Artificial Intelligence to Increase New York Sales Leads by 2,930%", reads an article in HBR this May. Today’s
leading organizations are using machine learning–based tools to automate
decision processes, and they’re starting to experiment with more-advanced uses
of artificial intelligence (AI) for digital transformation. AI is already
transforming web search, advertising, e-commerce, finance, logistics, media,
Here was the status of what
AI currently could do (as of November 2016) as per a founding Lead of the
Google Brain team:
Are there human faces?
Will they repay the loan?
Ad plus user information
Will user click an ad?
Targeted online ads
Transcript of audio clip
Sensor from hard disk,
Is it about to fail?
Car camera and other
Position of other cars
Corporate investment in
artificial intelligence is predicted to triple in 2017, becoming a $100 billion
market by 2025. Last year alone saw $5 billion in machine learning venture
investment. In a recent survey, 30% of respondents predicted that AI will be
the biggest disruptor to their industry in the next five years. This will no
doubt have profound effects on the workplace.
Machine learning is
enabling companies to expand their top-line growth and optimize processes while
improving employee engagement and increasing customer satisfaction.
Here are some possible applications of AI to Businesses today:
service. The potential to improve customer service
while lowering costs makes this one of the most exciting areas of opportunity.
By combining historical customer service data, natural language processing, and
algorithms that continuously learn from interactions, customers can ask
questions and get high-quality answers. In fact, 44% of U.S. consumers already
prefer chatbots to humans for customer relations. Customer service
representatives can step in to handle exceptions, with the algorithms looking
over their shoulders to learn what to do next time around.
Improving customer loyalty
and retention. Companies can mine customer
actions, transactions, and social sentiment data to identify customers who are
at high risk of leaving. Combined with profitability data, this allows
organizations to optimize “next best action” strategies and personalize the
end-to-end customer experience. For example, young adults coming off of their
parents’ mobile phone plans often move to other carriers. Telcos can use
machine learning to anticipate this behavior and make customized offers, based
on the individual’s usage patterns, before they defect to competitors.
Hiring the right people. Corporate job openings pull in about 250 résumés apiece, and over half
of surveyed recruiters say shortlisting qualified candidates is the most
difficult part of their job. Software quickly sifts through thousands of job
applications and shortlists candidates who have the credentials that are most
likely to achieve success at the company. Care must be taken not to reinforce
any human biases implicit in prior hiring. But software can also combat human
bias by automatically flagging biased language in job descriptions, detecting
highly qualified candidates who might have been overlooked because they didn’t
fit traditional expectations.
Automating finance. AI can expedite “exception handling” in many financial processes. For
example, when a payment is received without an order number, a person must sort
out which order the payment corresponds to, and determine what to do with any
excess or shortfall. By monitoring existing processes and learning to recognize
different situations, AI significantly increases the number of invoices that
can be matched automatically. This lets organizations reduce the amount of work
outsourced to service centers and frees up finance staff to focus on strategic
Measuring brand exposure. Automated programs can recognize products, people, logos, and more. For
example, advanced image recognition can be used to track the position of brand
logos that appear in video footage of a sporting event, such as a basketball
game. Corporate sponsors get to see the return on investment of their
sponsorship investment with detailed analyses, including the quantity,
duration, and placement of corporate logos.
Detecting fraud. The typical organization loses 5% of revenues each year to fraud. By
building models based on historical transactions, social network information,
and other external sources of data, machine learning algorithms can use pattern
recognition to spot anomalies, exceptions, and outliers. This helps detect and
prevent fraudulent transactions in real time, even for previously unknown types
of fraud. For example, banks can use historical transaction data to build
algorithms that recognize fraudulent behaviour. They can also discover
suspicious patterns of payments and transfers between networks of individuals
with overlapping corporate connections. This type of “algorithmic security” is
applicable to a wide range of situations, such as cybersecurity and tax
Predictive maintenance. Machine learning makes it possible to detect anomalies in the
temperature of a train axel that indicate that it will freeze up in the next
few hours. Instead of hundreds of passengers being stranded in the countryside,
waiting for an expensive repair, the train can be diverted to maintenance
before it fails, and passengers transferred to a different train.
Smoother supply chains. Machine learning enables contextual analysis of logistics data to
predict and mitigate supply chain risks. Algorithms can sift through public
social data and news feeds in multiple languages to detect, for example, a fire
in a remote factory that supplies vital ball bearings that are used in a car
Other areas where machine
intelligence could soon be commonly used include:
Career planning. Recommendations could help employees choose career paths that lead to
high performance, satisfaction, and retention. If a person with an engineering
degree wishes to run the division someday, what additional education and work
experience should they obtain, and in what order?
Drone- and satellite-based
asset management. Drones equipped with
cameras can perform regular external inspections of commercial structures, like
bridges or airplanes, with the images automatically analysed to detect any new
cracks or changes to surfaces.
Retail shelf analysis. A sports drink company could use machine intelligence, coupled with
machine vision, to see whether its in-store displays are at the promised
location, the shelves are properly stocked with products, and the product
labels are facing outward.
Machine learning enables a
company to reimagine end-to-end business processes with digital intelligence.
The potential is enormous. That’s why software vendors are investing heavily in
adding AI to their existing applications and in creating net-new solutions.
- gleaned from the pages of HBR