Canadian AI strategy consulting that scores use cases, kills the weak ones, and costs the rest.
One Canadian team scores the use cases, then has to build them. The deliverable is an AI roadmap you can fund, not an AI business strategy nobody sequenced.
Every candidate gets scored on business value, data readiness, and effort to build, so the weak ones die in week three instead of month eighteen.
Inference, GPU, storage, and rework priced per use case, not a benefit statement your board has no way to check.
Who owns models, who approves a deployment, who watches cost, and whether a centre of excellence earns its name at your scale.
19.2% of Canadian businesses used AI to produce goods or deliver services in Q2 2026, up from 6.1% in 2024 (Statistics Canada, 2026-05-27). Most got there on three use cases, not thirty.
IN PRODUCTION
A workshop hands you eleven AI ideas. Score them honestly and seven have no data behind them, two cost more in inference than they'd save, and two are worth doing. The valuable part isn't the two. It's being told no with the arithmetic attached, by the people who'd have made money saying yes to all eleven.
Most AI strategy work fails the same way: nobody priced the running cost. This is written by the engineer who'd have to hit the number.
An AI business strategy worth funding names three use cases, not thirty. Each one carries the data it needs, the cost to run it for a year, and what happens if it doesn't work. Ranking use cases by excitement is how firms end up with a pilot nobody uses. We score on value, data readiness, and effort, and the data readiness column kills more candidates than the other two combined. If the data isn't there, the AI use case isn't a use case yet. It's a data project wearing a better title.
We price the run, not just the build, because that's where AI plans break. Risk sits in three places. Inference cost that scales with usage, so success gets expensive. A model that needs a human checking every output, which quietly removes the saving. And the pilot that works on curated data and fails on the real thing. Roughly 88% of agent pilots never reach production, and the most common root cause is unclear success criteria (source: Forrester and Anaconda research, 2026). We write the kill criteria before the work starts.
Canadian context is unusually favourable and worth naming. 19.2% of Canadian businesses used AI to produce goods or deliver services in Q2 2026, up from 12.2% a year earlier and 6.1% in 2024 (source: Statistics Canada, 2026-05-27). The top uses are data analytics, text analytics, and chatbots. On law: Canada has no AI-specific federal statute in force. AIDA died at prorogation in January 2025 and no successor has been tabled. What binds you is PIPEDA, Quebec's Law 25, your sector regulator, and your client contracts, applied to whatever data your models touch.
Four steps, and step two exists to disappoint people. Scoring a use case list honestly means telling a client that most of what came out of their innovation workshop isn't fundable yet, and we'd rather say that in week three than eighteen months into a programme. It's also why we insist on building at least one of them. A consultancy that only writes AI strategy never learns what its own advice costs. We do, because the engineers who priced the roadmap are the ones who have to hit the number.
Every candidate use case, where it came from, and who wants it. Then what data actually exists for each one, where it lives, and who trusts it. Most lists shrink here before anyone scores anything.
Value, data readiness, effort. We name what dies and why, in writing, and it's usually most of the list. The rejected page is the one clients circulate, because it's the money they don't spend.
Build cost, then twelve months of inference, GPU, storage, and human review. The second number surprises people, and it's the one that decides whether a use case survives contact with a budget.
Who owns models, who approves a deployment, what gets measured, and who sees the cost line monthly. AI CoE design if you need one, and an honest answer if you don't.
Answers first, including which use cases we'd talk you out of. An architect takes the call.
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