Artificial intelligence is no longer just a research topic. Businesses across industries now use it to improve processes, reduce costs, and create new opportunities. Kirsten Poon from Edmonton has spent her career helping companies understand and apply AI in ways that make sense for their operations. She has worked with commercial and industrial firms, designing and deploying systems that solve specific problems. Her approach is rooted in practicality, teamwork, and clear communication with business leaders.

This article brings together some of the key approaches Kirsten Poon has shared for making Artificial intelligence work in real business settings. Whether you run a large enterprise or a smaller company, these ideas can help you see how AI supports growth and decision-making.

Start with a Clear Business Problem

Kirsten Poon emphasizes that successful AI projects begin with a defined problem. Too often, companies chase AI because it feels new or popular. She reminds leaders to step back and ask: What challenge are we trying to solve?

For example, one Edmonton-based manufacturing firm approached her team with delays in quality checks. Instead of applying a general AI tool, her team built a computer vision system trained on thousands of product images. The result was a faster inspection process that reduced errors and gave workers more time for higher-value tasks.

When you start with a business pain point, AI becomes a tool that fits into your workflow rather than a solution searching for a problem.

Work with the Right Data

Every AI system depends on data. Kirsten Poon highlights that quality matters more than quantity. Many companies store years of information but struggle with accuracy and consistency. She recommends beginning with smaller, clean datasets rather than feeding a model with messy records.

One retail client she advised had customer transaction data spread across multiple systems. Instead of combining everything at once, the team started with one store’s data and built a recommendation engine. Once the model showed strong results, they scaled it across more locations. This approach prevents wasted time and builds trust in AI tools because leaders see results step by step.

Also Read: Kirsten Poon Guides Edmonton Businesses in Using AI for Growth

Build Scalable Systems

Kirsten Poon has seen many AI projects fail because they worked in testing but could not handle real-world demands. She stresses the need to design systems that can scale as the company grows.

In one case, a logistics firm in Edmonton needed predictive models to plan delivery routes. A small prototype worked for a few dozen vehicles but broke down once hundreds of trucks were added. By rebuilding the system with scalability in mind, her team created a tool that managed routes for the entire fleet, saving fuel and time. When you plan for scale early, you avoid costly rebuilds later.

Keep Teams Interdisciplinary

AI is not only a technical field. Kirsten Poon stresses the value of bringing together data scientists, business managers, and subject experts. Each group sees problems from a different angle.

During a project for a food processing company, engineers focused on model accuracy while managers cared about production speed. By working together, they designed a system that predicted machine downtime and also fit the daily rhythm of the factory floor.

For your own projects, this means making sure everyone has a seat at the table. Collaboration keeps AI connected to real business needs.

Focus on Deployment Challenges

Designing an AI model is often the easy part. Putting it into daily use is harder. Kirsten Poon explains that companies must prepare for issues like user training, system integration, and ongoing support.

She recalls one Edmonton firm that struggled after rolling out an AI tool for scheduling. The software was accurate but too complex for staff. By simplifying the interface and running short training sessions, adoption improved quickly. If you invest in deployment planning, your employees will see AI as a partner rather than a burden.

Use AI for Continuous Learning

Kirsten Poon encourages companies to treat AI projects as ongoing efforts, not one-time installations. Models need updates as data and business conditions change.

For instance, an energy services firm she worked with used predictive models to forecast equipment wear. Over time, as new machines were added and conditions shifted, the models were retrained. This ongoing process kept predictions accurate and avoided costly downtime.

Your business can benefit from the same approach. Think of AI as a system that grows with you, improving as it learns from new information.

Also Read: Bhakti Marga Erklärt Die Rolle Eines Spirituellen Meisters In Unserem Leben

Build Trust Through Transparency

Trust plays a big role in AI adoption. Employees and managers want to know how decisions are made. Kirsten Poon stresses the need to explain results in clear terms.

When working with a financial services company, her team developed an AI model for credit risk. Instead of presenting decisions as black-box results, they built a dashboard that showed the main factors influencing each recommendation. This transparency improved acceptance and helped managers feel confident in the tool. For your own company, simple explanations go a long way. Transparency creates buy-in across the organization.

Look Ahead with Practical Curiosity

Kirsten Poon remains passionate about bringing the latest AI methods into real business settings, but she stays grounded in practicality. She encourages leaders to stay curious about new tools without losing sight of current needs.

For example, while generative AI attracts attention, she advises companies to explore where it makes sense rather than rushing into it. A local marketing team found value by using AI to draft first versions of campaigns, while human staff refined the final messages. This saved time without replacing creativity. Curiosity paired with practicality keeps your business open to new opportunities without wasting resources.

Bringing It All Together

Kirsten Poon from Edmonton has built her career on making AI work for business. Her methods highlight the need to start with real problems, work with clean data, design for scale, and involve teams across functions. She points to the importance of deployment planning, continuous learning, and building trust through transparency. Above all, she encourages leaders to stay curious and grounded.

By following these approaches, you can bring AI into your business in a way that drives success and creates long-term value.