Adapt your job to AI: improve one workflow and verify the result
Start with one permitted workflow where you can compare the output with a known standard. Define what improvement means and what must remain under human review. Knowing a tool’s name is less useful than showing that you can use it, detect errors and explain a decision.
Choose a bounded task
Use public, fictional or otherwise authorized information. Examples include drafting a meeting brief or classifying a small sample of non-sensitive requests. State the input, the intended output and the checks. Follow your employer’s policy before bringing any real workplace data into a tool.
Compare against a baseline
Illustrative test: prepare a short brief manually, then with approved assistance. Check completeness, factual accuracy and the time spent correcting errors. Faster drafting is not a useful improvement if it introduces unsupported claims or shifts more checking work to someone else.
Show the judgement, not only the prompt
Keep the task specification, sample outputs, errors found and corrections. Explain why one output was usable and another was not. LinkedIn’s 2026 report includes AI-related skills, but a relevant work example is more informative than adding “AI expert” to every profile.
Decide whether to expand the experiment
If the workflow passes your checks, discuss where it can be used and who remains responsible. If it fails, document the limitation. Never invent productivity gains or publish confidential examples to create a portfolio. Use your result to choose further learning tied to an actual responsibility.
Write an evaluation brief for the experiment
Name the permitted task, the information you can use, the expected output and the mistakes you will check for. Create a small baseline example so you can compare completeness and accuracy. Decide who needs to review the output before it is used and how you will document corrections.
Run the comparison on the bounded example and keep the result, including any failures. If the assisted version is quicker but less reliable, record that limitation. If it is useful, explain which checks made it acceptable. This gives you a credible learning example without claiming an unmeasured productivity improvement or exposing workplace information to create an impressive story.
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Sources and further reading
Editorial review: 13 October 2026. Examples are illustrative; results depend on the information supplied.