Artificial intelligence and the rise of augmented agents in customer experience

Artificial intelligence and the rise of augmented agents in customer experience

Published on: May 31, 2017
Author: Pascal Gauvrit - CTO

Artificial intelligence (AI) is currently the hottest topic in customer experience, with brands exploring how they can use digital technology such as chatbots to deliver the fast, accurate and personalized service that consumers demand. AI will radically change how customer service teams operate, with Gartner predicting that it will disrupt the jobs of 1 million phone-based customer support agents by 2020.

However, as Gartner analyst Michael Maoz pointed out at the recent Gartner Customer Experience and Technologies Summit, disrupt is not necessarily the same as replace. Delivering a superior customer experience must involve a balance between the efficiency and speed of tech and the empathy, emotion and complex problem solving that humans provide.

The rise of AI and automation will make it easier for consumers to find information more quickly through digital channels, rather than picking up the phone. According to Gartner, the percentage of interactions on the telephone (including through IVR), will drop from 69% in 2017 to 22% in 2022. But there are two key points that brands need to factor in:

  • The overall volume of interactions is growing, as consumers have more questions about the products and services that they are using, and expect more from the brands that they deal with.
  • Many interactions will involve humans and technology working together, such as through co-browsing on chat for example. Therefore, while the percentage of interactions handled by technology will rise from 49% in 2017 to 85% in 2022, the human side will not fall by a corresponding amount. In fact, by 2022 36% of interactions will involve humans, either working independently, or more likely with technology support.

Striking the balance between humans and AI
Therefore, the key action for customer experience leaders is to analyze all of their processes and understand where they sit on a sliding scale, with full automation at one end, and complete human interaction at the other. This means measuring the complexity of the process and the interaction and judging what will best meet customer needs. For example, Accenture research found that 71% of British consumers prefer dealing with human agents when asking for advice, as it provided the empathy and understanding they were looking for. In contrast, simply checking the balance on your account or the status of an order should be easy to automate through self-service.

As well as analyzing processes against the technology/human scale there are four further areas that brands should concentrate on when it comes to successfully adopting AI in their customer experience:

1. Augmented agents
Nothing annoys customers more than speaking to customer service agents who cannot provide definite, consistent answers to their queries, normally because they don’t have the right information to hand. No amount of empathy and understanding will help if agents don’t have the knowledge to respond to customer questions. Therefore, brands need to look at augmenting the human traits of agents with AI-based technology, giving them the support they need to focus on empathy and solving more complex problems. Systems that automatically scan incoming digital interactions, such as email or social media messages, and suggest relevant responses to agents not only help them deliver faster, more productive service, but empower them with the knowledge they need to meet customer demands.

2. Focus on emotion
Understanding what consumers want can be difficult, particularly on digital channels which lack the context of face to face or voice communication. Consequently, consumers often complain that agents don’t empathize with their situation, failing to spot and respond to factors such as being angry or upset. One of the roles of agents is to provide this human, emotion-based response, and again their capabilities can be augmented through AI. Using techniques such as Natural Language Processing (NLP), digital communications can be analyzed for factors such as context and emotion, enabling agents to respond accordingly with personalized, empathetic replies. This strengthens loyalty and provides a human face to the brand.

3. Ensure staff have the right skills
The move to a more blended model of technology and humans will mean agents require new skills. They need to be more creative and autonomous, and to be able to solve complex problems, all while demonstrating understanding and emotional engagement with the customer and their needs. Add in the ability to collaborate with peers and to master technologies such as video chat and mobile apps and it is clear that the role and status of the agent will actually grow over time.

4. Making the process seamless
Customer experience is a conversation, rather than a single transaction. Consumers want to escalate from automated channels, either because they have more detailed, personalized requirements or if they are not happy with the response received. Therefore, it is vital that brands make these escalation points between different channels, and humans and technology, simple and seamless. That means integrating information so that consumers don’t have to repeat themselves or expend additional effort to get the response and experience they require.

Successfully delivering the best customer experience in the future will require a different approach to that of today. Brands will need to bring together humans and technology, with simple interactions handled automatically by bots, and AI supporting agents when they are having more complex conversations with consumers. As Michael Maoz says, humans will remain at the core of great customer service – but only by playing to their strengths and using artificial intelligence to augment their skills.

Tags: Eptica, AI, Artificial intelligence, Gartner, Michael Maoz, chatbot, emotion, Customer experience, Customer Service, augmented agents, NLP, Natural Language Processing
Categories: AI, Trends & Markets

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