on cleaning your data, eliminating silos, and ensuring a holistic view of customer data. Take a balanced approach to data maturity aligned with your strategic goals. Knowing it’s not realistic to improve everything at once, work to understand the specific data requirements needed to deliver your AI use cases in a phased approach. Most importantly, keep data quality and availability front and center as you build a strategy in tandem with AI.
See Gartner’s top picks for customer data platforms
Read the research on what ranks #1 — and why. It’s not just about afghanistan phone number list being able to see your entire customer, it’s about being able to execute quickly on those insights.
Find what works
2. Build trust in AI
Building trust is a must in AI. Almost all respondents (96%) said that trust is important – or even critical – when partnering with an AI vendor. Genuine concerns about unintentionally exposing private customer data, infringing copyright, or violating data regulatory compliance requirements raise a lot of questions. Specifically, organizations are seeking vendors who already have security protections – such as data masking (the practice of anonymizing sensitive data) – baked into the tool. Another way to protect from potential risk is to choose a vendor that offers AI as part of their core CRM offering, so there are no extra hoops or complexities with trying to integrate an outside source.