Map the work.
Follow a typical job from first contact to the final record. Look at repeated data entry, task volumes, staff time and the information handled at each step.
How it works
Understand it. Connect it. Keep it working.
Start with a working day and the people who know it. A clear process turns a broad idea into an agreed scope, tested connections and practical instructions for your team.
Build the connections.
Agree the scope, dependencies and acceptance checks. Connect the right tools, test the everyday process and prepare for the exceptions staff need to handle.
Prepare your people.
Walk staff through the systems they will operate. Provide hand-over training and written instructions. Add a team workshop when more practice would help.
Support and review.
Agree the workflows covered and response arrangements. Monitor connections, answer staff questions and compare performance with the original baseline.
Built into the approachYour team.
Your team.
Your decisions.
Useful automation needs clear ownership, suitable access and a way to check whether the result helped.
Human review.
Staff review AI-generated material before it affects customers, finances or other consequential decisions. Approval stays visible in the workflow.
Defined data handling.
Review the information needed, access permissions, vendors and processing locations before connecting systems. Consider suitable Canadian-hosted storage where available.
A baseline to compare.
Record task volumes and staff time before a build. Review later results over an agreed period so the business can assess what changed.
Before anything is builtMake the scope
Make the scope
easy to understand.
The written proposal sets out deliverables, responsibilities, price, third-party dependencies and the checks used to accept the work. You keep your data and software accounts.
A practical first step
Explore the auditFind the right workflow to improve.
Explore the AI Readiness Audit and see what the report gives you before deciding whether to build.