Artificial intelligence has the potential to transform the contact center industry. Much of the conversation has centered on chatbots or virtual assistants that respond to text and speech, but this is just one aspect of the wide-ranging, permanent effect that machine learning combined with process automation will have on customer service. Machine learning algorithms learn from successes and mistakes to attain programmed goals.
Applied to AI within the contact center, machine learning can improve the understanding of customer behavior to the point where issues can be predicted long before they arise. This predictive mechanism can be made possible by analyzing customer interactions at a macro level and deploying successful customer strategies that build on continual learning and optimization. Machine learning algorithms like these grow in influence and scope, and eventually, may dictate the way service is delivered at all operational levels. When AI can predict how to produce customer satisfaction in the long-term better than a human agent could ever hope to, delegating more decision-making power will begin to make greater sense.
“Think of AI as a member of staff”
Thinking about implementing AI within the contact center in terms of machine learning reminds you that it’s not a case of “drop, deploy and forget”. You should come to think of your AI as a member of staff, who will learn and develop as your business does. New processes and requirements will be introduced slowly until the algorithm becomes more proficient than human agents.
Most importantly, to build up the most accurate picture of customer behavior over time, contact center AIs will have to be omni-channel and exposed to multiple customer channels. They will also need to integrate with back-office systems throughout the organization, including field management solutions and ERPs. To learn to the best of its ability the software will need to understand every piece of the puzzle.
In the medium-term, the contact center will see itself becoming a hub for higher quality customer interactions. More human agents will deal mainly with complex problem-solving and relationship management and maintaining those relationships for longer, particularly with high-involved products and services. Eventually, we might see artificial intelligence deployments within customer service converge with the AIs of operational marketing. After all, they are essentially working toward the same goal, namely fostering customer loyalty and maintaining high perceptions of brand, which they do through optimizing customer interactions and producing long-term engagement. This will close the circle on brand promise and customer experience.
Looking much further ahead, mature AI deployments with years of experience of interacting with customers may develop more rigid patterns of behavior. They might find it harder to cope with unpredictable, transformative changes, such as a sudden change in the economic climate or an incoming generation of paying consumers with different expectations and behaviors. Prudent service organizations might find it necessary to develop and maintain a portfolio of AIs that they can cycle out and phase in when appropriate to maintain a dynamic customer service.
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