Developing Solutions for AI – How to Get Started

Without a doubt there is a tremendous amount of buzz around Artificial Intelligence (AI) right now. But AI has so many facets that many people are left feeling confused and really don’t quite know where to start. While it is certain that AI has the potential to transform industries by enabling businesses to make smarter decisions, automate processes, and create innovative products. The biggest hurdle is understanding the business use cases and where to start the journey to developing AI solutions. The most important aspect is to keep in mind that AI is just another tool in our toolbox and the focus is on the data and business case and not the technology. AI should really be another tool to help us address business challenges! Our goal in this blog will be to guide you through the essential steps to get started with AI, emphasizing the importance of defining the AI use case and evaluating its desirability, viability, and feasibility. Ultimately leading down a path where you can leverage AI as a valuable tool to provide a solution to some of your business challenges.

Defining the AI Use Case: Predictive AI vs. Generative AI

The first challenge in developing AI solutions is defining the use case. This involves understanding the difference between predictive AI and generative AI:

• Predictive AI: Uses historical data to predict future outcomes. Common applications include demand forecasting, customer churn prediction, and risk assessment.
• Generative AI: Creates new content based on existing data. Examples include text generation, image creation, and music composition.

Choosing the right type of AI depends on your business needs. For instance, if your goal is to improve customer retention, predictive AI might be the best fit. On the other hand, if you want to enhance your marketing content, generative AI could be more suitable.

Steps to Build the AI Use Case

Once you have identified the AI use case, follow these steps to build it, we like to look at these as the pillars to success:

1. Use Case Ideation: Think about the data your organization has; create a list of where AI can solve specific business problems. Your ideas could have large positive implications for the business or be very tactical in nature.
2. Build a Playground: Create a sandbox environment where you can experiment with different models and approaches without affecting your production systems.
3. Train: Collect and prepare the data needed to train your AI model. This involves cleaning the data, selecting relevant features, and splitting it into training and testing sets. Export data sets or document files to train your model. Don’t forget to have two different sets of data, one for training and one for testing.
4. Test: Evaluate the model’s performance using the testing data. Adjust the model parameters to improve accuracy and reliability.
5. Upload Data: Continuously feed new data into the model to keep it updated and improve its performance over time.

Evaluating the AI Solution: Desirable, Viable, and Feasible

After building the AI solution, it’s crucial to evaluate it through three lenses:

• Desirable: Do stakeholders and customers want or need it? Conduct surveys, focus groups, and pilot tests to gather feedback and ensure the solution addresses real needs.
• Viable: Is it sustainable in the long term? Assess whether the solution will provide the intended value over time and if it aligns with your business goals.
• Feasible: Do you have the data and resources to support it? Ensure you have access to the necessary data and technical capabilities to maintain and scale the solution.

Getting Started with AI

Starting with AI can be intimidating due to uncertainties about costs and the complexity of the technology. Here are some common fears and how to address them:

• Not knowing the cost: Begin with a small data exploratory project to understand the potential costs and AI services before scaling up.
• How to start: Partner with experts or use AI platforms that offer guided workflows and support.

This is a journey, and every journey comes with its own sets of bumps in the road. Remember, failure is part of the process. Many initial AI use cases may not deliver the results you anticipated, but through continued fine tuning and prompt engineering exercises, new valuable insights can be discovered. Continuously validate and refine your data and models to build a successful AI solution.

Conclusion

If you need help envisioning your AI use cases, consider requesting our free AI Discovery workshop. We help your organization identify impactful use cases for AI. By the end of the session, participants will have a clearer understanding of how AI can be utilized within their organization.

Embark on your AI journey with confidence, knowing that each step brings you closer to unlocking the full potential of AI solutions for your business.