI usage, team structure, and current governance posture.
Identify where AI adds velocity into constrained review, testing, release, or security systems.
Review policy, controls, model selection, context usage, agent loops, and spend attribution.
Deliver executive summary, evidence, roadmap, and scale / fix / stop recommendations.
The four weeks exist so you can start without approving a transformation programme on day one. The decision pack costs and scopes the work below, and you enter each phase only after the previous one has shown its results.
Maturity baseline, delivery-system bottlenecks, governance gaps, and the target operating model. Ends with a board-ready decision pack and a costed roadmap.
Implementation, not recommendations. Governed pilots on real workflows, role-based agent patterns, review gates and policy, and the measurement system wired into your delivery data. The first team leads are trained to run it.
Rollout across the engineering organisation through trained champions, with the metrics review and coaching cadence that keep the operating model running once I am no longer in the room.
This assessment is not about whether developers like the tool. It checks whether AI-assisted coding is improving the delivery system: PR flow, review economics, quality controls, governance, and measurable throughput to production.
DORA shows delivery outcomes. This assessment connects those outcomes to AI adoption, review load, governance controls, and cost attribution so leaders can decide whether to scale, fix, or stop parts of the rollout.
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.
It is not for teams still debating 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.
View a redacted decision pack, read the assessment outline, or run the 60-second diagnostic for a reading on your own organisation.