4 Principles for Your AI Strategy

When planning your AI journey, businesses will typically discuss objectives and goals for which they believe to be the greatest value they can get out of their data.

Often forgotten though, are the guidelines needed to ensure a smooth transition to being a data-driven business. After all, the AI your company creates should solve real problems and delight customers — not cause frustration and technical mishaps.

Data Science Principles

Life principle.

To make certain that your business’ strategy will maximize its return on investment from Data Science, its important to follow some basic fundamentals during planning. Here are 4 simple and easy to integrate Data Science principles that can be used to kickstart your AI journey:

  1. Impact decisions and actions — Any analytics or AI created needs to be focused on what is important. Knowing your objectives (sales, costs, etc.) upfront will help prioritize resources. Just like other investments, you want to see real change or a return within a particular timeline. Evaluate goals (sales, costs, etc.) alongside data to get a better picture of where to start, and what to avoid. This guide can help prioritize all the ideas you come up with.
  2. Open minds to innovation — My belief is that AI should create new opportunities and inspire curiosity. Even the analytics from a simple dashboard should help people figure out how to use their data, what the pros/cons are, and what new questions to ask. If any results don’t inform, inspire, inquire, or innovate — then its time to pivot. A good place to start is going back to the basics — how to ask a good question.
  3. Data and AI go together — You can’t get the most bang for your buck if you do one without the other. Traditionally, companies will implement a database or develop a data strategy before ever considering the analytical possibilities. You need good data to build strong AI. Data that is used by AI, goes up in value. Including data experts during your planning will help mitigate issues later, avoid costs and delays, and improve controls.
  4. Build an AI community — When doing Data Science, most people only think about data scientists. The fact is, it takes many other individuals to successfully build, deliver, and use AI. From the folks on the factory floor to the engineers and statistics junkies, a lot of people often interact and engage with AI. Be sure to create a community — one that includes employees and customers — where they can learn, and discuss common topics and opportunities.

While no one-size-fits-all approach to Data Science exists, including these basic principles into your strategy or next AI project will help take your team and product to the next level.

If you want to learn more, check us out at OG Data Labs.



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Stephen Rohrer

Putting Data to Work @ OG Data Labs | Ex-Head of Data Science @ TIAA | Data Scientist | Analytics Advisor | AI Evangelist | Data Nerd | www.OGDataLabs.com