Put AI where judgment can support it

A task can be difficult to perform and easy to check. Finding a particular clause in a long document may take time; checking a proposed passage against the original may be quick. That imbalance creates a useful opening for AI.

The reverse deserves caution. A fluent answer to an unfamiliar question may take more expertise to verify than you have available. The important question is what it costs to recognize a good result. AI becomes more useful when the surrounding work makes that judgment practical.

Choose work you can judge

For document intake, extracting a date with its source passage gives a reviewer something concrete to inspect. Asking for a definitive assessment of an unfamiliar contract gives them a much harder verification problem. Both may look like “document AI” in a proposal, but they require different expertise and tolerances for error.

This suggests a better first project: a familiar task with visible evidence, useful partial results and a manageable consequence when something is wrong. The tool can flag uncertainty instead of manufacturing completeness.

Separate the proposed answer from the action it might trigger. Preparing a record, approving it and sending it to another system are distinct responsibilities. That separation lets AI contribute useful work while the business controls when an uncertain result becomes an operational fact.

Compare the whole task

Count the preparation and checking as well as the generation. A draft produced in seconds may still require careful correction. If staff spend longer verifying it than doing the original work, the tool has moved effort rather than removed it.

Build a small evaluation set from representative examples you are allowed to use. Include missing information, contradictory instructions and cases where the correct response is to ask for help. Record what “good enough” means before reviewing the outputs. Compare against the present process, including its errors and delays.

We would also test a simpler approach. A template, search improvement or ordinary rules may solve the task with less uncertainty. AI earns its place when its flexibility is useful and the surrounding process can absorb its limitations.

Make review workable

Show the reviewer enough context to make a decision. For a document summary, that may mean links to the relevant passages. For a proposed update, it may mean a clear before-and-after view. A generic approval button is weak protection if the person cannot reasonably inspect what they are approving.

Define which cases are held for review and who handles them. Keep a way to correct the underlying record. If an error would be difficult to undo, consider keeping the tool advisory until there is stronger evidence that the full process works.

Consider the operating conditions

• Which information may enter the tool, and under what provider settings or agreements?

• Who maintains the instructions, source material and test examples?

• What happens when a provider is unavailable or changes its behavior?

• What is the cost per completed, accepted task, including review?

• Can the business pause the tool and continue operating?

The NIST AI Risk Management Framework is a useful voluntary reference for considering risks throughout design, use and evaluation. It does not replace a decision about your particular task. Our starting point is a bounded trial, a named owner and evidence that the result improves the work. Read human review for the approval design and measuring value for evaluating the return.

Resources for a working trial

Anthropic’s guide to building effective agents is useful when deciding between a predictable workflow and a more open-ended agent. Cloudflare’s Agents documentation offers an implementation path for stateful agents and connected tools. Neither choice removes the need to evaluate the task on your own examples. Begin with the smallest approach that can produce a useful, reviewable result.