Your Best First AI Project Might Be the Most Boring Task in the Office
Before buying another subscription, test one repetitive task against a clear definition of useful. A fictional hotel example shows how.
The first AI project in a business does not need a launch event. It might begin with the same notice someone rewrites every week, a rough set of handover notes, or a routine reply that needs a careful first draft. A small experiment gives your team something concrete to evaluate before a larger commitment.
A fictional hotel example
Suppose a hotel needs a guest notice about planned maintenance. Staff already know the approved date, time, affected service, and contact point. The experiment is narrow: can an AI draft reduce rewriting while keeping every approved fact intact? It is not a project to automate guest promises or change operating policy.
The approved facts come first
Create a fictional or non-sensitive sample for the first test. Write down the facts an acceptable notice must preserve, the tone you want, and anything it must not promise. Give one person responsibility for checking the result. Follow the business’s existing tool and information-handling policies before entering any material.
A prompt for the test
“Act as a hotel communications assistant. Draft a guest notice using only the approved facts below. Keep it under 100 words, use plain English, and include the supplied contact point. Do not add compensation, alternate services, or completion guarantees. Ask if a required fact is missing. Approved facts: [insert a fictional sample].” The restriction is part of the assignment, and the reviewer still needs to check that the tool followed it.
A scorecard with observable results
Compare the AI-assisted draft with your usual process. Record how long drafting and review took, how many factual corrections were necessary, and whether a colleague could use the final notice. Keep the same sample task for the comparison. These are proposed pilot measures, not claims about how much time your business will save.
A stopping rule belongs in the plan
Decide what would make the experiment unsuccessful. For example, stop if the tool repeatedly invents a service promise, if checking takes longer than writing, or if the necessary data cannot be used under your policies. An honest decision to stop is a useful outcome. You have learned which part of the task needs a different approach.
Expansion happens after review
If the experiment works, document the prompt, review checklist, and responsible owner. Try another approved sample before involving more people. NIST’s AI risk management resources offers a broader framework for considering risks and responsibilities. A consultant can help define the pilot and assess the result, but a clear business problem should remain the starting point.