Most “bad AI output” is really a bad‑brief problem. This guide shows how to frame goal, context, audience, constraints and output so your AI briefs consistently produce drafts that are usable on the first pass, not generic text you have to rebuild from scratch.
Most AI pilots in internal operations don’t fail dramatically; they quietly stop changing tickets, approvals or reconciliations. This piece shows three stall signals and how to redesign workflows, metrics and ownership so pilots become real operating models.
stalled-pilots-article Read More »
Most teams avoid the unglamorous workflow audit, but tracing what people actually do—step by step—before adding AI is the only way to build workflows that survive real‑world exceptions.
workflow-audit-article Read More »
Most teams get stuck in AI pilot mode; this piece shows how to design an AI operating model that turns one‑off wins into reliable, everyday work.
ai-operating-model-article Read More »
Most AI adoption failures do not stem from weak models, but from broken workflows at the human-AI handoff. This article explores how unclear briefs, missing governance, and unprepared teams cause high-potential AI systems to fail—and how redesigning workflows, prompts, and operating models can turn AI into a reliable operational partner.
The Handoff Problem: Why Most AI Rollouts Fail Before the Model Does Read More »
AI use case prioritisation for a mid‑size product organisation: moving from scattered AI experiments to a clear 12‑month roadmap with four high‑impact, governed use cases.
AI Readiness Audit: Prioritising Use Cases Across a Product Organisation Read More »
An AI readiness audit for product organisations: a practical, quietly opinionated look at how to prioritise AI use cases, redesign workflows, and turn scattered experiments into an adoption plan that teams can actually use.
AI Readiness Audit: Prioritising Use Cases Across a Product Organisation Read More »
