You already know that a decision that is backed by some analysis and data is usually better than a decision that comes purely from the gut. So when you need to make a decision on ways of improving your customer success experience, you need some data to work with. Most organizations employ some twitter usage in their customer success, some more than others. That’s why it’s important to do some analysis on incoming tweets and how well they are replied to.
Business leaders are becoming more keen to evaluate A.I. solutions. Based on our conversations in the market and being an A.I. provider, here is a list of things to consider when evaluating A.I for customer support.
Business leaders from a wide variety of industries and from companies both large and small are feeling the pressure to implement viable and effective AI strategies to keep up with the many innovations happening in the field.
As a CTO and a former consultant in the data science and machine learning space, I often get asked the question, “How do I go about planning out and deploying a machine learning project at my company?” Most of the time, managers do not have intimate familiarity with machine learning or how to apply machine learning to their business problems. When I get asked this, I almost always reply with a simple answer, “BIOD”.
Several recent studies and surveys’ across several sources has shown that implementing an artificial intelligence strategy can be a challenging maze to navigate. With a lack of extensive business cases and A.I. implementation examples it can create a needle in the haystack scenario for business leaders not only looking for relevant A.I. solutions, but also successfully creating A.I. initiatives.