When it comes to AI, the concept of “garbage in, garbage out” has never been more relevant. Predictive models are only as good as the information you feed into them. Data that is incomplete, inconsistent, or inaccessible has derailed many fledgling AI projects.
But before you can launch an AI initiative, you need to first figure out what you want to use it for. Do you want to reduce customer churn? Minimize network downtime? Offer more personalized recommendations? Iterate faster on new product designs? Improve customer service while slashing support costs? The list of potential use cases for AI is almost infinite — particularly when factoring in powerful agentic AI solutions.
Once you’ve identified your key business objectives, you need to figure out if you have the right kinds of data, in the right amounts, to achieve your goals. You also need to have the right processes, policies, and infrastructure in place to ensure a robust data foundation optimized for AI success.
Consider these six steps to implement an effective AI data strategy.
