Insights

What it takes to put AI to work.

Field notes from real engagements: how the work was scoped, where the team needed control, what changed, and how we measured it.

Latest note

What AI implementation involves beyond the demo

A practical implementation model for moving from a promising AI demonstration to a controlled business workflow with accountable ownership and measurable outcomes.

Implementation · July 2026 · 3 min read

Measurement · June 2026 · 3 min read

How to evaluate whether an AI workflow created value

Use an observable process measure before and after implementation: response time, manual effort, follow-up completion, or turnaround time.

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Implementation · June 2026 · 3 min read

Five reasons AI implementation projects stall

The recurring obstacles are usually operational: unreliable inputs, unclear ownership, disconnected systems, weak adoption, and pilots with no decision rule.

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Method · May 2026 · 3 min read

Start with the workflow, not the model

Tool selection matters, but it comes after the work is understood: trigger, inputs, decisions, ownership, and the result the workflow should improve.

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Results · May 2026 · 4 min read

Five implementation notes, measured in operational terms

Examples from recent engagements: faster inquiry handling, less manual study setup, reliable inventory synchronisation, and reactivation work that used the data already available.

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Safety · April 2026 · 3 min read

Approval rules, source controls, and audit trails

The operational controls behind a reliable AI workflow: defined approvals, approved information sources, escalation rules, and records a business owner can review.

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Trends · April 2026 · 3 min read

A practical filter for business AI investments

A way to distinguish useful AI capability from product-news noise: start with the workflow, the operating constraints, and the evidence required to continue.

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