CIO Straight Talk - Issue 9 - 16
There will be a greater than 300% increase in investment in artificial
intelligence in 2017 compared with 2016. (Forrester Research)
to more than 3 billion Internet users worldwide,
a number of companies offer advertising
One of these companies is AOL Platforms, a
unit of Internet pioneer AOL, now a subsidiary
of Verizon Communications. It started building
its data science team more than fifteen years
ago and has continuously evolved its skills and
expertise as required by the rapid changes in
advertising formats, practices, and consumption
patterns. "The world is always changing," says
Seth Demsey, CTO at AOL Platforms. "Having
a strong connection between the content and
the consumer, no matter what the channel, and
having timely and relevant messaging is key.
And you need to use the right tool for the right
job. Today, the shiny object is deep neural
networks but we use a variety of AI tools to get
the job done."
One area where AOL Platforms employs neural
networks is the prediction of demographics for
consumers, allowing AOL's advertisers to better
target their ads, according to Rob Luenberger,
Chief Scientist and Senior Vice President,
Research and Development. "Based on data
provided by consumers who have opted in,
we have some demographics information, the
history of the web sites they visited, zip codes,
etc. Using neural networks, we can then predict,
for example, the chance of a person falling
into a certain age bucket," he says. "And we've
found that neural networks perform better than
more traditional machine learning models such
as decision trees," the multi-layered analysis of
neural networks proving more effective than
decision trees' sequential analysis of decisions
and their possible consequences.
The data scientists at AOL Platforms use
another AI tool, natural language processing,
to automatically analyze the content of a Web
page and even assess its mood and tone,
helping to match advertising with web content.
AI-driven natural language interfaces are also
used to automate the answers to questions
AOL Platforms' customers have about their
advertising campaigns-for example, queries
about the relationship between the number of
views that a particular person has of a particular
ad and the chance of that person clicking on the
ad. The tool "understands a question phrased in
38% of enterprises are already using AI technologies
and 62% plan to use them by 2018. (National Business
a number of different ways and to some extent
it understands the context of the content,"
Luenberger says. "If I ask how many impressions
campaign x got yesterday, it gives the answer.
And then if I ask how many clicks, it understands
that I'm still asking about the same campaign."
Adds Demsey: "It's like a natural language
To the enterprise
Artificial intelligence has arrived.
As the various uses in the enterprise described
here make clear, though, AI is far from intelligence
that is equal or superior to human intelligence
but rather enables practical applications, big and
small, in a wide variety of industries.
As with any other new-new thing, technology
executives must be aware of potential pitfalls and
be prepared for the difficulties of-and possible
organizational resistance to-adopting new tools
and becoming accustomed to new practices.
Machine learning can draw
a picture and analyze
events, trends and activities,
providing insights we
couldn't even imagine before.
Manager of Enterprise
Logging at Cox Automotive
A first step in putting the power of artificial
intelligence to work in your enterprise is to
identify specific activities that could benefit from
the predictive power of modern machine learning
tools. Look for "the thin edge of the wedge
of AI in your business," recommends Catalyst
Paper's Einarson, opportunities to demonstrate
immediate impact and a clear ROI for AI.
"Look for real-world examples where the data is
of suitable complexity and a human is likely to
miss trends," says Einarson.
Similarly, AT&T's Gilbert advises looking first
at the places where your organization "does
things inefficiently." Machine learning "is not
a solution to all problems," he warns. "But for
repeatable problems, there is a huge opportunity
for machine learning and AI." As we have
Getting Ready for "Codified Consciousness"
Sarah Burnett, Vice President of Research at
Everest Group in London, focuses her research on
digitalization and automation. Here Burnett - chair of
"BCS [British Computer Society]) Women" and one
of Computer Weekly's "50 most influential women in
U.K. IT" - shares her views on the impact of artificial
intelligence on organizations.
What is the likely effect of artificial intelligence on
how work is done in organizations?
The use of AI will change the way that we work.
That is for sure. One of the biggest challenges all
organizations will face is how to manage people
and skills over the next five to ten years. I mean
everything from the total number of employees
to recruitment levels, from developing skills and
creating new roles to succession planning. Thanks
to automation, the number of people needed to
deliver lower-skilled transactional work is going to
decrease, leading to a smaller base at the bottom of
the traditional organizational pyramid.
Add AI to the mix, and the number of people needed
to deliver some knowledge-intensive work will
decrease, shrinking the middle layer of the pyramid.
The decline in the number of junior-level positions
means organizations will have to rethink their hiring
processes. But they also will need to figure out how
to grow and develop staff to fill senior levels in
Another impact on work is the "watching and
learning" nature of AI. Some of the learning done
by AI involves watching what employees do, and in
the process it is probably able to assess and identify
potential performance problems. HR departments
and business leaders will have to work out how to
deal with this.
How can organizations make the transition to
automation and AI while maintaining their ongoing
AI means a move towards what we call "codified
consciousness" in IT infrastructure services
automation. Organizations have to adapt to
this changing world and develop new skills and
capabilities. Automation software applications
are designed to integrate with most systems. The
usual route is to start small, trial automation by
doing a proof of concept, then scale up using the
lessons learnt from the PoC. When it comes to
smart automation, there is a period of learning for
the AI before it can actually start to work. During
that period, it will be watching and learning and
can be deployed gradually, by activating it in test
environments, then testing and validating its actions
before moving into live processes.
What sort of AI investments should organizations
Organizations need a coherent and business contextcentered IT infrastructure service automation
strategy. A disjointed strategy will inhibit enterprises
from realizing the benefits of IT infrastructure
investments. For example, you might have a strategy
for adopting some kind of X-as-a-Service model, but
without a coherent strategy you end up with little
automation and find it hard to efficiently manage its
I also recommend taking a 360-degree approach to
investments. Investing in technology is one thing but,
with AI, it is also very important for organizations
to invest in training for general awareness about AI
at all levels of the organization. I do not mean just
the people who would deploy AI but all who will be
impacted by it.
Where should organizations start in trying to
leverage the benefits of AI?
There are certain use cases that have become
synonymous with uses of AI in an IT setting-for
example, AI handling L1 and L2 problem resolution
and reducing resource requirements and costs. But
the typical use cases only scratch the surface of AI's
potential. Just as AI engines need to learn about us,
we need to learn about them and what they can do
for us. That requires a new organizational mindset. At
present, most businesses typically try to shoe-horn
technology into existing operational and business
models - doing things in the old way but with new
technology. As we learn more about AI, we need to
keep an open mind and be ready to accept how we
can do things in entirely new ways.