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August 21, 2018

Conversational AI Is Becoming More Mainstream As Demand Increases

Andy Peart

(Artram/Shutterstock)

Artificial Intelligence (AI) is evolving and showing strong growth for enterprises who are deploying conversation AI for customer service solutions. In the last eighteen months, 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.

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.

Drive Customer Interaction

For enterprises using advanced AI-driven conversational platforms, 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, and 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 provide 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.

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. An example of this is Shell Lube.

Shell LubeChat is designed to help customers with all their lubrication needs

Shell has implemented the LubeChat “bot”’ and deployed it in multiple languages across multiple geographies. The bot needs to know about millions of different combinations – ranging from knowing what lubricant goes into over a million or more engine types to understanding the various physical properties and attributes of tens of thousands of Shell and competitor lubricants. Since its deployment, Shell has experienced a 40% reduction in calls to live agents in their call centers and the AI-bot is enjoying a 98% end-user approval rating.

Strategy & Human Touch Is Key

While speed and ease of development is paramount for businesses looking to gain a foothold in their AI strategy, there are other aspects to consider too.

If you’re a global company you’ll need language support and it’s important to note that while some vendors offer multiple languages, deploying different language options frequently involves a whole new build to use them. The same goes with porting to different services or devices. As the number of devices that users interact with everyday grows from smart homes to in-car tech, enterprises will need to ensure that intelligent agents will work across them.

Conversational AI Opportunities

Tractica report forecasts that unique active consumer Virtual Digital Assistants (VDA) users will grow from 390 million in 2015 to 1.8 billion worldwide by the end of 2021. During the same period, unique active enterprise VDA users will rise from 155 million in 2015 to 843 million by 2021. The rapid and sustained growth that is being predicted by all major analysts points to the strongly defined opportunities and benefits that both consumers and enterprises see in the conversational AI space.

(ktsdesign/Shutterstock)

For users, conversational AI offers a means to interact with technology using their own words. Users can receive the right answers to their questions anytime they choose, without having to wade through technical FAQs or interminable automated telephone menus. They can create complex tasks with a sentence such as setting a home automation system with a simple dialogue of “Turn on the porch light after 8pm and lock the front door.” People will start to choose which companies they buy from based on the seamless and efficient experience of interacting with a chatbot or digital assistant. .

For enterprises, conversational AI offers not just a chance to differentiate themselves in a crowded market-space, but the opportunity to garner valuable data on the voice of the customer. To understand what they are looking for today and in the future; to engage with them on any device or service they use; and to deliver a personalized service not just to high value customers, but to every customer.

Of course, this neglects to mention the very tangible savings businesses can achieve by using AI-driven chatbots for automating many tasks such as customer service. Juniper Research puts the cost savings of over $8 billion annually by 2022, up significantly from $20 million in 2017. While many people worry about the impact this may have on employment, it is worth remembering that with new technology, comes new work opportunities. Already AI is contributing to an increase in skill sets of workers, and therefore remuneration, across a wide range of industries from healthcare to clerical work.

With Gartner predicting that “Conversational AI-first” will supersede cloud-first, mobile first” as the most important high-level imperative for the next 10 years, there is no doubt that many enterprises in the next 12 months will be deploying conversational systems. How successful they are, in both providing value to the customer and the business will depend greatly on the technology used, the data collected and its ability to deliver the conversational experience your customer expects.

About the author: Andy Peart is the Chief Marketing & Strategy Officer for Artificial Solutions, where he’s responsible for the strategic positioning of Artificial Solutions in the wider natural language and AI marketplace. Andy has more than 25 years experience in managing marketing operations for sector-leading software and services companies in the UK, Europe and US.

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