Your first AI project: what good support should make clear

What should you ask an AI agency for in a first project?

Ask for one clearly scoped task, the required information and connections, verifiable deliverables, trial criteria and a budget separating setup from running costs. Clarify approvals, human handover and post-launch support. A first trial should produce evidence that helps you decide whether to continue, adjust or stop.

Buy clarity that helps you move forward

A first AI project often brings several ambitions together: simplify support, make better use of documents, prepare proposals faster or reduce administrative work. Good support turns those possibilities into a next step people can understand.

You should be able to explain the problem being addressed, the people involved and the result you will review. That clarity makes proposals easier to compare without requiring you to become an expert in every technology.

A scope connected to real work

Ask for a description of the current task and the proposed process. It should identify the trigger, information sources, expected result and cases that remain with your team.

The scope needs to be specific enough to test. “Bring AI into operations” leaves room for many interpretations. “Prepare a summary from authorised notes and send it for review” gives the people involved something concrete to discuss.

Also check what is outside the first stage: additional connections, historical data preparation or support for particular applications. Clear boundaries make a proposal easier to understand and reduce mismatched expectations later.

Connections checked early

A demonstration may work with carefully prepared information. Your environment can have different constraints: scattered documents, varying permissions or software with limited connection options.

Ask which checks happen before feasibility is confirmed. Who provides access? Which information is needed? What happens if a connection is unavailable? The answers may change the first scope, and it is useful to find that out early.

This is also a chance to identify preparation your team will need to do. Updating a procedure or agreeing who owns a source can be part of making the project usable.

A trial that leads to a decision

Agree representative examples and a way to assess results. Include incomplete or unusual cases. Identify who will review the work and how much time they can give to feedback.

At the end of the trial, you should be able to continue, adjust or stop. A list of completed tasks is not enough on its own. Look at quality, correction effort and what the team could do with the capacity created.

Area to clarify	What to look for
Scope	An observable task and result
Access	Identified sources and permissions
Trial	Real examples and agreed criteria
Costs	Setup, usage, support and review time
Continuity	An owner and a workable handover

Costs explained over time

Separate implementation, the tools used, usage-based charges and ongoing support. Changes in scope should also have a clear process.

Ask how value will be assessed. Freed time can support better service or create capacity for more work. The assessment still needs to include the checking that remains and how that capacity could be used. A return-on-investment percentage without explicit assumptions offers little help when comparing options.

The same applies to schedules. Ask which dependencies affect delivery and which decisions your team will need to make. A plan is easier to use when responsibilities are visible.

A team that knows how to take over

Support continues through adoption, feedback and adjustments. Check where to report a problem, how to pause the process and who approves changes.

A good first project leaves you with something concrete: a better-informed decision, a process you can test and a team that understands its role. That is a useful foundation for what comes next, whatever the size of your organisation.

Explore our opportunity review for choosing a first use case.

The NIST AI RMF is a voluntary framework for examining AI risks across the system lifecycle. After scoping, explore the steps from prototype to everyday use.