When the AI reaches a certain level of confidence for a suggestion, it can automate and respond to repetitive questions without requiring agent approval.How to natively integrate Cognigy.AI with Salesforce Einstein and the Salesforce Cloud for amazing customer experiences. The model, in turn, creates higher efficiency for the agents, helping them serve customers faster and more effectively by helping with the more routine, repetitive answers and allowing the agents to focus on more complex responses. The agents, simply by doing their job, are helping the AI model learn. The more often a topic comes up, the better equipped the AI becomes at handling it. This ongoing improvement, referred to as continuous learning, is the process through which the algorithm gets stronger and smarter around the topics it interacts with. This joint approach ensures that the AI can be used effectively in the long run and continues improving at a rapid pace. When you give AI logs of historical interactions between service agents and customers, you are giving it a strong baseline of knowledge to start suggesting answers to customer questions, but the real learning happens when the agents start interacting with those suggestions by approving, rejecting, or personalizing them. Assist your agents with AI trained on real historical data. The use of specific terms, such as “pepperoni” or “extra cheese,” is consistent and straightforward, so, with few exceptions, orders would be accurate. Customers who place an order have a clear objective-getting a pizza-and it’s easy to measure success-delivery of the desired product. A chatbot could handle ordering pizza quite well because the process need not be complicated. Any time the scope of a customer’s inquiry is highly predictable, a chatbot can serve as an effective tool. There are numerous meaningful applications for which chatbots can be useful. Is there a valid place for chatbots? Yes, certainly. Instead, it would only offer whatever general information it had been programmed to provide. This response might not address the specific complexities of the distressed traveller’s situation. Let’s use ‘lost luggage’ as an example, if the customer were to text a chatbot about their lost bag, in all likelihood, the chatbot would recognize the term “lost bag” and provide the related response. Once they recognize a word or phrase from their complex database, they spit out a pre-determined answer. Most chatbots on the market rely on a rule-based system in which someone has pre-scripted a set of rules to understand specific words, patterns, or synonyms. They won’t improve to give better or more accurate answers. They cannot learn over time, and so don’t have actual intelligence. Though chatbots are often incorrectly characterized as AI, the truth is, they mostly just respond to keywords in a scripted way. What’s the difference between a chatbot and AI? So, should companies be investing in scripted chatbots or proper machine learning & AI to power their customer experience? Well, it depends on the problem they’re trying to solve. The problem is that rule-based chatbots are rarely able to perform beyond the very simple tasks they are designed for. Organisations want to harness the power of AI to intelligently deal with increasing customer service volumes from their frontline teams, but they often end up dealing with less-than-perfect chatbots and annoyed customers. Neither situation is an accurate representation of the technology available to customer experience leaders today.
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