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Unformatted text preview: Artificial Intelligence (AI) is the ability of a computer or machine to simulate human intelligence. It is
dependent upon three elements: massive amounts of data, sophisticated algorithms and high
performance parallel processors (Bookbinder, 2017). Utilizing these elements, computer systems learn
to identify and classify input patterns, probabilistically predict and operate unsupervised.
AI is classified into two fundamental groups – applied and generalized. Applied AI is commonly defined
as an application of AI that enables a high functioning system to replicate, and perhaps surpass, human
intelligence for a dedicated purpose. Generalized AI is an application of AI that enables general purpose
systems to think with an intelligence comparable to that of a human mind. It is intended to demonstrate
understanding and reasoning skills with a breadth and depth of knowledge that allows it to easily
transverse between vastly unrelated topics and use cases just as humans can (Young, 2017).
Generalized AI has led to the development of machine learning and deep learning. Both machine and
deep learning use neural networks to classify information the same way a human brain does. Neural
networks can be taught to recognize, for example, images and classify them according to elements they
contain. Essentially, they work on a system of probability – based on data it is fed, it is able to make
statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables
learning – by sensing or being told whether its decisions are right or wrong, it modifies the approach it
takes in the future (Marr, 2016a). Deep learning differentiates itself by focusing even more narrowly on
a subset of machine learning tools and techniques and applying them to solving any problem that
requires thought – human or artificial. Because deep learning is focused on further developing neural
networks, they become what are known as Deep Neural Networks (DNNs) – logic networks of the
complexity needed to deal with classifying datasets as large as, say, Google’s image library (Marr, 2016b).
Some examples of how machine and deep learning are being used today are the navigation of selfdriving cars, precision medicine and fraud detection.
Disruptive Innovation Theory
Over the past year, AI has transformed at a rapid pace. Machines are now capable of processing and
analyzing data sets larger than we once thought possible. This can be attributed to a significant increase
in processing speeds, the progression of machine learning algorithms and diminishing technology costs.
This has had a significant impact on industries that rely heavily on data, such as financial, retail and
transportation industries. According to research, the financial industry spends approximately 50% of
their time processing and collecting data. The ability of AI to significantly improve this processing time
will inevitably disrupt this industry and those alike (HG, 2018).
AI’s ability to simulate, and potentially surpass, human intelligence has transformed the way businesses
in almost every industry operate. Thanks to cloud computing, massive amounts of data are able to be
stored and processed at a fraction of the cost. This allows AI applications to interpret increasingly rising
data volumes at manageable costs. Because of this, I believe AI represents a disruptive technology. At
this time, I do not believe there are any technologies that might prove disruptive to AI. However, as
computing costs continue to shrink and machine learning algorithms advance, the pace of innovation will
accelerate. With that being said, new applications of AI have the potential to disrupt those already in
existence. Innovation Attributes
AI has the potential to add value to every industry. From finance to healthcare, its ability to enhance
efficiency and make faster, more informed decisions is transforming the way industries operate. Tasks
previously performed by humans can now be performed by machines faster and more accurately. In
addition, machines do not experience emotions or require breaks – allowing them to make rational
decisions 24 hours a day, 7 days a week. Through the use of machine learning patterns in large data sets
can not only be identified, they can also be acted upon. Lastly, AI is not prone to decision fatigue,
allowing an infinite number of decisions to be made with inherent accuracy (Chou, 2018).
The evolution of cloud computing is allowing companies to develop and use AI applications at a
manageable cost. The cloud can help provide AI with the information which they need to learn, while AI
can provide the cloud with more data. In addition, increased processing power has made it possible for
AI to execute tasks at speeds once unimaginable – at a cost that has fallen rapidly. Lastly, mobility and
bandwidth ubiquity have made it possible for workers to access applications from most remote locations
(Sallomi). All of these technological enhancements have made it easier for AI to be implemented.
Despite AIs rapid growth, there is still a widespread lack of familiarity. According to West and Allen
(2018), “When 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17
percent said they were familiar with it. A number of them were not sure what it was or how it would
affect their particular companies. They understood there was considerable potential for altering
business processes, but were not clear how AI could be deployed within their own organizations.” As
companies continue to experiment with AI, effective ways to implement it will become clearer.
Organizations in almost every industry have begun investing in AI tools and techniques to improve
business operations. One of the most well known company’s utilizing AI is Amazon. With its algorithms
refined more and more with each passing year, the company has gotten acutely smart at predicting what
customers are interested in purchasing based on their online behavior. Because of reductions in
technology costs, smaller companies now have the opportunity to utilize AI in a similar manner.
Despite its numerous benefits, adoption of AI remains relatively low. One of the biggest barriers to
implementation is unclear examples of success among similar businesses (HG, 2018). For the most part,
many of today's AI systems are merely advanced machine learning software with extensive behavioral
algorithms that adapt themselves to our likes and dislikes. While extremely useful, these machines
aren't getting smarter in the existential sense, but they are improving their skills and usefulness based on
a large dataset (Adams, 2017). As companies gain an understanding of how AI can successfully be
integrated into their business process, observability will increase. Technology S Curves
The concept of AI has been around for the better part of half a century. One of the best known early
applications of AI was in 1959, when Arthur Samuel, an IBM scientist, published a solution to the game
of checkers. For the first time, a computer could play checkers against a human and win. Other early AI
attempts used computers to mimic human behavior through rule-based methods, which applied logicbased algorithms to tell a computer to “do this if you observe that.” Today, logic based machine learning
is being replaced with a data based approach. In a data based approach a computer learns directly from
the data it is fed. Using this approach, answers to problems are achieved through recognition of patterns
and common associations in the data (Bauguess, 2017). This new data based approach can be
considered a disruption to the original logic based approach.
