23 / 33AI Strategy Consulting

AI strategy consulting in Canada, priced before it's promised

Canadian AI strategy consulting that scores use cases, kills the weak ones, and costs the rest.

Talk to an architectSee what's covered
  • CCIE and CCDE-led team
  • Building since 2021
  • Canadian data residency available
THE WORK

AI strategy consulting, three pillars, one operator.

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.

  1. 1

    Use case scoring

    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.

  2. 2

    Costing and ROI advisory

    Inference, GPU, storage, and rework priced per use case, not a benefit statement your board has no way to check.

  3. 3

    Operating model design

    Who owns models, who approves a deployment, who watches cost, and whether a centre of excellence earns its name at your scale.

THE PROOF

Built to last. Evidence over promises.

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

AI plans a Canadian board will actually fund.

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.

SMEnode · Engineering principle
  • CCIE Data Center
  • CCIE Security
  • CCDE Design
  • Canadian data residency
THE DEEP DIVE

AI strategy consulting in Canada, made real.

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.

Three use cases, not thirty.

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.

Price the run, not just the build.

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.

The Canadian picture, and the law that actually applies.

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.
Inference graph
THE METHOD

How our AI transformation strategy work runs.

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.

  1. Step 01

    Inventory

    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.

  2. Step 02

    Score and kill

    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.

  3. Step 03

    Cost the run

    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.

  4. Step 04

    Design the operating model

    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.

QUESTIONS

AI strategy consulting questions, answered straight.

Answers first, including which use cases we'd talk you out of. An architect takes the call.

A scored use case shortlist, each item costed for build and twelve months of running, plus an operating model. Usually five weeks. The most useful page is the list of what we recommend not doing and why, because that's the money you don't spend. Every number is one our engineers would have to deliver against.

Three columns: business value, data readiness, and effort to build. Data readiness eliminates the most candidates. If the data is scattered, undocumented, or nobody trusts it, the honest answer is that you have a data project first. We say so. Pretending otherwise produces the pilot that demos well and dies in production.

Probably not yet, and most firms your size don't. AI CoE design makes sense when you're running several models across departments and need shared standards. Before that it's overhead with a nice name. What you usually need is one named owner per model, a deployment approval step, and someone watching cost. We'll tell you which situation you're in.

This is the planning engagement: scoring, costing, and sequencing before anyone builds. The broader practice covers building and running the systems, from generative AI and RAG through to MLOps. Most clients start here, fund two or three use cases, and move into build. You can also take the plan elsewhere, and some do.

Canada has no AI-specific federal law in force right now. AIDA died at prorogation in January 2025 and nothing has replaced it, so anyone selling you compliance with a Canadian AI act is selling something that doesn't exist. What does apply is PIPEDA, Quebec's Law 25, and sector rules like OSFI guidance. We plan for the rules that exist and flag what's likely coming.

They get written up, not deleted. Each one carries the reason it didn't make the cut, usually missing or untrusted data, and what would have to change for it to qualify. Clients revisit that list when a data project lands or a vendor price moves. Killing a use case in week three costs a meeting. Killing it in month eighteen costs a budget.

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