We have all had the same conversations about dirty data. Messy data attributes are preventing better personalization, better AI tooling, and expanded distribution networks. And that can all be true.

But practitioners are telling a different story. They are not waiting for clean data to start using AI. Instead, they are forging ahead using AI as an enabler, while putting guardrails in place to achieve a balance of speed and risk. As one executive told us, “While we see that AI does make mistakes, it operates at such a large scale that we see a ton of value.”

In this report, we look at four best practices we heard repeatedly from digital executives at manufacturers and distributors: scoping AI narrowly instead of waiting for a perfect foundation, letting AI do the merging and categorizing work that used to require a data scientist, focusing initiatives on channel partner needs, and putting governance in place as the initiative scales.