Getting started
How to choose your first AI project
The best first AI project is often a small, familiar task. You already know what good looks like, you can recognize mistakes, and you can tell whether a new approach actually helps.
Start with friction you can describe.
Look for something that happens repeatedly: preparing a standard draft, sorting incoming requests, finding information in a known set of documents, or turning unstructured notes into a consistent format.
Describe the task without mentioning AI. Who does it? What do they start with? What must be true when they finish? If that description is vague, the project needs more discovery before it needs a tool.
Make sure you can recognize a good result.
Choose work where a person can evaluate the output without repeating the entire task. Drafting a response for review is a more manageable experiment than giving a system permission to make commitments to customers on its own.
Collect a small set of representative examples, including awkward cases. Write down what a good result must include and what would make it unacceptable. That gives you something more useful than whether a demo feels impressive.
Measure the whole workflow.
Record how long the task takes today and how often it happens. Include the time spent reviewing, correcting, and moving information between systems.
For the experiment, count setup, review, corrections, and ongoing tool costs too. An output that appears in seconds is not necessarily a faster process if someone needs twenty minutes to repair it.
Keep the first version contained.
Limit the inputs, the users, and the actions it can take. Use sample or anonymized information where possible. Keep a person responsible for reviewing the result before it leaves the workflow.
A first project should answer a specific question: can this approach improve this task under these conditions? It does not need to solve the entire business.
Decide what happens after the experiment.
Agree on the decision criteria before running the trial. If quality and effort improve enough to justify the cost, you have a reason to continue. If the benefit is unclear, simplify the task, change the approach, or stop.
A useful discovery can be that you need a better template or a conventional integration. The objective is a better way to work.