What is conversational AI?
Conversational AI is a form of Artificial Intelligence that allows people to communicate with applications, websites and devices in everyday, humanlike natural language via voice, text, touch or gesture input.
For users it allows fast interaction using their own words and terminology. For enterprises it offers a way to build a closer connection with customers through personalized interaction and receive an unprecedented amount of vital business information in return.
Strong growth in conversational AI for the enterprise
The last eighteen months have seen no abatement in the demand for conversational AI platforms and predictions from major analyst firms shows the trend is set to continue strongly in 2018. But throughout this, there is an underlying message; enterprises need to deploy conversational platforms that are capable of truly understanding the customer—however they phrase the question.
Fueled by interacting with the likes of Siri and Alexa, it’s no surprise that Gartner predicts that by 2020, customers will manage 85% of their relationship with an enterprise without interacting with a human. In a recent report that explores the conversational AI applications and looks at development technology from Amazon, Apple, Facebook, Google and Microsoft, alongside Artificial Solutions, it discusses how the tremendous growth in the Intelligent Assistant market can be attributed to automated customer support services and smart home systems.
But it also points out that accuracy in some chatbots is the main restraining factor for uptake in the market. In addition, it is becoming increasingly apparent that customers are looking for a more humanlike, natural experience.
Intelligent conversational interfaces will drive customer interaction
However, for enterprises that can overcome this issue by using advanced AI-driven conversational platforms such as Teneo, the rewards are great. Not just the increase in customer satisfaction, but in the actionable data that conversational interfaces generate.
In order to achieve this, enterprises need to ensure that conversational chatbots can understand the context and the sentiment behind the conversation. That the conversational AI solution can seamlessly integrate with back-end data and third-party databases to enable deeper personalization. It also needs to be capable of creating detailed analysis of the chat logs in real-time to feedback into the conversation, improve and maintain the system and deliver actionable insights to the business.
Understanding the conversational data generated by intelligent digital assistants will reap huge rewards for enterprises. This is because when people communicate in a natural, conversational way, they reveal more than just the words they’re saying. Their individual preferences, views, opinions, feelings, inclinations and more are all part of the conversation. But conversational data must be interpreted within its proper context before it can be turned into actionable information.
Without the tools to analyze the conversations, it’s tempting for businesses to rely on their own, often prejudiced, interpretation of data. This is normally because they don’t have the necessary resources, or retrieving the relevant information takes too long. The problem with analyzing conversational data is that it isn’t like typical data sets with neat rows and columns, and most conversational analytic tools interpret the data simply as words. Without context, meaning, or frequencies it’s easy to misunderstand information.
For example, suppose you’re an airline and you see that the word “book” and its variants are used frequently with your conversational agent. Intuitively you’d think it was referring to booking flights, but data from a live Teneo installation revealed that the word book was most frequently used about seat reservations.
Conversational AI Platforms rank as a top tech trend for 2018
Gartner also lists conversational platforms as one of its Top 10 Strategic Technology Trends for 2018. But says that: “The challenge that conversational platforms face is that users must communicate in a very structured way, and this is often a frustrating experience.” A topic which we addressed recently in the blog post Why slot-filling chatbots will never meet human expectations.
But Gartner notes that not all AI-driven conversational solutions are equal: “A primary differentiator among conversational platforms will be the robustness of their conversational models and the API and event models used to access, invoke and orchestrate third-party services to deliver complex outcomes.”
This is functionality that Teneo has been delivering for years. In fact, some of our customers are retrieving data from legacy systems to use in their conversations that are so old they don’t have handy APIs to integrate. But this only goes to demonstrate the flexibility of our technology.
Integration with external systems is key for improving business agility, increasing personalization and customer satisfaction. As the use of AI in businesses develops it will be essential for information and data assets to be shared across the enterprise. Already in implementations such as Shell’s conversational bots that have access to hundreds of thousands different variables to consider that seamless integration is essential in choosing the right answer.
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SOURCE: Andy Peart, Chief Marketing & Strategy Officer at Artificial Solutions