AI Automation Projects: Three Useful Trials With Clear Checks

Useful AI automation projects start with a small, repeatable task whose output a person can verify. Three practical trials are sorting incoming requests, extracting fields from documents, and drafting a weekly recap from approved work updates.

Give each trial a responsible owner, a clear stopping point, and a way to compare the result with the source. fenbs can hold the project tasks, agreed boundaries, and lessons from the trial, so the next assistant starts with what you learned rather than repeating the same mistakes.

Which project should you try first?

Choose the task with the clearest checking method, even if another idea looks more impressive. Someone should already understand the work well enough to say whether the result is usable. Start with approved sample material and keep consequential actions outside the trial until you know how preparation behaves.

Avoid choosing a project only because data is easy to obtain. The task must also be useful, allowed, and maintainable. Our guide to AI for project managers covers the wider role; the trial here should answer one narrow question about preparing a particular output.

How can you trial a request sorter?

Say a design studio receives requests for new work, changes to existing work, and help with delivered work. Give the AI redacted examples and the categories your team already uses. Ask it to suggest one category, explain which words support that choice, and flag requests that do not fit.

• Input: approved sample requests, category definitions, and examples of ambiguous wording.
• Output: proposed category, supporting text, and any question that prevents a clear choice.
• Check: compare with the intake owner’s classification and inspect disagreements.
• Boundary: produce suggestions only; do not assign live work or send replies.

Include a request that belongs to two categories and one that lacks enough detail. The useful behavior may be asking for clarification rather than forcing a label. Record whether a mistake came from unclear definitions or misreading the request. Those need different corrections: improve the category rule in one case and improve the model instructions or abandon that use in the other.

How can you trial document-field extraction?

Choose one document family and a small set of fields, such as invoice reference, document date, and purchase-order reference. Use non-sensitive or redacted samples. Require the proposed fields to stay beside the original document, so a reviewer can compare the output without hunting for its source.

As a product example, Microsoft AI Builder document processing↗ (opens in a new tab) extracts document information for use in Power Automate and Power Apps. That establishes a possible preparation method; your trial still needs to show whether it works on the documents you actually receive.

• Input: approved documents with ordinary layouts, missing fields, and difficult examples.
• Output: candidate values and a clear marker when a value is missing or unreadable.
• Check: compare every required field with the original, including dates and leading zeros.
• Boundary: no payment approval, accounting-system update, or disposal of the source document.

Name the person who owns correction and decide where corrected values belong. If reviewers repeatedly fix the same field, keep examples of that problem in the trial record. Do not call an extraction successful merely because the output has the right number of fields.

How can you trial a weekly work recap?

Give the AI current, approved task updates and ask for three sections: completed work, work still in progress, and questions waiting on a person. Require a source reference for each important statement. Make the project lead responsible for checking the draft before it reaches colleagues or clients.

Include an update that says a task is built but untested. The recap should preserve the remaining check. Include a proposed date that no one has approved; the draft should present it as a proposal. These examples show whether the assistant preserves the meaning of progress rather than simply producing smooth prose.

Measure how much editing the project lead needs, including looking up missing references. A shorter writing session may still create extra work if the summary blurs uncertainty. The AI project management explainer covers the wider coordination question. Keep this project focused on drafting the recap from a known set of updates.

How do you run a trial that teaches you something?

Define success before showing the assistant the evaluation examples. Write what a usable output contains, what causes rejection, and how much correction effort is acceptable for this task. Keep some examples separate from the ones used to improve the instructions, so you can check whether the changes help on unfamiliar inputs.

1. Record how the owner currently performs the task, including checking and correction time.
2. Run the AI preparation step against a fixed set of approved sample inputs.
3. Compare each result with the agreed outcome and record mistakes by type.
4. Change one part of the instructions or scope, then check the separate examples.
5. Ask the owner to continue, narrow, or stop the trial based on the complete effort and result.

NIST’s AI Risk Management Framework↗ (opens in a new tab) addresses trustworthiness in AI design, use, and evaluation. For a small project, assess the surrounding process too: whether the source stays available, whether review happens, and whether someone can stop the work. Our guide to managing AI projects covers the broader management work.

How to do this with fenbs

1. Create a free account and add a project for the single trial you chose.
2. Add tasks for sample preparation, the first run, and review. Give each task a plan that names the expected output and check.
3. Record the boundaries a person approved in Decisions and rules, including which live actions remain outside the trial.
4. Connect a supported assistant from Integrations and ask it to read the rules and current project context.
5. Save the trial result, a useful lesson, and Where we left off, including the next test or the person whose decision is still needed.

fenbs keeps the decisions and lessons; it does not run document extraction or execute the project’s automation. A single throwaway experiment may need only a short document. Use fenbs when the trial will continue across sessions or its lessons need to inform later work.

What should you keep after the trial ends?

Keep the tested scope, the important failure examples in their approved source system, the reviewer’s conclusion, and the reason for continuing or stopping. Turn repeated mistakes into specific lessons. A useful lesson says which input caused trouble and what to check next time, rather than simply saying that AI makes mistakes.

If you continue, widen one boundary at a time and check the result again. The AI-driven automation guide covers handoffs during ongoing use, while the AI at work hub points to specific workflows. Your first project has done its job when its result tells you what to try next.