CTO Straight Talk Issue 4 - 60

developed but not deployed. I've been
telling companies to calculate their
deployment score-what percentage

mentioned is companies not
having enough data to feed
the AI algorithms.

of the models get deployed. It's worse
with AI, because it's getting easier to

Yes, this is an issue, especially with deep

generate the models but maybe even

learning, which relies on lots of data to

harder to deploy them fully.

develop and train the models. I think

Is the culture of the
organization a big factor
contributing to the difficulties
in deployment you have
observed?

this will lead to the creation of data
clubs or associations. You are starting
to see different groups come together
to combine their data sets in specific
domains. For example, radiology images
where a group of hospitals combine

People say " culture " but I don't think that

their data to use it for training their deep

this umbrella term really helps. The real

learning models.

issues are that we haven't done enough
with employees to prepare them for AI,
we haven't thought about what their job
would look like in the near future when
it's assisted by AI, and who would be a
good fit for working in these
new environments.
In a global survey I did with ServiceNow,
the workers were not that concerned
about AI taking their jobs, but they
were concerned about not getting retrained, and nobody was telling them
what their job was going to be like in
the future. Companies need to think
about these issues-how to prepare
people for jobs that involve working
closely with machines, including when
the machine is in a supervisory role. I
recently talked to Farmers Insurance
and they have an AI system that tells call
center representatives whether they are
using simple language that's easy for
customers to understand and act on. As
usual, it's the people on the front line

Avoid moonshots and focus on the lowhanging fruit. Singapore-based DBS
Bank, the largest bank in Southeast
Asia, tried to use IBM Watson to make
investment recommendations to the
bank's relationships managers and their
customers. They failed. The technology
wasn't quite ready for this ambitious
task. But they had great success with
applying AI to smaller tasks such as
knowing when the ATM is going to
run out of cash, predicting the churn
of salespeople, and detecting fraud
in trading. Each one of these is not
dramatic, but if you combine them in a
specific area of the business they could
end up being transformational.

technologies, not senior executives.

What do you see happening
with enterprise AI in the
near future?

Another challenge often

We will see AI applied in the context of

that get added supervision from new

60

Given all these challenges,
what's your number one
advice to enterprises
considering or experimenting
with AI?



CTO Straight Talk Issue 4

Table of Contents for the Digital Edition of CTO Straight Talk Issue 4

Contents
CTO Straight Talk Issue 4 - Cover1
CTO Straight Talk Issue 4 - Cover2
CTO Straight Talk Issue 4 - 1
CTO Straight Talk Issue 4 - Contents
CTO Straight Talk Issue 4 - 3
CTO Straight Talk Issue 4 - 4
CTO Straight Talk Issue 4 - 5
CTO Straight Talk Issue 4 - 6
CTO Straight Talk Issue 4 - 7
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CTO Straight Talk Issue 4 - Cover4
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