Q + A + Q + A + Q + A + Q + A + Q + A + Q + A + Q + A +
DR NICOLA J. MILLARD,
PRINCIPAL INNOVATION
PARTNER, BT
EDITOR’S QUESTION
Machine Learning tends to work
best in areas which are data rich,
process driven, and unambiguous,
for example cyberattacks on networks,
diagnosis of very specific health conditions,
pattern recognition for standard equipment,
matching a defined set of products with a
specific set of requirements.
That said, no amount of secret sauce will
revolutionise a business unless its data is in
order. Machine Learning does not work by
magic – it depends on data. If that data is
unstable, inconsistent and spread among
multiple legacy systems, Machine Learning
becomes more difficult and far more
expensive to do.
Aside from data, enterprises also need to
step back and ask what problem they want
Machine Learning to solve and whether it will
actually solve it. For example, if they want
to deploy a chatbot to improve customer
experience, does it actually improve it or
just add another level of frustration for
customers if the ‘bot’ hits a dead end and
abandons them if it can’t understand what
they want? Enterprises need to understand
how Machine Learning will integrate with
legacy systems and processes (e.g. in the
contact centre), how much training it will
require and what the business case is (given
return on investment may take some time).
There are some interesting opportunities
around something I term the “me”conomy.
This is all about leveraging customer
data to tailor experiences. It starts with
personalisation – something that Amazon and
Netflix have done for years and customers
like. The next step is proactivity – i.e. telling
customers things that they might want to
know (e.g. appointments, fraudulent activity,
faults, contract renewals), on the right channel
and at the right time before they have to tell
you. This is generally regarded as a good thing
by customers but could become irritating if
done badly. Finally, you could then become
predictive and anticipate what customers
might need before they know that they need
it. This can get creepy, though! If customers
get irritated or creeped out, they are likely to
stop sharing their data. No data means that
Machine Learning is no longer effective.
Going forward, Machine Learning will
inevitably get more accurate as more
and better quality data is available. As
organisations move more into the digital
space, 5G and IoT connect more things and
better data standards are agreed, it can
become more powerful.
CIOs and IT leaders need to ensure that the
wider business doesn’t get carried away by
the hype around AI and Machine Learning.
They need to work with the wider business
to identify where deployment of Machine
Learning is appropriate, likely to deliver ROI,
and (above all) deliverable. There may be
“
CIOS AND IT
LEADERS NEED TO
ENSURE THAT THE
WIDER BUSINESS
DOESN’T GET
CARRIED AWAY
BY THE HYPE
AROUND AI
AND MACHINE
LEARNING.
a need to completely re-engineer legacy
infrastructure, data and processes, so CIOs
and IT need to engage with the rest of the
business to gauge their appetite to do this and
ensure that investment budgets are realistic.
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