INSIGHT
85% Businesses will capture
85% of customer contact
using AI by 2020
The Chatbot Challenge
Given that Gartner is expecting businesses to be capturing 85% of customer contact using artificial intelligence
(AI) by 2020, it is reasonable to think that we should be clicking on that chatbot with a bit more confidence by now.
Is that the case for you?
When I open a
conversation with a
chatbot it’s with a slight
feeling of trepidation?
How quickly will the chatbot
understand what I need, will
conversation be meaningful and
relevant, and – if we are still
talking by this point – how well
will it resolve my query?
According to Renat Zubairov,
CEO and co-founder of elastic.
io, a company that supports
organisations of all sizes in their
digital strategy initiatives by
helping them spend less time
on integrating and monitoring
various data sources across the
business, and more time on
using this data, the challenges
that companies face in deploying
effective AI solutions tend to
follow common themes. In
fact, of the three biggest issues
companies experience with these
applications, Zubairov says
three of them relate to one key
ingredient – data.
Context through learning
Ideally, companies want the
chatbot to answer as many
questions and handle as many
scenarios as quickly and
accurately – and meaningfully
– as possible from the day they
go live. And therein lies the
first challenge, because AI is a
learning technology not a plugand
play solution.
A chatbot or AI can only
be as good as the data it is fed
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from the outset, and the better
they are integrated with other
applications across the business,
the more prepared they can be
when they meet a customer and
the more effective and intelligent
their actions.
Humans don’t interact
within set parameters; we tend
to have several words with
similar meaning, and pepper
sentences with colloquialisms
and expressions. As such, every
query is different, even when
the answer they seek may be the
same. Chatbots need to have
a solid background of context
on which to draw in order to
predict outcomes, learn formula
and extrapolate algorithms for
the most common queries they
handle.
Using an integration platform
as a service (iPaaS) enables
developers or organisations to
link AI programmes to data
sources across the enterprise.
Working as part of the overall
system structure, instead of in
isolation, provides the bot with
access to contextual data that
creates stronger algorithms and
establishes known pathways it
can follow for query resolution.
Outside the test environment
As already noted, chatbots are
expected to be ready, willing and
able from the day they go live,
which puts enormous pressure
on the testing phase. Integrating
with several data sources during
the learning phase can be costly
– though critical – and tends
not to be the area on which
developers and companies want
to be spending their money.
Ideally testing should be
on the same live data that the
chatbot is expected to run on,
but in isolation from the day to
day business so that customers
aren’t exposed to the learning
phase.
Achieving viability
It has been estimated that
one in seven conversations
with a chatbot ends up being
routed to a person and one in
eight is abandoned altogether.
Is AI technology viable in
the commercial world if
organisations are risking the
delivery of their frontline
customer experience?
For a meaningful interaction
between human and machine
the chatbot must first be
enriched with lots of clean,
relevant data. Without this data
input, AI systems will lack the
training to expand and extend
the models on which they
operate and the ability to deliver
meaningful results that satisfy
users.W
hen referencing multiple
data sources, errors can easily
throw the chatbot off course
and enterprise IT structures
are notorious for carrying
discrepancies between related
fields in different software. If the
online sales interface receives an
update, such as a new delivery
address or order amendment,
is it communicating with the
logistics software or inventory
platform to feed the right
information to a chatbot if the
customer has a query?
In most instances, probably
not. But creating integration
across the full range of the
enterprise architecture can
easily be achieved by deploying
an iPaaS solution that links
common fields across multiple
applications.
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