An AI idea deserves more time when it improves the work without creating a larger problem elsewhere. Check whether it saves time, cuts cost, simplifies the process, reduces employee load, or improves quality. Count preparation, review, corrections, exceptions, and maintenance.
New technology creates a long list of possible projects. A meeting transcript could become a summary. Customer messages could be sorted. Reports could be drafted. Documents could be checked. Each example can look convincing in isolation.
The decision becomes harder when the demonstration ends. Someone has to prepare the input, check the answer, handle the unusual cases, and keep the workflow working after the tool changes. Those parts belong in the evaluation.
Record the Current Job
Measure the job before adding AI. How long does it take? Who touches it? Where does work wait? Which mistakes repeat? What does good output look like? A vague baseline makes every new tool look like an improvement.
You do not need a large research project. Follow five or ten normal cases. Write down the time spent, number of handoffs, common corrections, and problems people already know. The baseline gives you something honest to compare.
Choose one report, one support ticket, one invoice, or one meeting. Measure it from the moment the input arrives until the next person can use the result.
1. Does It Save Time?
Count the entire job. Include collecting the material, cleaning it, entering instructions, waiting for the response, checking facts, correcting language, moving the output, and handling failures.
A model may draft a summary in thirty seconds. If a person spends twelve minutes comparing it with the transcript, fixing names, and moving actions into the project system, the saving is smaller than the demo suggests. It may still be worthwhile. You need the full number.
Measure elapsed time and human attention separately. A job that runs unattended for ten minutes may be easier than a three-minute task that demands constant supervision.
2. Does It Cut Cost?
Start with subscription and usage fees, then add setup, integration, training, review, maintenance, and the cost of failures. A cheap API call can sit inside an expensive process.
Cost also includes duplication. Teams sometimes keep the old process while adding the AI process because nobody trusts the new one yet. That may be sensible during a trial. It should appear in the calculation.
Use a time period that matches the work. A monthly cost makes sense for recurring tasks. A one-time project may need a total setup cost. Include the cost of the person who maintains the workflow after the person who built it moves on.
3. Does It Simplify the Process?
Draw the steps before and after. Count handoffs, decisions, tools, queues, and places where work can wait unnoticed. A workflow can become faster at one point while becoming harder to understand as a whole.
Watch for new dashboards, copy-and-paste steps, exception folders, approval loops, and accounts only one person can access. Each item adds a place for the process to break.
Simplicity also affects recovery. When something goes wrong, can the person doing the work see the cause and continue manually? A system that saves two clicks but requires a specialist for every exception is fragile.
4. Does It Reduce Employee Load?
Removing repetitive typing can help. Moving the same effort into monitoring, correction, and exception handling produces a different result. Ask the people doing the job where their attention went.
Some work is tiring because it repeats. Other work is tiring because it demands vigilance without giving the person control. An automation may reduce the first kind and increase the second.
Look for interruption. If the AI produces work at unpredictable times or sends frequent low-quality alerts, it can fragment the day. Batch review may be better than constant notification.
5. Does It Improve Quality?
Define quality before the test. It might mean fewer missing fields, quicker response, consistent terminology, better traceability, fewer unsupported claims, or a clearer handoff. Pick measures connected to the job.
Do not let presentation replace accuracy. A well-formatted answer can contain the wrong date, invent a source, or omit the one exception that mattered. Check a sample against the original material.
Quality can improve in one area and fall in another. Standard language may make reports easier to scan while removing useful nuance. Faster replies may help customers while increasing the risk of an incorrect promise. Record the trade.
Use a Small Scorecard
For each question, write the baseline, the result after AI, the evidence, and any new problem. Avoid a single score too early. The notes explain why a number changed.
Before: 18 minutes per handoff. After: 11 minutes including review. Evidence: ten normal cases. New problem: names from handwritten notes still need manual checking.
One clear benefit can justify the project. It does not need to win all five categories. A quality improvement may be worth a small increase in cost. A time saving may matter during a daily bottleneck. Make the benefit explicit and check that the losses remain acceptable.
Run a Pilot That Can Disagree With You
Choose normal work, difficult work, and at least one case likely to fail. Decide the stopping rule before the trial. If the process misses a required field in more than two of twenty cases, for example, it returns for redesign.
Let someone who did not build the workflow try it. Builders remember unstated steps. A second person reveals what the process failed to explain.
At the end, decide among four ordinary outcomes: use it, change it, limit it to certain cases, or stop. “Promising” is not a decision.
Related framework: The NIST AI Risk Management Framework addresses benefits and risks across the lifecycle of an AI system. This five-question check is a smaller first filter, not a replacement for formal risk assessment.
Next guideBuild the System Around the AI Tool →