On the eve of the #CRSummit in Charleston, customer experience leaders from various industries held the first AI Committee meeting. AI is a challenging topic to cover since it has varied customer experience applications depending on a brand’s growth cycle, customer base and business challenges. Companies like eBay place big bets on AI, while others use natural language processing (AI) only to build smart chatbots.
Regardless of their approaches all companies have one thing in common – they all need to prepare for AI implementation by having a comprehensive data strategy with flexible architecture and a lot of storage. This is the missing piece for most companies. Organizations have different reasons for lacking intelligent data. Some brands are too young and have homegrown systems that need major overhauls to even scale for the growth of the companies. Others have more robust data repositories, but have been built without the customer as the common unit.
There is a third scenario: companies that have third party CRM systems that also host the data. This makes it almost impossible to have the end-to-end data to use for building personalized experiences. It is important to learn the necessary foundations so when you meet with sales reps you can recognize the option that will fit your technology needs.
Another foundational and somewhat counter-intuitive aspect of applying AI is the need for humans. The biggest misconception about AI is that it will “remove jobs”. Meanwhile, customer experience leaders are all struggling to persuade CFOs to fund new teams of data scientists, people who would tag existing data, or people who watch for the “triggers” to use the data. Once this is done, brands will need data councils to add new elements or to design new uses of AI. Companies will always need more people to manage AI effectively.
Lastly, we all want to build solutions that will save operating costs today and enable a future customer experience transformation. So when we build, we need to think about scaling and building further, with the customer at the center.
There are still questions we need to answer. How do we begin the process (especially without much funding)? How can we make AI a reality and not just talk about it? What are the tradeoffs (if any) that we will have to make in the process?
Stay tuned for the next post on AI.