Clear thinking on data & AI — without the hype.
Practical writing for growing businesses weighing up automation, analytics and AI. What actually works, what quietly fails, and where to start.
LatestStart with the bottleneck, not the tool
Most AI projects fail because the tool gets bought before the problem is defined. How to find the bottleneck first — and when the answer is no AI at all.
Before you switch on an AI agent
Agents work — the projects that get switched off skipped three decisions: what it may do, who checks it, and what it logged. The gates to set before you deploy.
An AI assistant is only as good as what it's allowed to read
An AI assistant is only as good as the documents it can read. How retrieval-augmented generation (RAG) grounds models in your data and cuts hallucinations.
A dashboard nobody opens is a cost, not an asset
Business intelligence is shifting from reports you read to decisions you act on. Why dashboards go unused, and what good, decision-driving BI looks like in 2026.
When AI projects fail, the data is usually to blame
Most failed AI projects fail on data, not the model. The difference between 'clean' and 'AI-ready' data, and the unglamorous fixes that make AI actually work.
Most companies are using AI. Far fewer are getting anything back from it.
Most companies have adopted AI, but few see real returns. Why pilots stall, what separates the winners, and how smaller Canada firms can actually benefit.
Turn the ideas into outcomes.
Book a free diagnostic and we'll tell you, honestly, whether data and AI can move a number you care about.