Two kinds of complicated, at the same time

I'm leading product development for a complex platform that brings together messaging, cryptocurrency and digital banking in a brand-new EU regulatory context.  This means that while there is much that is known, and for which best practices exist, there is also a big part of the work that has no guardrails.  The work runs across mobile and backend engineering, design, analysis and QA. I'm responsible for product direction, delivery roadmap, and stakeholder management across four different layers.

Alongside that, I'm leading AI adoption strategy across the software delivery lifecycle: identifying high-value opportunities, prioritising implementation and coordinating practical improvements with delivery teams.

This is strategic and implementation leadership, not personal experimentation with new tools. The work is finding, inside real delivery environments, where AI genuinely helps and where it simply adds another thing to manage.

While I can't publish productivity gains yet, my strategy is this: outline the company's direction with AI -- where they want to go and what they're not willing to sacrifice on the way, divide the product lifecycle into individually automatable steps, then lay the groundwork for step-by-step automation, measuring metrics and reporting outcomes.