This is a scenario where we are contemplating if the future of customer service will be around chatbots and humans will be out of the equation. There are some challenges that human-based customer services face. They are, for example
- Many queries remain unattended simply because a human service executive skipped it.
- The customers are always in a queue to attain the service because human based customer service can scale only if more people are recruited to the service executives team.
- Different human members in the team usually don’t keep a track of an ongoing/unresolved query and deal it from scratch every time in their own way. This induces delays and variabilities.
- Training humans is a repetitive and resource consuming process.
- A limited set of services and inability to solve complex issues on their own.
- Slow resolution of queries.
Whereas a chatbot is expected to solve these challenges by having a scalable, concurrent, uniform, single point, fault tolerant decision engine and communication engine, it imposes another set of challenges that can be summarized as
- Lack of fluent, human friendly communication.
- Difficulty in understanding natural dialogues of humans
- Triggering a correct decision based on the interaction with human
- Seamless availability
The artificial intelligence developers understand that finding solution to these challenges is critical in boosting the AI chatbot based customer service market and are working towards elimination of these challenges. Here are 4 key features which AI chatbot can provide:
- Quickly customizable based on the use-cases of the client
- Ability to take in limited inputs while keeping user engaged through interactions.
- Seamless Hand-off to NLP bot or agent
- Intuitive analytics for mapping user behaviour
- Understand the context of a conversation due to previously defined workflows.
- Able to sort the intent of questions to give meaningful answers
- Options to confirm or clarity intent
- Machine Learning from prior conversations to train for better responses
Multi Agent Queuing
- Queuing up of chats for an agent to ensure zero drop-offs
- Dynamic allocation with balanced load
- Supports multiple operators with ability to tweak work flows
- Conversation history availability with bot to maintain context
- Service oriented architecture with REST APIs
- Integrations support with CRMs
- Integrations support for lead management systems
- Integrations with any 3rd party systems
Over the time, these chatbots need to learn how to build relation with customers rather than plain fresh chat every time. With this relation, the customers will start to embrace the bots and bots will start understanding them better.
But, will AI chatbots completely replace humans is still an open question. Shep Hyken, who is the Customer Service Speaker & Expert at NY Times Bestselling Author, says that AI chatbots are powerful business tools and create a better business experience, but they will never totally replace humans, only enhance the interactions.
Ernan Roman, President of ERDM Corp says that “Based on thousands of hours of VoC research our firm, ERDM Corp. has conducted for clients such as IBM, MassMutual, Gilt and QVC, we believe that marketers must leverage AI and related technology to improve human interactions and CX. Technology should not be seen as a means to replace high value functions like Sales Reps or Customer Service Reps. Instead, technology should significantly empower these people to deliver the quality of CX that, per our VoC research, remains sadly lacking for B2C and B2B customers! Therefore, technology has to enable marketers to finally personalize the purchase and service experiences at the level of one individual at a time!”
Therefore, it is conclusive for today’s time that there are a set of functions which humans will always be preferred over chatbots. Chatbots can replace the repetitive human task to a high extent but a set of humans will always be required to supervise the AI chatbots, and live chat with human service executive will still remain powerful for SMEs.