How AI is affecting the customer service industry today and in the future
IIn this special guest feature, Muddu Sudhakar, Co-founder, and CEO of Aisera, describes how AI provides firms with a way to manage customer service at scale, by providing better on-demand answers and actions. Muddu Sudhakar is a successful entrepreneur, executive, and investor with strong operating experience with startups as CEO as well as SVP & GM roles in several public companies. Owning more than 40 patents, Sudhakar boasts deep product, technology, and GTM experience, in addition to extensive knowledge of enterprise markets including Cloud, SaaS, AI/Machine learning, IoT, and more.
Firms need automation to add intelligence to customer service processes. They’re managing high expectations from mobile-connected consumers who want immediate relevant answers to any questions. They want to perform tasks with a minimal number of actions and human interaction but want to speak to someone when needed. How can companies keep pace? Artificial intelligence provides firms with a way to manage customer service at scale, by providing better on-demand answers and actions.
Industry analysts such as Global Industry Analysts Inc., (GIA) see a dramatic uptick in AI’s usage in customer service. The company’s recent study predicted just within spending of $3.5 billion a year by 2026 just for the call center market. The expected growth relates to AI’s ability to understand customer requests and the opportunity for it to drive automation.
AI That Understands Context
To improve the customer experience, firms need advanced AI technology that better understands human interactions and expectations. A key development to making this happen is conversational AI that leverages unsupervised NLP/NLU. It’s an advancement within AI processes that dramatically improves and offers a step function shift in customer service. Prior iterations of AI were able to work with guided or structured informational flows. These use conditional statements as a guide, so a chatbot can have instructions on how to respond based on a kind of conversational flow chart. People consider the different questions a customer might pose to a brand, and then suggest the best ways the chatbot can offer an answer that makes sense. There are significant limitations with guided flows because they’re constrained by the set rules, and don’t “learn” over time. Moreover, the experience comes off as robotic as it is missing an understanding of context.
Conversational AI enables firms to leverage unsupervised dynamic workflows, where responses can come from disparate sources powered by a Knowledge Graph. Any company can set up an automated service desk that leverages website data, CRM information, ServiceNow or Salesforce platforms, and a range of others. 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 any prior training. Advanced AI platforms incorporate high-fidelity natural language understanding and processing for both written and spoken words, allowing the system to assess intent and customer sentiment even for long worded inquiries. Informed by this context, the system can alter responses accordingly, such as directing a chat automatically to a human agent when a customer’s language indicates elevated frustration. Such an operation points to the need for firms to boost their AI customer service with natural language intelligence and automated workflows.
Adding Automation to the AI Mix
The next layer of an AI-powered customer service experience comes with automation. To streamline the customer journey, firms can use robotic process automation (RPA) technology that pairs with conversational AI to deliver conversational workflows. RPA automates repetitive tasks previously done 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 chatbot structure, a customer might connect 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 intervene and complete the subscription renewal process. With AI, the customer’s intent prompts an RPA process that automates the subscription renewal through a few simple prompts. The customer enjoys a faster more connected process, and the company can remove mundane tasks from its agents. Instead, these agents can focus on upselling or handling very complex customer questions that still require a human mind.
RPA and AI combine to manage multiple use cases across multiple domains from IT, HR, Sales Ops, Customer Service, and Operations, including automation of service desk requests for password reset or software provisioning. This dynamic applies to both internal staff and to 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 might have a contract up for renewal, but the AI and bot cannot find the right information to pre-fill the contract details. With RPA, the system recognizes this exception and then routes it to an agent that deals with contracts. So instead of being “bounced around”, the customer receives a relevant and speedy response from the most qualified source.
The future for AI in customer service centers on greater contextual understanding and delivering personalized experiences at scale. It’s the combination needed to keep pace with the mobile digitally transformed world.