Your First AI Build Should Solve One Recurring Problem

When people start building with AI, the temptation is to rebuild the whole business at once: a new website, a new content engine and a new customer system, all connected. That is often where projects stall. A better first build is smaller. Pick one job that happens again and again, help it get done well, and keep a person in charge of the result.

Start with one recurring job

A recurring job is something you or your team already do on a regular rhythm, such as preparing an event page, drafting a follow-up or summarizing a set of notes. Because it repeats, you can see whether the new version actually helps. Because it is narrow, you can finish a first version and test it before you have spent months on it.

For a first attempt, avoid the most sensitive process in the business. Choose a job where a weak draft is easy to spot and cheap to fix.

Define the job before you choose a tool

Before comparing tools, write down five things:

• Audience: who the output is for.
• Task: the one job the system should help with.
• Information: the approved sources it should work from, such as a brief, product notes or past examples.
• Expected output: what a good result looks like, including format and length.
• Reviewer: the person who checks the output before anyone else sees it.

These answers shape the build more than any tool choice does. They also make it much easier to tell later whether the system is doing its job.

A prototype example: the event studio

In her Mini AI Workshop, Anna Anisin walked through an event studio prototype. An event brief drives the event page. Draft campaign content is generated in a separate AI tool and then copied in by hand, so a person reviews it before it is used. A sample registration shows the sign-up flow, and a duplicate check prevents a second registration from creating another record.

The prototype also showed a useful gap. When the event date was updated, an existing campaign draft did not update automatically. It still carried the old date until someone changed it. Nothing was sent automatically in the demo, but the moment shows why consistency checks matter in any workflow where one source feeds several outputs.

Be clear about what is finished

Anna also showed I Want Her Shoes, a community platform, demonstrating conversations, members, messaging and engagement features. She described a digital closet and local swapping as features still in development, not finished ones.

That distinction is worth copying. For your own build, write down what works today, what is still being built and what has not been tested yet. It keeps expectations honest for your team and for anyone you show the system to.

Test before you rely on it

The checks below are recommendations for your own first build. They were not all performed during the recording.

• Run it with sample data first, not real customer records.
• Edit the brief after drafts exist, then confirm every output still matches.
• Enter the same person or item twice and confirm the duplicate is caught.
• Name who approves each output before it is used.
• Decide what happens when a step fails, and how you recover.
• Keep private and sensitive information out of tools that do not need it.

A modest definition of success

For a first pilot, success can be simple. The system produces output that is useful after review. The steps are written down so someone can repeat them. One named person owns it. If you can say yes to all three, you have something worth improving. If not, the tests above usually show where to look next.