The current state and future of AI for customer service
In this special feature, Muddu Sudhakar, co-founder and CEO of Aisera, describes how AI provides companies with a way to manage customer service at scale, delivering better responses and actions on demand. Muddu Sudhakar is a successful entrepreneur, executive and investor with a strong operating experience with startups as CEO as well as SVP and GM roles in several public companies. Possessing over 40 patents, Sudhakar has in-depth product, technology and GTM experience, in addition to in-depth knowledge of enterprise markets including Cloud, SaaS, AI/Machine Learning, IT. IoT, etc
Businesses need automation and adding intelligence to customer service processes. They manage the high expectations of mobile-connected consumers who want immediate relevant answers to all questions. They want to complete tasks with a minimal number of human actions and interactions, but want to talk to someone when needed. How can businesses keep up? Artificial intelligence offers companies a way to manage customer service at scale, providing better responses and actions on demand.
Industry analysts such as Global Industry Analysts Inc. (GIA) are seeing a dramatic increase in the use of AI in customer service. The company’s recent study predicted spending of $3.5 billion per year by 2026 for the call center market alone. The expected growth is tied to AI’s ability to understand customer demands and the opportunity for it to drive automation.
AI that understands context
To improve the customer experience, businesses need advanced AI technology that better understands human interactions and expectations. A key development to achieve this is conversational AI that takes advantage of unsupervised NLP/NLU. This is a breakthrough in AI processes that dramatically improves and offers step-by-step change in customer service. Previous versions of AI could work with guided or structured information flows. These use conditional instructions as a guide, so a chatbot can have instructions on how to respond based on a sort of conversational flowchart. People look at the different questions a customer might ask a brand, then suggest the best ways the chatbot can offer an answer that makes sense. Guided flows have significant limitations because they are constrained by defined rules and do not “learn” over time. Moreover, the experiment turns out to be robotic because it lacks understanding of the context.
Conversational AI allows companies to take advantage of unsupervised dynamic workflows, where answers can come from disparate sources powered by a Knowledge Graph. Any business can set up an automated service desk that leverages website data, CRM information, ServiceNow or Salesforce platforms, and many more. . With access to troves of data, a conversational AI chatbot can respond with improved accuracy and speed, working outside of predetermined responses without the need for prior training. Advanced AI platforms incorporate high-fidelity natural language understanding and processing for both written and spoken words, allowing the system to gauge customer intent and sentiment, even for lengthy inquiries. Informed by this context, the system can modify responses accordingly, such as automatically directing a conversation to a human agent when a customer’s language indicates high frustration. Such a move underscores the need for companies to empower their AI customer service with natural language intelligence and automated workflows.
Add automation to the AI mix
The next layer of an AI-powered customer service experience comes with automation. To streamline the customer journey, businesses can use robotic process automation (RPA) technology that combines with conversational AI to deliver conversational workflows. RPA automates repetitive tasks previously performed by humans, by training software to perform certain workflows that involve actions across multiple applications or systems.
For an example of RPA and AI in action, consider subscription renewals. With a traditional chat bot structure, a customer might log in to renew a subscription, and the bot might need a few inputs until it understands the customer’s intent. Once recognized, it prompts a human agent to step in and complete the subscription renewal process. With AI, customer intent triggers an RPA process that automates subscription renewal with a few simple prompts. The customer benefits from a faster and more connected process, and the company can remove mundane tasks from its agents. Instead, these agents can focus on upselling or dealing with very complex customer questions that still require a human mind.
RPA and AI combine to address multiple use cases across multiple areas of IT, HR, sales, customer service, and operations, including automating help desk requests for password resets or software provisioning. This dynamic applies to both internal staff and customers, adding significant value to AI/RPA implementations that can automate multiple layers of processes. RPA systems can also recognize exceptions. For example, a customer may have a contract to renew, but the AI and bot cannot find the right information to pre-populate the contract details. With RPA, the system recognizes this exception and then escalates it to an agent who handles contracts. So, instead of being “returned”, the customer receives a timely and relevant response from the most qualified source.
The future of AI in customer service lies in better contextual understanding and delivering personalized experiences at scale. It’s the combination needed to keep pace with the digitally transformed mobile world.
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