AI STRATEGY · ESSAY 02
Most AI conversations start with “which tool?”, but the real leverage comes from mapping one critical workflow first so you cut tool sprawl and make better product decisions.
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AI enablement
What most teams get wrong about AI adoption
Many teams approach AI adoption as a tooling upgrade rather than a systems redesign. This article breaks down the most common mistakes—from fragmented workflows to lack of governance—and explains how organisations can move from ad hoc experimentation to structured, scalable AI enablement.
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The Handoff Problem: Why Most AI Rollouts Fail Before the Model Does
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.
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Designing an AI-Assisted Content System That Scales With the Practice
A case study on building an AI-assisted content system for a professional services firm to scale thought leadership without overloading partners. By introducing structured templates, AI-assisted drafting, and streamlined review workflows, the system enabled faster publishing cycles, consistent quality, and a repeatable content engine aligned with high-trust expertise.
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Turning a Stalled AI Pilot Into a Working Content Workflow
A practical case study on building a human-led AI content workflow for financial services teams. This framework shows how to balance automation with editorial control, enabling faster content production, stronger governance, and scalable AI adoption without compromising quality or compliance.
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AI Readiness Audit: Prioritising Use Cases Across a Product Organisation
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.
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Claude Code Is Not Winning Developers With Better Chat. It Is Rewriting the Interface Between Code, Tools and Control.
Claude Code is not trying to win on prettier autocomplete. It is quietly turning the dev environment into a network of supervised agents, opinionated tools and tight permissions – a move that matters far more to real teams than another benchmark chart.
Codex after the hype: how an AI coder became a workflow engine
Codex has moved past “AI autocomplete.” This article shows how teams turn it into a supervised workflow engine, and why tests, access, review and logging decide who actually wins.
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