How to Scope Your First AI Deployment

Providers can only give you a useful proposal if you give them a useful scope. This template is what experienced deployment teams ask for in the first call.

1. One workflow, stated in business terms

Write down the single workflow the AI will change, who does it today, how often, and what a good outcome looks like. "Answer tenant maintenance requests on WhatsApp within a minute and create a ticket in the property system" is a scope. "Use AI in customer service" is not.

2. The systems it touches

List every system the AI must read from or write to, with a note on API quality and who owns access. Integration is where most time goes. If one of the systems is legacy or on-prem, say so now.

3. The data it needs

Where does the knowledge live? Documents, tickets, databases, people's heads? How current must it be? Who is allowed to see what? Permission handling is a design decision, not an afterthought.

4. Stage and definition of done

Be explicit about the stage: prototype, pilot with real users, or production. For a pilot, define the user group, the duration and the metric that decides whether it goes to production.

5. Evaluation

How will you know it works? Collect 50 to 200 real examples of the task with acceptable answers. This dataset is the most valuable artefact in the project and you can start building it before choosing a provider.

6. Constraints

Security review requirements, data residency, model vendor constraints, budget range, timeline and who from your side is available and for how many hours a week.

7. Ownership after launch

Who owns prompts, retrieval, integrations and monitoring after the engagement? If the answer is "nobody yet", that is a scoping item.

Turning scope into requirements

The matching tool on this platform turns these answers into a deployment profile: required capabilities, team size, engagement model and technologies. Use it to compare providers on the same terms, then send the profile with your enquiry.