How to Prepare Your Business Data Before Building an AI Workflow

Collect examples from the real workflow

Useful examples include incoming documents, completed outputs, staff notes, customer messages, approval rules, and common exceptions. A few good examples from the real workflow can reveal more than a long list of tools.

The goal is to show how work actually moves. What enters the business? Who reviews it? What is copied, summarized, routed, approved, or sent? Where does the team slow down?

When those examples are clear, the AI workflow can be designed around real business activity instead of vague ideas about automation.

Define what the system should produce

AI projects become easier when the desired output is specific. The output might be a draft reply, a structured summary, an extracted data table, a task note, a report draft, or a recommended next step for staff review.

Collect examples of good outputs and imperfect outputs. Explain what makes them useful, what must be accurate, and what would make the output unsafe or unhelpful.

This gives the project a practical target. Instead of asking AI to help with everything, the team defines a narrow workflow where success can be reviewed.

Define what should stay human

Before automation starts, teams should decide which actions require approval, which outputs are drafts, which exceptions need escalation, and which decisions should never be automated.

This makes the AI workflow easier to trust. Staff know what the system is allowed to do, owners understand the risk boundary, and future improvements can be based on real review data.

For many Canadian businesses, the best preparation is simple: gather representative examples, write down the current workflow, define review rules, and choose one first workflow that is repetitive enough to matter.

Common questions