[insights]

Practical notes
from delivery

Short pieces on what actually moves AI from activity to outcome. Written from engagements we've been in, not from analyst reports.

Six reasons pilots stall between demo and deployment, and what to decide before the first line of code.

How to set a target metric, a control, and a kill criterion so an AI initiative can be judged in the language of the business.

Why architecture, governance, and unit economics belong before a prototype, not after it.

What changes when an agent leaves the notebook: evaluation, observability, guardrails, and ownership.

How the accelerator turns fragmented customer data into an actionable view without a multi-year data programme.

What the regulation means in practice for organisations deploying AI in Europe, and how to build so compliance is inherited rather than retrofitted.

Ready to move from pilots to P&L?

Tell us about the decision or workflow you want to change. We'll come back with an honest view on whether it's worth proving, and what it would take.