In this special guest feature, Muddu Sudhakar, Co-founder and CEO of Aisera, describes how AI gives companies a way to manage customer service more comprehensively, by providing better on -demand answers and actions. Muddu Sudhakar is a successful entrepreneur, executive and investor with strong operational experience with startups as CEO as well as SVP and GM roles in several public companies. Owning more than 40 patents, Sudhakar boasts in-depth GTM product, technology and experience, in addition to extensive knowledge of enterprise markets including Cloud, SaaS, AI/Machine learning, IoT, and more.
Companies need automation and to add intelligence to customer service processes. They manage high expectations from mobile -connected consumers who want instant relevant answers to any question. They like to perform tasks with a minimal number of human actions and interactions but like to talk to someone when necessary. How can companies keep up? Artificial intelligence gives companies a way to manage customer service comprehensively, by providing better on-demand answers and actions.
Industry analysts such as Global Industry Analysts Inc., (GIA) are seeing a huge increase in the use of AI in customer service. A recent company study predicted to spend just over $ 3.5 billion a year by 2026 just for the call center market. The expected growth is related to the ability of AI to understand customer requests and the opportunity for it to drive automation.
AI that Understands Context
To improve the customer experience, companies need advanced AI technology that better understands human interactions and expectations. A major development in doing this is communication AI that uses unsupervised NLP/NLU. It is an advancement within AI processes that dramatically improves and offers a step change function in customer service. Earlier iterations of AI were able to work with guided or structured information flows. They use conditional statements as a guide, so that a chatbot can have instructions on how to respond based on some kind of communication flow chart. People consider the different questions a customer might ask a brand, and then suggest the best way the chatbot can offer a meaningful answer. There are significant limitations to guided flows because they are constrained by set rules, and do not “learn” over time. Moreover, the experience emerges as robotic because it lacks an understanding of context.
Communication AI allows companies to use unmanaged dynamic workflows, where responses can come from a diverse source powered by a Knowledge Graph. Any company can set up an automated service desk that uses website data, CRM information, ServiceNow or Salesforce platforms, and more. By accessing data troves, the talking AI chatbot can respond with enhanced accuracy and speed, working outside of predefined responses without the need for any prior training. Advanced AI platforms incorporate high-fidelity natural language comprehension and processing for both written and spoken words, allowing the system to assess customer intent and emotion even for long words. questions. Because of this context, the system can modify responses accordingly, such as automatically directing a chat to a human agent when a customer’s language indicates high frustration. Such an operation addresses the need for companies to strengthen their AI customer service using natural language intelligence and automated workflows.
Adding Automation to the AI Mix
The next layer of AI -enabled customer service experience includes automation. To streamline the customer journey, companies can use robotic process automation (RPA) technology paired with communication AI that delivers communication workflows. RPA automates repetitive tasks that people have previously done, through training software to perform certain workflows that include actions across multiple applications or systems.
For an example of RPA and AI in action, consider subscription renewals. In the traditional chat bot structure, a customer can connect to renew a subscription, and the bot may require some input until it understands the customer’s intent. Once identified, it encourages a human agent to intervene and complete the subscription renewal process. In AI, the customer intent drives the RPA process that automates subscription renewal via a few simple prompts. The customer enjoys a faster more connected process, and the company can eliminate mundane tasks from its agents. Instead, these agents may focus on upselling or handling very complex customer questions that still require the human mind.
Combines RPA and AI 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 identify exceptions. For example, a customer may have a contract for renewal, but the AI and bot cannot find the right information to pre-fill in the contract details. In RPA, the system recognizes this exception and then takes it to an agent who deals with the contracts. So instead of being “bounced around”, the customer receives a relevant and quick response from the most qualified source.
The future for AI in customer service is centered on greater understanding of context and delivering personalized experiences to scale. This is the combination needed to keep pace with the mobile digitally transformed world.
Sign up for the free insideBIGDATA newsletter.
Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1