2016 has been predicted to be the year of conversational commerce, and I’d say that this prediction largely held true. Conversational interfaces have become more and more mainstream, and their support by AI and bots has become all the rage. While people more and more turn to their smartphones and Google to find service and get answers to their questions, companies are increasingly looking at bot support to increase the efficiency of their call centers.
But where is reality?
In their 2016 hype cycle on emerging technologies Gartner places
- Conversational UIs in the innovation trigger phase with a predicted time of 5 – 10 years to mainstream adoptions
- Machine Learning on the peak of inflated expectations with a period of 2 – 5 years to mainstream adoption
Forrester Research in their recent AI tech radar places virtual agents and machine learning into their growth phases of their respective life cycles, giving them 5 – 10 years to mainstream, while acknowledging a successful trajectory.
So, clearly, AI and conversational systems are strategic.
At the same time Abinash Tripathy, CEO of helpshift, a leading helpdesk company providing users with instant, proactive, and personalized in-app support, feels that “we are closer to IoT than to having really helpful bots”. Some bots are actually harming the customer experience.
And he is right.
Why? Several reasons. Essentially artificial intelligence, driven by machine learning or deep learning, is not yet intelligent enough. Too many bots are still driven by decision trees, which severely limit the possible conversations that the bot can serve.
Second, bots’ ability to understand natural language is still lacking, albeit improving, and probably improving fast.
At the same time a handover to a human agent, or another bot – think bot-swarm – is often poor or non-existent, leaving the customer with an unresolved question and in limbo.
This leads to a poor customer experience.
Additionally, the bot’s, as well as the overall help system’s, integration into a knowledge base is crucial, but often underdeveloped. A customer query needs to be translated into a meaningful query to the knowledge base and/or additional information. Think “What is the status of my recent order?” or: “I cannot receive calls but can dial out?”. This needs deep integration into a corporate knowledge base as well as transactional systems.
While a human agent can cover the lack of systems integration, a bot cannot achieve this. A bot is depending on the intelligent, and continuous indexing of corporate knowledge.
Again, poor customer experience.
In summary, Abinash maintains that “the need most bots are filling should not require an artificially intelligent conversation. If they are, they’re probably not doing it very well (yet). Rather, the best chatbots allow users to interact with their surroundings (like the baseball game example), act as refined search engines, or provide real-time updates”. At the moment “Chatbots are best used to relay simple updates”.
But, wait! On one hand AI and bots are part of the future and then they are not really useful but harm the customer experience. Isn’t that a contradiction in itself?
No, it is not. Merely a question of Thinking Big while Acting Small and doing first things first. As I have written before an important part of providing good service is being available to help the customers on their preferred channels, at the time of their choosing, and at their pace.
And one of the main channels is the smartphone, the second is chat. Additionally, people are starting with search before going for direct support. So, the way to help customers is an integrated, intelligent, efficient service offering that helps them getting to the information that they want with minimal effort on their side.
The keywords here are: Mobile, app, search, and chat.
Combining this we arrive at an offering that offers customer service directly in app, with an integrated, local knowledgebase, integrated and embedded chat, as well as the easy ability to turn the conversation into a voice (phone) conversation. Due to being embedded into the app, this offering also helps the agents by delivering contextually relevant information that shortens time to resolution. The same works for embedding this offering into a web site.
Add a back end that efficiently supports the agents with automation, workflow, collaboration tools, a clean user interface, and that integrates well with community systems, knowledge bases, other OLTPs including CRM systems, and the enterprise- and web content management systems and there is a strong foundation for improving even further.
Once this foundation is in place, AI and bots can be deployed in a useful manner, first starting in a learning-only mode, then more and more engaging in customer initiated as well as company initiated interactions. These bots can ensure quick reaction; they gather missing relevant information can already offer solutions for the simpler problems. Minimally they keep the customer engaged and do a seamless handover to a human agent. This does not only help the customer, but the agent, too, as she is prepared and can dig right into the problem at hand. As the chatbots continue to be supported by a learning system they ‘learn’ from ongoing conversations and therefore the problems they solve can become increasingly complex. This fact, plus their ability to support the agent via continuously suggesting good solutions based upon the conversation flow and the knowledge base further increase the efficiency of the supporting agents who can increasingly support on more challenging problems instead of being in the need to ask repeatedly for the same information and answer the same ole questions over and over.