Preparing for AI: The Analyst’s QuickStart Guide

In the world of data, information is as valuable as gold. As Artificial Intelligence (AI) becomes increasingly embedded into business processes, it’s essential for analysts to keep up with the rapid pace of change and ensure they’re equipped to leverage AI for effective decision-making. Let’s discuss how analysts can prepare for the future of AI by focusing on five key aspects.

1. Prepare Business Questions. The first step in preparing for AI involves framing the right business questions. This is about understanding the business goals, identifying the problems at hand, and determining how AI can help address these issues. Make sure that the questions you ask are not only relevant but also actionable. Remember, data in itself is meaningless unless it’s tied to a business context.
   • Identify the core business problem you’re aiming to solve.
   • Understand how AI can help address this problem.
   • Formulate questions that are specific, measurable, achievable, relevant, and time-bound (SMART).
2. Determine the Location of Relevant Data. The next step involves understanding where to find the necessary data to answer your business questions. Data is the fuel for AI; without it, your AI model is like a car without gas.
   • Identify the sources of data relevant to your business problem.
   • Understand the structure, format, and quality of your data sources.
   • Consider both internal (company-owned) and external (third-party or public) data sources.
3. Select Features for AI. Once you’ve identified your data sources, the next task is to select the features (or variables) that will help your AI model make accurate predictions or classifications. This is known as feature selection and it’s a critical step in the AI process.
   • Prioritize variables that are likely to have a strong impact on your outcome.
   • Consider the complexity and interpretability of your AI model. Simplicity often trumps complexity.
   • Test the impact of different features on your model’s performance.
4. Choose the Right Model Class. Choosing the right model class is another crucial step. This involves understanding the strengths and weaknesses of different AI models and selecting one that aligns with your business problem and data.
   • Understand the pros and cons of different model classes (e.g., regression, decision trees, neural networks).
   • Test multiple model classes and compare their performance.
   • Prioritize model interpretability, especially when the decisions based on the model’s predictions have significant implications.
5. Determine How to Communicate and Action the Results. Lastly, you must plan for how you’ll communicate the results of your AI model to stakeholders and how these insights will be used to drive action.
   • Develop a strategy for communicating your results in a way that is clear and impactful.
   • Consider who your audience is and what they need to understand about your results.
   • Identify the actions that can be taken based on the results and ensure there’s a process in place to implement these actions.

By adhering to these five steps, you’re not just preparing for AI — you’re paving the way for a data-driven future. The power of AI lies in its potential to turn raw data into actionable insights. And it’s the role of the analyst to harness this power and steer their organization towards informed decision-making. As the Greek philosopher Heraclitus once said, “The only constant in life is change.” Embrace it. Be the change you want to see in the data world.