Kirsten Poon Outlines 5 Things to Know Before Using AI

Artificial intelligence is becoming part of regular business activity, from helping with data to improving daily tasks. Kirsten Poon, an AI analyst based in Edmonton, helps companies understand how to apply AI in ways that match their actual needs. Her work focuses on making AI practical and useful in real-world settings.

As more businesses show interest in AI, She has identified five key points that can help teams prepare before using these tools. These are based on her hands-on experience designing and deploying AI systems in both commercial and industrial environments.

1. Start With a Clear Problem

Before choosing any AI tools, it’s important to define what needs to be solved. AI is most useful when it addresses a specific question or task. For example, a business might want to reduce delivery delays, forecast product demand, or improve response time to customer questions.

She often begins projects by helping teams focus on what they’re trying to achieve. This avoids wasted time and helps narrow down which tools or models are needed. A clear goal also helps different team members stay on the same page.

2. Use the Data You Already Have

Many businesses in Edmonton and elsewhere already collect useful data through sales, support systems, inventory records, or equipment logs. AI tools don’t always need new or advanced data to be useful. Often, working with what a company already tracks is enough to get started.

Businesses are advised to begin with small, structured data sets. These could be spreadsheets, customer feedback, or machine records. Kirsten Poon notes that clean, consistent data makes it easier to build models that provide helpful results. If the data is incomplete or unclear, time should be spent improving it before applying AI.

3. Not Every Task Needs AI

AI works well in some areas, but not all. It is most helpful in tasks that involve patterns, predictions, or large amounts of repeated activity. For instance, AI can be useful in forecasting sales, flagging system errors, or detecting unusual behavior in data.

Kirsten Poon encourages teams to first check whether simpler tools like regular reporting, automation, or decision trees might solve the problem just as well. In some cases, AI may add more work than benefit. The goal is to make work easier, not more complex.

4. Collaboration is Key

AI projects often involve people from different backgrounds, technical staff, managers, analysts, and field workers. Each group has different knowledge about the problem and how things work on the ground. Bringing these people together early in a project makes a big difference.

In her work across Edmonton businesses, She has seen better results when team members share ideas and raise questions before any model is built. Open communication helps catch small issues before they grow, and makes sure the final system is actually useful for the people who will use it.

5. Plan for Long-Term Support

Once an AI system is working, the process isn’t over. Models may need updates as new data becomes available or as business goals shift. It’s helpful to assign someone or a team to check on the system regularly.

She also advises setting clear roles for who monitors the AI’s output and who makes changes when needed. This reduces confusion and keeps the system reliable over time. For example, if a model starts giving results that don’t match real outcomes, someone should be ready to review and adjust it.

These five points offer a starting place for companies interested in using AI in a thoughtful and clear way. AI doesn’t have to mean large changes or complex systems. With clear goals, good data, and communication, businesses can apply these tools in a way that supports their daily work.

Kirsten Poon’s approach is shaped by real projects, not theory. Her focus remains on helping teams across industries build systems that work in practice. As Edmonton businesses explore new ways to improve through technology, She continues to guide them with clear methods, careful planning, and experience grounded in daily operations.