REALTOR.ca already runs on AI — just unevenly, invisibly, and without a way to prove or scale what works. This plan recovers the value, controls the risk, and pays for itself in the first year.
Teams build local automations and find genuine gains. The momentum proves the value — but fragmentation caps it and raises risk at the same time.
No shared prompts, skills, or templates. Every team re-solves problems others already cracked, and quality swings by person.
Spend is uncontrolled and value is anecdotal. Nothing is baselined, so nothing can be proven, attributed, or scaled.
No acceptable-use standard, no review gates, no data-exposure controls. Shadow AI grows faster than governance.
Start with low-risk capacity recovery that funds the program. Layer in revenue bets only once the capability is proven.
Recover time and lift quality in existing workflows — documentation-heavy, repeatable knowledge work. Fast, low-risk, self-funding.
AI-enabled products and data services. Validated bets, sequenced after the capability is in place — not day-one work.
The lasting value isn't any single tool or model. It's the harness — shared context, skills, policies, routing, telemetry and cost controls that every team reaches through the interface that fits its work.
M365 Copilot, Copilot Studio, Power Automate, Claude Code, GitHub Copilot, custom HTML — all governed entry points, not disconnected experiments.
A Git repo versions context, prompts, skills, policies and evals under PR review. It holds no secrets and grants no privileged access.
Delegated identity, least privilege, sensitivity labels, DLP, and per-task telemetry feeding one executive view of value, cost and risk.
Each builds on the one before it: assess, map, design the asset, sequence the work, then commit the investment.
The honest baseline: how AI is used today, the fragmentation problem, and the measured capacity at stake.
Where value sits across functions — scored productivity wins plus the strategic revenue bets.
The reusable control plane — entry points, repo-as-source-of-truth, governance and telemetry.
The 180-day adoption plan and parallel harness build — pilot, prove value, scale.
The lean team model, the cost-vs-value math, and the four decisions requested from the CEO.
A 5-FTE central team plus ~15 embedded champions — existing employees at 10–20% capacity.
Each is reversible and measured. The risk of waiting is continued fragmentation, duplicated spend, and unmanaged data exposure — with no view of cost or value.
A central AI Adoption Team starting at 5 FTE, plus a ~15-person champions network.
Six to eight prioritized use cases, baselined before/after, with an executive readout.
Tier-1 productivity workflows plus engineering enablement — the fastest, safest wins.
The recurring productivity value covers the central-team cost 1.6–3.2× before counting delivery speed, quality, or revenue upside. The capability is self-funding from the floor.