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TECHNICAL RESEARCH
Introduction
The chatbot market place is currently flooded with a range of chatbot
solutions from novel implementations to enterprise grade. It is
predicted that by year 2020, 80% of all first-line user-business
interactions would be fielded by a chatbot. A chatbot enables the
human agent to concentrate on higher valued tasks like business
development, customer understanding, tailoring products/ services
and conversations per user, reduce organization costs, among many
benefits. This brings to the fore the key question – “How do I design
and implement an enterprise grade chatbot?” 25
There are three design principles which can be used for this as shown
in diagram below. And technology selection and implementation is the
3rd step in this process. Once the business has a good understanding
of why and if the users would want it, then comes the technology
question – which platform to use, what NLP engine to implement, how
to design conversations, etc?
Recognizing the market trend, all of the technology giants have
already made their foray into this area, either for improvement of their
own products/ services or as a commercial offering for other
organizations to implement on their own. Google integrated API.ai into
their cloud offering and has been acquiring additional businesses to
bolster their offering. Microsoft built Language Understanding
Intelligent Services (LUIS) which can plugin into any dialog manager
as well as their own BOT framework. Facebook has recently acquired
wit.ai to enhance their products. IBM has built the most popular
enterprise chatbot infrastructure as part of their Watson conversation
engine 26 (Table 11).
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