For a technology to have a dominant design it must have a stable set of features and attributes. As far as
AI software, there are currently many different products offered by a range of vendors. Big players such
as Google and IBM continue to add major components in an attempt to be recognized as the dominant
design for the future. Looking at AI applications, use cases are being developed in various settings with
various stages of maturity and success (Roelands, 2018). This experimentation of different product
designs indicates that AI is in the fluid phase of the S-Curve technology cycle.
As computing costs continue to diminish and machine learning algorithms advance, the pace of AI
innovation will accelerate. As companies are able to successfully implement applications of AI within
their business processes, the adoption rate will begin increasing. In addition to observable success, the
ability of companies to build trust among consumers will be critical in increasing the adoption of AI.
Despite the number of benefits AI offers, its adoption rate remains relatively low. This can be attributed
to competing investment priorities and unclear examples of success among similar businesses (HG,
2018). According to a recent survey conducted by MIT and the Boston Consulting Group, there is a
significant gap that exists between an organization’s AI ambitions and its ability to execute on them, with
just one in five companies merging AI solutions with their processes. Another survey conducted by
Forrester found that 58% of business and tech professionals are researching AI systems but only 12% are
currently using them (as cited in Gordon, 2018). Because of the small percentage of businesses actually
utilizing AI, I would place it within the Early Adoptors phase of Roger’s diffusion model. Early adoptors
are those who represent opinion leaders. They are aware of the need to change and comfortable
adopting new ideas.
Although AI is ready to use and has been shown to meet customer demands, there is still a great deal of
skepticism among consumers. For example, a survey of more than 1,000 car buyers in Germany showed
that only 5% would prefer a fully autonomous vehicle. With that being said, I believe trust must be built
among consumers before AI can cross the chasm. Three factors will be crucial in gaining this trust:
performance, the application performs as expected; process, there is an understanding of the underlying
logic of the technology; and purpose, there is faith in the design’s intentions (Enkel, 2017).
After thorough analysis, I believe AI is likely to emerge as a technology within the next decade. As
automation becomes increasingly sophisticated and technology costs continue to diminish, AI will
continue to transform the operations of almost every industry. The use of AI in determining customer preferences has transformed our shopping and entertainment experiences, with no signs of slowing
down anytime soon. This application of AI has been proven successful by large companies such as
Amazon and Netflix. This success, in conjunction with reduced technology costs, has encouraged small
and medium retail businesses to begin adopting similar applications of AI. AI is capable of far more than
predicting customer preferences. Large financial institutions have invested billions of dollars in AI over
the last year in order to upgrade their systems, protect their data and improve mobile banking and other
digital customer experiences. Banks are now utilizing AI for things such as fraud detection, data requests
and fund transfers. The continued success of these developing AI applications will build trust among
consumers which, in conjunction with diminishing costs, will encourage the adoption of AI in businesses
large and small.
Artificial intelligence is continuing to transform almost every industry, and the future remains seemingly
limitless. Specifically, AI has had a significant impact on industries that rely heavily on data, such as the
financial services industry. This is because the use of machine learning and deep learning to identify
patterns is much easier to adopt in a data rich environment. Not to mention, half of the time spent by
workers in the financial industry is allocated to collecting and processing data. AI is being utilized in the
following aspects of financial services.
The financial services industry has long been plagued by the issue of fraud. A record 16.7 million
US adults experienced identity fraud in 2017. The amount of fraudulent transactions, massive data
breaches, and instances of identity theft continues to rise as hackers and fraudsters become more
sophisticated. Artificial intelligence and its subsets of machine learning and deep learning make it
possible to accurately process, verify, and authenticate identities at scale (Larson). For example, CitiBank
has made a strategic investment in Feedzai, a leading global data science enterprise that works in realtime to identify and eradicate fraud in all avenues of commerce including online and in-person banking.
Through its continuous and rapid evaluation of large amounts of data, Feedzai can conduct large-scale
analyses. Fraudulent or questionable activity is identified and the customer is rapidly alerted. Feedzai is
able to do so through the use of “machine-based learning” to evaluate “big data” and potentially
fraudulent activities (Sennaar, 2018).
Robotic Process Automation (RPA) is one of the key drivers of process automaton in the financial services
industry. Through the use of AI, RPA is evolving into a cognitive process automation that allows systems
to perform more complex automation. JPMorgan Chase recently invested in a new technology called
COiN that reviews documents and extracts data in much less time than it would take a human. This tool
reviews about 12,000 documents (which, without automation, would require more than 360,000 hours
of work) in just seconds (Maskey, 2018). Trading AI has become prominent in stock exchanges. High-frequency trading by machines has replaced much of
human decision making. People submit buy and sell orders and computers match them in the blink of an
eye without human intervention. Machines can spot trading inefficiencies or market differentials on a
very small scale and execute trades that make money according to investor instructions. Powered in
some places by advanced computing, these tools have much greater capacities for storing information
because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in
each location. That dramatically increases storage capacity and decreases processing times (West &
As AI continues to evolve in the workplace, employees will have to evolve their own roles as well.
Mundane tasks will be able to be performed by computers, freeing up time for employees to focus on
more complex tasks. In an ideal world AI would only remain a supplement to employee roles, rather
than a complete replacement. Time will tell the impact this great technology will have on the
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