How It Works

The Process Behind Every Tool on This Site

Not magic. Not automation. A repeatable process for turning ideas into tested solutions in days.

1

Identify

2

Define

3

Prototype

4

Test

5

Ship or Kill

01

Identify the Problem

Not "we need a feature."

A real pain point you face as a delivery practitioner.

Example from my work:

"Sprint planning takes 4 hours because we can't split oversized user stories consistently."

The test: Can you explain the problem to a colleague in one sentence?

If you can't, you don't understand it yet.

02

Define Success

What does "solved" look like?

Not vague aspirations. Specific outcomes.

Example:

"A tool that takes a large user story and suggests 3-5 smaller stories using proven splitting patterns. Output should be copy-pastable to Jira."

The test: Would you know success if you saw it?

If not, refine your definition.

03

Prototype with AI (1-2 days)

Tools I Use:

  • Perplexity Pro — Academic research, framework discovery ($20/month)
  • Claude Code — Primary building tool ($20/month)
  • Claude CLI — Command-line interface for rapid iteration (free)
  • Gemini AI Pro — Comparative reasoning + NotebookLM for ingesting many documents at once ($20/month)

Total cost: ~$60/month. Not expensive. Just strategic.

What I Ask AI to Do (example prompt):

"Build an HTML form where users can input a user story. Show 6 splitting pattern options (workflow steps, business rules, data variations, operations, performance, platforms). When user selects a pattern, generate 3-5 smaller stories based on that pattern. Make output exportable to CSV."

What AI Assists With: Drafting HTML, CSS, JavaScript, and implementation options.

What I Direct: Workflow logic, acceptance criteria, testing, prioritization, and delivery judgment.

This is the same process I used as a Product Owner for years — just faster. I define the workflow, decisions, and success criteria. I test what comes back. I iterate until it is useful. The only difference is that instead of waiting weeks for a development cycle, I can validate a working version in minutes.

Average iterations per tool: 10-20

Time per iteration: 5-20 minutes

Total time: 1-2 days of focused work

04

Test with Real Users (1 day)

Show it to 3-5 colleagues.

Don't explain it. Just watch.

What to observe:

  • Do they understand it without explanation?
  • Do they get stuck anywhere?
  • Does the output match what they need?
  • Do they actually use it or just say "interesting"?

Take notes. Don't defend your choices.

User confusion = design problem, not user problem.

Iterate based on real feedback, not assumptions.

05

Ship or Kill (immediate)

Ship if:

  • Solves the problem
  • People actually use it
  • Feedback is positive
  • Maintenance is manageable

Kill if:

  • Doesn't solve the core pain point
  • Too complex for the value
  • Better solutions already exist
  • Requires ongoing maintenance you can't provide

No attachment to ideas. Attachment to outcomes.

Some of my killed projects:

  • Backlog health analyzer (too subjective)
  • Automated meeting scheduler (too many edge cases)
  • Requirements quality scorer (people gamed the metrics)

Killing bad ideas fast is a feature, not a bug.

Real Examples

Ignite Intelligence v15

Problem: No off-the-shelf platform existed for an operator-grade command centre that fulfils my personal requirements and use cases. So I built one — with autonomous agents and structured morning briefings.

  • Months 1-2: Architecture from first principles using Claude Code — agent registry, autonomy tiers, model selection
  • Months 3-4: Build the orchestration layer (Olympus Chairman, Jarvis dispatch) and core agents (Finance, Opportunity Scout, Brain Coach, Coder, Researcher, Outreach, System Guardian)
  • Months 5-6: Real-time telemetry, FastAPI + MongoDB + Qdrant integration, WebSocket realtime channel, scheduled routines
  • v15: Production deployment. Multi specialist agents in daily use. Chairman briefing every morning.

Shipped: Multi-agent autonomous platform in production daily use. The skill was systems thinking, not syntax.

User Story Slicer

Problem: Sprint planning wasted 4 hours on story splitting debates

  • Day 1: Researched story splitting patterns (Richard Lawrence's work)
  • Day 2: Built HTML tool with 6 patterns using Claude Code
  • Day 3: Tested with my team
  • Day 4: Refined based on feedback
  • Day 5: Shared with 5 colleagues outside my team

Shipped: Free tool, available to community.

Stakeholder Map Generator

Problem: 50+ stakeholders on banking merger, unclear communication strategy

  • Day 1: Designed Power/Interest matrix framework
  • Day 2: Built interactive tool
  • Day 3: Tested on real project stakeholders
  • Day 4: Added communication plan automation

Used on: large-scale project with 73 stakeholders.

This Website

Problem: Portfolio needs to prove product thinking + AI execution capability

  • Week 1: Positioning strategy
  • Week 2-3: Content creation across all pages
  • Week 4-5: Build using Claude Code
  • Week 6: Testing and refinement (300+ iterations)

Shipped: This site you're reading.

The Division of Labor

What AI Assists With:

  • Drafts HTML, CSS, JavaScript, and implementation options quickly
  • Helps assemble forms, dashboards, workflows, and calculators from defined requirements
  • Applies changes when I describe the exact behavior or acceptance gap
  • Reduces the technical delay between idea and prototype

What I Direct:

  • Decide what features to build
  • Prioritize competing stakeholder needs
  • Translate business strategy into usable workflows
  • Say no to weak feature requests
  • Make product decisions and validate the output

AI is a tool. Like Excel is a tool for financial analysts. Like Figma is a tool for designers.

AI is the prototyping tool for Product Owners.

How the work actually gets done

What I use:

Research & reasoning: Perplexity Pro · Gemini AI Pro (NotebookLM)

Build & execution: Claude Code · Claude CLI

What these tools don't do: they don't decide what to build. They don't write the acceptance criteria. They don't decompose ambiguous business need into a structured spec, or hold the architecture in working memory across months of build, or know when to stop.

That's the work. The tools execute against a brief. The brief is mine. The architecture is mine. The judgment is mine. Eight years of $50M+ enterprise delivery exposure across banking, insurance, government, and core platform programs is what shapes the brief these tools receive.

Ignite Intelligence v15 — a multi-agent autonomous operations platform — was architected from first principles by a Business Analyst and Product Owner. 1 hour a day for 6 months. The discipline is Business Analyst / Product Owner. The output is AI infrastructure. Same skill, new surface.

Your Turn

Try this experiment this week:

  1. Identify one repetitive problem you face as a delivery practitioner
  2. Spend 1 hour with Claude Code, Claude CLI, or Gemini AI Pro
  3. Test it with one colleague
  4. Iterate once — fix the biggest confusion point
  5. Decide: Ship or Kill. If it works, share it. If it doesn't, try a different problem.

Useful AI-assisted prototypes still need product thinking: know the problem, describe it clearly, define acceptance criteria, test, iterate, and make strategic decisions. That's Product Ownership.

Try the Tools View Portfolio

Download the Complete Guide

The AI Execution Guide (PDF)

Complete step-by-step process including:

  • • Problem identification framework
  • • How to describe what you want to AI (with examples)
  • • Testing checklist
  • • Ship/kill decision criteria
  • • 15 real examples from my work
  • • Common pitfalls and how to avoid them
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