Decision snapshot.
| Problem | Requirements, platform notes and technical decisions are spread across documents and people. |
|---|---|
| Approach | Use focused AI-assisted reviews to surface questions, dependencies and tests before planning. |
| Ownership | I own the delivery framing and review path; engineers and platform specialists own technical authority and production implementation. |
| Result shown here | A reusable review process and illustrative output structure, not a claimed client efficiency metric. |
AI can shorten the distance between a messy input and a useful first draft. It does not make an incomplete source correct or replace the people who own the system.
Break the problem into focused reviews.
I regularly use Claude and Claude Code, including custom subagents and Agent Skills, for research synthesis, requirements interpretation, implementation questions, draft QA scenarios and structured documentation. I also use AI-assisted code and implementation review to identify questions for technical specialists.
What does the source say?
Extract requirements, assumptions, exclusions and unresolved decisions from available materials.
What must happen first?
Surface integration, data, consent, QA and approval dependencies for sequencing.
How would we know?
Draft acceptance criteria, negative cases, evidence needs and handoff questions.
Specialist work is not simply “accepted” because it appears in generated output. The point is to bring a better starting question to the right owner.
A generalized requirements-to-QA example.
Illustrative scenario: A CRM team wants to change who enters a lifecycle journey when status and consent data arrive from different systems. This example is synthetic and is intended to demonstrate a review format, not to claim that this exact engagement occurred.
| Area | Question / draft output | Owner to confirm |
|---|---|---|
| Source | Which system is authoritative for current status? | Data owner |
| Consent | Does opt-out always override inclusion? | Compliance / CRM owner |
| Timing | Can status arrive after a scheduled journey entry? | Integration specialist |
| QA | Test eligible, recently changed, opted-out and missing-ID records | QA and client UAT |
| Release | Name reconciliation evidence, monitoring and rollback owner | Delivery / operations |
The useful output is not a polished summary; it is a set of reviewable questions and testable conditions that specialists can accept, reject or refine.
Use explicit review gates.
AI-generated code observations may become investigation leads; they are not accepted as confirmed defects without reproduction, source evidence or developer validation.
Make useful patterns repeatable.
I also share reusable instructions and AI-assisted review patterns with colleagues for ticket preparation, issue triage, requirements questions and documentation. The principle is to preserve the review structure, not to turn one client's confidential material into a reusable prompt.
Useful reuse depends on small, maintainable instructions with an explicit input scope, expected output, validation step and human owner. A shared workflow is only valuable if another person can understand its limitations and use it safely.
What this page proves, and what it doesn't.
Described from my reported practice: regular use of Claude Code, subagents, Agent Skills, AI-assisted code review and team-shared workflows.
Illustrative: the sample lifecycle journey, table and review sequence. It is not a redacted production transcript, an AI product deployed for a client, or a measured time-saving experiment.
Not claimed: autonomous production changes, completed MCP implementation, a numerical productivity uplift or full delegation of technical validation to AI.
For an executable and testable example of my technical reasoning, open the Waypoint policy exercise →.
For evidence definitions and reported career metrics, see measurement context →.