Now
Where I am now — and where I want to go next
My current work combines product and operations leadership with hands-on AI-assisted implementation. I use this capability to turn operating requirements into clearer mechanisms, analytical tools, and controlled workflows.
Current practice
I already use AI-assisted and spec-driven development to build deterministic Python tools for regular operational work. The tools connect data collection, analysis, forecasting, planning transformations, and downstream integrations across weekly, campaign, and long-term planning horizons.
AI supports requirements development, issue investigation, specification, implementation planning, and controlled changes. The calculations and business rules remain deterministic, and unresolved exceptions are stopped for human review rather than being resolved automatically.
This extends my leadership from setting direction and building operating mechanisms into implementing parts of the solution directly. A review-support agent for unresolved planning exceptions remains on the roadmap; it is not yet implemented.
What I am exploring next
The next stage is to deepen how experienced product and operations leaders can combine domain expertise, coding assistants, agents, and lightweight software development without weakening accountability or human judgment.
That includes questions such as:
- How can operational knowledge be translated into reusable analysis logic?
- How can agents investigate a problem without losing context or human oversight?
- How can coding assistants help non-engineering operators move from an idea to a working internal tool?
- How should teams divide work between analysis, interpretation, strategy, and execution?
- How can AI improve decision-making without creating false confidence or removing accountability?
- What does an effective AI-native operating model look like inside a large organisation?
My direction remains work that sits between domain expertise, product and operations leadership, and hands-on implementation.
Not AI as a separate specialism, but AI as a new layer of operating capability.
Current areas of exploration
- AI-enabled logistics analysis
- Forecasting and planning-quality systems
- Adaptive network design
- Agentic research and reporting workflows in independent projects
- Spec-driven development using Codex in independent projects
- Review-support agent design for unresolved exceptions (roadmap)
- Human judgment in increasingly automated organisations
- Slime mould and other adaptive-system models as a lens for logistics networks