Why Conversational AI is Set to Bloom

2019 may well be the inflection point for conversational AI adoption, going by the recent advances this technology has made on the ground. For years, there’s been din in the market; that AI is arriving. In 2018, it only got shriller – with an upsurge in published papers, events, media glare, investments and more, and actual real-world implementations, bringing the possibilities of conversational AI closer to the world. While it’s thrilling to see a new idea hit the ground and set about the changes it promises to bring, the next 18 months will be crucial for the success of conversational AI.

For now, the prospects seem optimistic. A recent McKinsey survey that tracked the percentage of executives whose companies are using variants of AI in at least one business function or unit, has found an increased thrust on applications such as image detection, voice enablement, natural language understanding, voice search, translation, and data mining.

What’s unique about Conversational AI

When combined and used in specific contexts, these technologies – NLU, NLP, NLG, and machine learning – are helping build powerful conversational systems with marked features such as:

  • Conversational intelligence that goes beyond natural language understanding. Today, you have the ability to glean understanding from conversations – whether in spoken or written form – by analyzing content at an organizational level, using domain-specific models. This way, you derive insights from huge piles of data captured in conversations.
  • Platforms with simplified bot development processes. A plethora of platforms are now available in the market that facilitate the design, development, management, and operation of conversational solutions leveraging natural language enabling technologies and third-party APIs.
  • Proliferation of human-machine interactions in natural language. Chatbots have come of age with enhanced abilities to simulate intelligent conversations with humans in natural language. Most times, these interfaces that are non-industry specific, and apply across a range of domains and functions.
  • Regular messaging turning intelligent. Our ubiquitous messaging platforms such as email, text messages, and other human-to-human communication – can now be programmed to be smarter with a range of possibilities to ease communication.
  • Voice assistants with a range of capabilities. Spoken, conversational interfaces, that come embedded in hardware devices, and enable spoken interaction and connect with cloud-based ML capabilities.

Each of these, again, have a specific growth trajectory with deep implications for the industry.

Intelligent virtual assistants (IVAs)

Take the improvements in NLP technology, for instance, with reference to virtual customer assistants (VCAs) in customer service. Gartner predicts that by 2020, 25% of customer service and support operations will integrate virtual customer assistant technology across engagement channels, up from less than 2% in 2015.

In the near future, virtual assistants will be the first line of interaction with customers in the coming decade, just like the way the 1990s were dominated by the telephone call center and the 2000s saw the shift to websites and email.

Virtual assistants will form a synergy with other digital solutions such as IoT, analytics, and image recognition, and will become an interface that compiles the information from these devices to offer meaningful insights to users. Combining with advanced analytics capabilities, VCAs can also process information much faster than human agents and will predict consumer needs and behavior.

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