AI is useful when it removes work without making the result harder to trust. That sounds straightforward until a team tries to measure it. A draft produced in seconds may still take ten minutes to check. An assistant that answers most questions may create extra work when nobody can tell which answers need correction.
We start with the task and the person responsible for its outcome. What are they doing now? Which part takes time? What would they need to see before accepting the result? Those questions usually produce a more useful brief than a request to add a chatbot.
Look at the work before choosing the interface
Consider a purchasing team that receives supplier updates in different formats. Someone reads each message, finds the order number and proposed delivery date, then updates a record. There may be a useful role for a model in interpreting the message.
The date comparison itself can remain ordinary code. So can checking whether the order exists and whether the user has permission to change it. The model’s job is the part that needs interpretation; the surrounding application can enforce the rules the business already knows.
A first version could show the proposed update beside the original message. A member of the team confirms or corrects it. That gives you a way to observe whether the extraction saves time and which message formats cause mistakes, before letting the system change records automatically.
The interface might be a review queue inside existing software. People should not need a conversation to accept a delivery date they can already see.
Separate an answer from an action
A summary and a change to a business record have different consequences. A team may tolerate an imperfect draft that remains private. Sending that draft to a customer or using it to approve a supplier requires a different decision.
For each proposed feature, describe what the software can read, what it can prepare and what it can change. Identify the person who can authorise each consequential action. This is easier to inspect than a broad instruction telling the agent to be careful.
An approval screen should show enough context to make review meaningful. If a supplier’s bank details are changing, presenting only a green confirmation button is inadequate. The reviewer needs to see the proposed change and follow the organisation’s established verification process. A model-generated explanation cannot replace that process.
Treat incoming documents and messages as information to interpret. Instructions found inside them should not be allowed to redefine the application’s permissions or the user’s request. Keep the authority to perform an action in the application, where it can be checked and recorded.
Make the review cheaper than the original job
Imagine an internal assistant preparing an exploration-project update. It has access to field notes, a sample register and previous reports. A fluent paragraph alone gives the reviewer more reading to do. A draft linked to the exact source records gives them a route to checking it.
Show missing information plainly. If an assay result has not arrived, the assistant should preserve that gap rather than produce a plausible completion. Distinguish the date of the source from the date the summary was generated; a current-looking answer can still be based on an old record.
This is a proposed workflow, not a claim that a model can judge geological significance or approve market disclosures. The software can help assemble material while the accountable specialists decide what it means and what may be published.
Measure how often a reviewer has to reopen the source, correct a field or discard the draft. If the assistant merely relocates effort from writing to checking, revise the task or stop the experiment.
Give the trial a fair comparison
Run a pilot against the existing way of working. Use comparable tasks and include awkward cases, such as incomplete messages, conflicting dates and requests that belong to another team. Keep a separate set of examples for checking changes so that improving the system does not simply mean tuning it to the demonstration.
Record the time spent reviewing and correcting results as well as the model’s response time. Include the cost of running the service and maintaining the workflow. A saving on the first attempt may disappear after retries, follow-up questions and manual reconciliation.
Some mistakes matter more than others. A slightly clumsy sentence and a wrong account reference should not receive the same treatment in an evaluation. Agree which errors make the result unusable before interpreting an overall success rate.
Anthropic’s Building effective agents distinguishes predefined workflows from systems in which a model chooses the steps. Its guidance favours starting simply and accepting extra complexity only when the task benefits. For a fixed review process, that supports testing a bounded workflow before introducing a more autonomous agent.
Know where to leave ordinary software in charge
When the inputs and rules are already explicit, a model can add uncertainty without removing much work. Calculating an invoice total, enforcing an access rule or checking that a required field exists usually belongs in code that can be tested directly.
Ambiguous requests need a useful fallback. The system might ask a clarifying question, leave a field empty or send the task to a person. Choose the fallback according to the consequence of being wrong. An empty suggestion can be a better result than an answer that sounds complete but cannot be supported.
There are also tasks where review is too difficult for the proposed benefit. If the only qualified reviewer must independently repeat the entire analysis to trust it, a narrower use may work better. Let the model organise the documents or find relevant passages while the specialist performs the analysis.
Decide what would justify expanding it
Before a pilot begins, agree what evidence would support a wider rollout. That might be fewer manual corrections for a defined class of messages, faster preparation with unchanged review quality or fewer enquiries waiting for routing. Also agree what would cause the team to pause it.
Give someone responsibility for reviewing the system when source material, business rules or model behaviour changes. A workflow that was useful during the pilot can become less useful when the company introduces a new product or changes its approval policy.
Pick one repeated task this week and sit with the person doing it. Write down the input, the expected output and how they know the work is correct. If you cannot describe the check, the next step is to understand the job better before automating more of it.
Explore AI agent development for the implementation work behind a bounded, reviewable workflow.