As customers demand a higher standard of experience with brands and services, businesses are increasingly looking towards innovative solutions to provide this. In their fast-paced lives, between work, family and commuting, customers will seldom have the patience or time to sit and wait on a call with a customer service agent. Instead, they expect to see immediate results when they contact businesses, with quick solutions to their problems in a matter of minutes, not hours. With this expectation, more and more businesses are attempting to improve their customer services through the speed of artificial intelligence.
Acting as an “intelligent agent”, artificial intelligence can provide more efficient response times to customer issues, solving problems as they sort through data and messages far more quickly than humans. However, this AI technology alone cannot produce the best customer service experience for businesses. Left alone with an automated service, many customers become even more frustrated, especially as robotic responses often do not produce customised solutions to the customer. In fact, although AI systems can deliver efficiency, they still require the human touch to carry forward businesses and their process services.
Bringing in creative ideas and solutions from real people can mean the difference between a frustrated customer dealing with a robot, and a customer satisfied with the quick and tailored solution to their problem. If humans work with the AI technology and robotic systems available today, businesses can gain a competitive edge in their customer services.
One way of approaching this collaboration is through a process of “design thinking”, where businesses can build upon an AI’s efficient processes to create real solutions to a customer’s problems. Exploring the strategy businesses should employ to produce the best service experience for their customers, the solution should lie in this combination of AI technology and human creativity.
An Approach to Implementing AI Solutions with Design Thinking
As a process working towards the ideal customer experience, the model approach with “design thinking” would evolve over six stages of combining AI and human intelligence. To illustrate the process, take the example of a global bank that needed to improve its customer satisfaction and engagement. The bank was able to refine its customer query chat function with these steps.
Stage 1: The first port of call is creating a team that represents your customers and stakeholders, members of the operations team, and technical experts who would be able to provide guidance on whether or not an idea is feasible.
Stage 2: This team’s focus should foremost be on clearly identifying the problem faced by their customers. The bank, for example, recognised that customers were becoming frustrated with how long their chat-bot conversations took to resolve an issue. They identified that the customer service team needed to reduce the handling time of chats, using multiple case studies from across the bank as a reference.
Stage 3: Having identified and analysed the issue, this design thinking team would proceed to brainstorm possible chat-bot scenarios to develop and test with an AI. In the case of the bank, the team created a comprehensive list of all customer case types processed by existing agents, refining a shortlist of case types that occurred most often.
Stage 4: This is where AI comes in. Finding the optimum solution could take days and weeks for human intelligence alone. With AI’s cognitive intelligence, the team could use the system to test each scenario to identify the best approach to a customer issue. Once this approach is established from the AI’s results, the team would create process maps to provide initial guidance to the “Cognitive Bot”.
Stage 5: Combining the results of both human intelligence and AI, the team created a prototype to showcase how the chat-bot would help make the online chat function smarter, shaving off valuable seconds from the overall chat time.
Stage 6: Finally, the team put the Cognitive Bot in a test environment to identify the most effective responses to customer queries. Monitoring in real-time, the team was able to adjust the course of the AI’s chats in case of any deviations from the process maps they had set out. With a combination of the team refining the responses and the chat-bot’s own self-learning capability, the team was able to ensure the chat-bot would correct itself and speed up its own process of solving a customer’s issue.
The Impact of AI with Design Thinking
Design thinking not only speeds up the implementation process of AI, but also allows a team to test multiple scenarios simultaneously. This enables them to create the right solution the first time around. As with the bank in the above example, they were able to offer a chat-bot directly to card holders on both its websites and mobile apps, meaning individuals could begin a conversation with the AI chat-bot on their computers and continue it later on any other device. Chat agents were also equipped with AI to assist customers with basic questions regarding their accounts, only needing to step in for very specific issues.
Simple and less complex questions can be answered quickly by using an AI’s responses, as extensive communication won’t be necessary, as in the case of the bank, customer queries about lost or stolen cards or PIN generation, for example.
With a message data bank, an AI chat-bot can easily answer these queries with standard responses. Particularly with the help of the AI’s self-learning technology, the chat-bot is able to process each customer case so that it takes less time to answer the same query in the next case.
With the volume of customer queries on a chat function being distributed between AI and agents from the team, employees’ working hours can be used more efficiently and have a positive impact on profit margins as well. This approach of combining AI and human intelligence increases the productivity of existing employees.
In today’s market, regulatory requirements are a reality not only for banks and financial organisations, but across the legal, media and retail industries. When it comes to regulations for AI technology, companies can save millions of dollars in lawsuits by constantly training their AI systems to adapt to more complex scenarios. Initially, these complex scenarios would benefit from “design thinking”, with human intelligence ensuring a smooth solution alongside AI technology.
However, following simulated learning sessions, the AI system should be able to learn to solve these customer issues with greater ease. With the latest advancements in this area, humans and computers can interact more naturally and any gap in communication is narrowing with each passing day. Perhaps in the years to come, “design thinking” will become an inextricable part of AI implementation to produce the best processes for the customer and company.
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