04 Dec The Rise Of Conversational AI

The article originally published in Forbes on 12/4/2017.

The chatbot craze began in 2016 with Facebook’s announcement of a developer-friendly platform to build chatbots on Facebook messenger. Soon, chatbots were heralded as the next stage of the conversational revolution. Toolkits that helped you build a bot in five minutes grew popular, companies raced to the market with new bot announcements and technology conferences headlined buzzword-driven keynotes about how bots would take over human jobs.

The hype that bots would become the next great thing can be attributed directly to app fatigue. Consumers currently spend most of their time using apps created by Apple, Google and Facebook. However, most smartphone ownersdon’t buy any apps each month. Bots presented a new way for companies to adopt nascent natural language technologies to generate traffic, usage and engagement — the buzzwords of the app economy. Fueled by free platforms such as Facebook’s Wit.ai acquisition and others, developers and brands alike raced to jump on the post-app-fatigue bandwagon.

Now, less than two years later, 100,000 bots litter the Facebook messenger landscape. While the effort brought focus and media glare, it’s safe to say the chatbot revolution did not go well. As most of these chatbots have been deemed useless, the time has come to separate the wheat from the chaff.

Chatbots are primarily natural language text interfaces that are constructed using rules that encourage canned, linear-driven interactions. They are typically easy to build and navigated by pre-defined flows. For example, instead of clicking on a menu of choices or speaking predetermined commands, you can type or talk as if you were having a normal conversation in natural language.

This approach is wearing thin, despite new bots arriving in the marketplace, because it only works well for those conversations with a predefined flow — such as ordering flowers, finding a yoga teacher or booking a reservation. Attempt to ask a bot complex questions, full of unexpected stops and starts, word choices or implied meanings and suddenly, you find yourself with the bot version of Twitter’s infamous Fail Whale.

These natural, everyday and highly complex conversations require a level of comprehension and cognition that goes far beyond the predefined flow of today’s chatbots.

As chatbots failed to deliver on expectations, the enterprise market in particular has turned toward conversational AI platforms, especially in complex use cases such as banking, insurance and telecommunications.

These platforms offer more than a natural language interface (NLI): they demonstrate true advancements in combining a variety of emerging technologies — everything from speech synthesis to natural language understanding (NLU) to cognitive and machine learning technologies — and are capable of replacing humans in a variety of tasks.

These new platforms are so sophisticated that Juniper Research projects advanced chatbots may cut business expenses by as much as $8 billion in the less than five years.

Companies that wish to avoid the chatbot trap should consider the following when assessing their AI strategy:

• Can it talk, text or chat? Conversational AI should be available on voice, text or Web, and it should be ubiquitous and seamless across channels. It can be available through Alexa, Google Assistant or even your company enterprise portal. Truly omnichannel interactions are the future, and they should be a priority for your business.

• Can it learn? A conversational AI solution should be able to use the abundant history available from existing enterprise interactions, including chat and voice transcripts, transactions and other preexisting corpora of enterprise data to learn. What’s more, you need AI that can converse, suggest, recommend and engage based on these learnings.

• Can it understand? Beyond chatbot capabilities, conversational AI should understand complex sentences of human speech in the same way humans do. Real human conversation is never straightforward — it is full of imperfections consisting of slang, multi-string words, abbreviations, fragments, mispronunciations and a host of other issues. Conversational AI is a form of technology that can be used to both navigate and comprehend these give-and-take interactions.

• Does it know? By integrating into your enterprise systems, conversational AI should know who you are. It can reference the previous transactions made by you and try to fix things. It can then use this history to make current interactions smoother, troubleshoot or solve issues in customer service, IT or invoice processing.

• Can it transact? Conversational AI is secure and can support sophisticated enterprise security considerations. It can be used to complete complex transactions, replacing humans beyond a mere shopping cart click. Examples of transactions that true conversational AI can manage include buying life insurance, processing a healthcare claim, troubleshooting Wi-Fi issues or approving a supplier invoice.

The Future Of Conversational AI Is Enterprise

Despite the bursting of the “consumer chat bubble” in 2017, technology is moving forward with great strides in the enterprise.

Intelligent conversational interfaces are the simplest way for businesses to interact with devices, services, customers, suppliers and employees everywhere. There are lots of companies that provide AI-driven conversational platforms specifically focused on high impact use cases, including IBM’s Watson, KAI and my own company, Avaamo.

Intelligent assistants built on these conversational AI platforms can be taught and continue to learn every day. Meaningful applications of conversational AI are already quietly up and running, and as cost benefits continue to pile up, the trend will accelerate in 2018.