Why most AI pilots never reach production — and what the successful ones do differently
The gap between a working demo and a production system is rarely the model. It is integration, entitlements, evaluation and the question of who owns it on a Tuesday afternoon in eighteen months.
Read Perspective — Public sectorData residency for Canadian public sector AI: what is actually required
A practical read on where regulated data can and cannot go, which deployment patterns satisfy provincial requirements, and how to document it for a privacy impact assessment.
Read Technical — Enterprise applicationsPutting AI on top of an ERP without breaking the ERP
Extraction patterns, write-back safety, approval gates and why querying the transactional system directly is almost always the wrong answer.
Read Technical — Quality engineeringEvaluating AI systems: replacing spot checks with a measured baseline
How to build a maintained test set, choose scoring rubrics, calibrate an LLM-as-judge against human raters, and wire the result into your deployment pipeline.
Read Perspective — GovernanceThe governance work that speeds AI delivery up
Risk tiering, pre-approved control sets and platform-enforced policy. Why organisations with real AI governance ship faster than those without it.
Read Technical — Data platformsLakehouse or warehouse: choosing on workload rather than on fashion
An honest comparison for organisations with mixed BI and AI ambitions, including the cost profiles that rarely appear in vendor material.
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