If you want to become a successful AI analyst, you need more than technical skills. You need real-world experience, strong communication, and a clear focus on solving problems that matter.

Kirsten Poon, an AI analyst from Edmonton, has built and deployed AI systems for both commercial and industrial use. She works closely with data scientists, engineers, and business teams to turn data into results.

Kirsten Poon shares 4 practical tips to help you grow in this role-  based on what's worked in the field, not just in theory.

1. Get Comfortable With Data Early

You can't succeed in AI without understanding data. Learn how to clean, organize, and interpret large datasets.

Start by working with open datasets like those on Kaggle or data.gov. Try predicting housing prices or customer churn. Focus on understanding what the data tells you before jumping into complex models.

Kirsten says her first breakthrough came when she built a simple churn prediction model using basic logistic regression. “It wasn't perfect, but it helped a client save money by spotting customers at risk of leaving,” she recalls.

2. Learn to Communicate Clearly

AI insights are useless if no one understands them. Develop the skill of explaining your findings in simple terms.

Use visuals. Charts and graphs often explain things better than words. Avoid jargon when talking to business teams.

Kirsten once turned a failing pilot project around by explaining the AI results in plain English during a board meeting. “Once they saw the pattern in a simple graph, they immediately got it,” she says.

3. Focus on Solving Real Problems

Don't build AI projects just for practice. Solve real problems that matter to people or businesses.

Find areas where small wins create value. That could be reducing delivery time, improving customer experience, or flagging faulty products.

Kirsten worked with a manufacturing client to detect early machine faults. The AI model wasn't complex, but it helped reduce downtime. That's what made it successful.

4. Collaborate With Other Experts

You won't work alone. AI projects involve data engineers, developers, and business leads.

Learn to ask questions, give feedback, and listen. Collaboration helps you grow faster.

Kirsten says, “Some of my best ideas came from casual chats with teammates. A different perspective can change your entire approach.”

Success as an AI analyst doesn't come from knowing everything. It comes from doing the work, solving real problems, and learning from others.