Annals of Operations Research
https://doi.org/10.1007/s10479-020-03620-w
S.I.: ARTIFICIAL INTELLIGENCE IN OPERATIONS MANAGEMENT
Transforming business using digital innovations:
the application of AI, blockchain, cloud and data analytics
Shahriar Akter1 · Katina Michael2 · Muhammad Rajib Uddin1 ·
Grace McCarthy1 · Mahfuzur Rahman3
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This study explores digital business transformation through the lens of four emerging technology fields: artificial intelligence, blockchain, cloud and data analytics (i.e., ABCD).
Specifically, the study investigates the operations and value propositions of these distinct but
increasingly converging technologies. Due to the dynamic nature of innovation, the potential
of this ABCD hybridization, integration, recombination and convergence has yet to be considered. Using a multidisciplinary approach, the findings of the study show wide-reaching
and diverse applications among a variety of vertical sectors, presenting exploratory research
avenues for future investigation. The study also highlights the practical implications of these
new technologies.
Keywords Digital transformation · Artificial intelligence · Blockchain · Cloud computing ·
Data analytics
B Shahriar Akter
[email protected]
Katina Michael
[email protected]
http://www.katinamichael.com
Muhammad Rajib Uddin
[email protected]
Grace McCarthy
[email protected]
Mahfuzur Rahman
[email protected]
1 Sydney Business School, University of Wollongong, Sydney, NSW 2000, Australia
2 School for the Future of Innovation in Society, Arizona State University, Tempe 85287-5603, USA
3 Lincoln International Business School, University of Lincoln, Lincoln LN5 7AT, UK
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1 Introduction
Pity the enterprise whose fortunes are tied exclusively to the analog world, be it producing film, renting videos, retailing books, or selling packaged software (Narayen
2018).
Digital business transformation (DBT) is a strategy that is gaining attention as companies are
challenged to continually improve their business processes and capabilities. DBT stimulates
new modes of working and interactions with customers, directly driving the creation of new
business models. According to Weill and Woerner (2018), DBT can make firms future-ready
and enhance average net revenues by 16% more than traditional firms. Evidence suggests that
digitalization could add 1.25 trillion Euros to Europe’s industrial value creation (Schweer
and Sahl 2017) while Australia could generate $315 billion worth of economic opportunities
(Alphabeta Advisors 2018). DBT refers to the use of technology to radically improve the firm
performance of an enterprise (i.e., organizational performance, the functioning of the firm
and outcomes of its operations) (Westerman and Bonnet 2015). DBT is an enabler of business
transformation and has already introduced massive changes in business operations through
better customer service, payments, business models and new methods of online engagement.
In other words, it is not the use of technology as an end in itself that adds value but rather
the application of technology to enhance user customer experience. As Grewal et al. (2020,
p. 6) state that “Netflix might have demolished Blockbuster; Alibaba, Tencent, and Baidu
might be issuing credible threats to traditional banks; and Amazon might have revolutionized
businesses in a vast range of sectors, including supermarkets, publishing, and logistics. They
have done so by gathering and leveraging information to enhance customer experiences”.
DBT is a way of conducting business and transforming business from traditional to digital
(Li 2018). It is more than just changing from a ‘bricks and mortar’ shop front for customers to
a ‘clicks and bricks’ environment; digital transformation pervades all aspects of business by
adopting cutting edge and often converging technologies. Thus, the goal of digital transformation is basically business transformation—using digital capabilities to transform a traditional
enterprise into a top performer in the digital economy (Weill and Woerner 2018). The most
digitally advanced firms, such as Google, Netflix, Uber and Airbnb, have successfully developed and leveraged their digitized, open and participative business models, incorporated in
a connected ecosystem of producers and consumers. Goodwin (2015) describes DBT as an
ecosystem of platform innovations, in which “Uber, the world’s largest taxi company, owns
no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba,
the most valuable retailer, has no inventory. And Airbnb, the world’s largest accommodation provider, owns no real estate.” In this digital world, subscription services are preferred
to ownership of assets or goods with little in the way of inventory requirements nor the
costs associated with depreciation of those assets. Furthermore subscription models offer an
on-going revenue stream and vast amounts of customer data which enables companies to
constantly refine their offerings.
Although DBT can be wide in scope, this study focuses on digitizing the organization’s
business using four viable pathways which we put forward as ABCD technologies, that is
Artificial Intelligence, Blockchain, Cloud and Data Analytics (Martin 2017). These technologies are expected to transform businesses of the future. Indeed, this is already happening.
For example:
1. Organizations across all industries are investing in AI to automate value chain and serve
customers, which is expected to reach $191 billion by 2025 with a compound annual
growth rate of 36.6% (Markets and Markets 2019).
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2. Gartner estimates blockchain technology is accelerating at a fast pace which will deliver
business value of over $3 trillion by 2030 (Gartner 2019).
3. The dramatic rise of cloud migration suggests that this sector will reach $383 billion by
2020 (O’Neal 2018) with an annual growth rate of 22.8%.
4. A recent report suggests that 91.6% of Fortune 1000 companies are investing in big data
analytics with 55% of firms investing greater than $55 million to address the fear of
disruption (NewVantage Partners 2019).
Companies looking to digitally transform must determine how best to integrate ABCD technologies, and re-establish their operating model using a new more advanced way of doing
business (Berman 2012). Because of recent technological changes, companies need to rethink
the implications of ABCD technologies, which are the key to success in the emerging digital
economy. There has been a paradigm shift in business strategy due to the emergence of nextgen technologies, given their emphasis on the provision of data, propelling insights toward
competitive advantage. This study will focus on four viable pathways for transformation by
exploring the merits and demerits of each. We choose ABCD as the critical next-gen technologies due to their interconnectivity and relationship to data-driven decision making in
business. While firms have been applying ABCD technologies for business transformation
in isolation, there is a paucity of research on their operational use cases, integrated applications, challenges and business opportunities (Kumar et al. 2020; Grewal et al. 2020). Thus,
the study puts forward the following research questions, which are significant from a digital
business transformation perspective:
• What are AI, blockchain, cloud computing and data analytics (ABCD) and how do they
work?
• How do ABCD technologies operate to transform business?
• What are the opportunities and challenges that ABCD technologies provide?
To answer these exploratory questions, we discuss the nature and attributes of ABCD technologies that can transform the future of business. We have structured our discussion as
follows. First, we define and discuss digital business transformation and its applications in
various industries. Second, we discuss AI and its two applications: machine learning (ML)
and deep learning (DL) with business cases. Next, we discuss blockchain, cloud computing
and data analytics with applications. Examples of ABCD technologies already deployed in
business are represented in tables in each section, providing evidence for the opportunities
that have arisen for many varied vertical sectors by adopting a digital transformation strategy.
Finally, we discuss the challenges and limitations of ABCD technologies and future research
implications.
2 Literature review
2.1 Defining digital business transformation
Digital business transformation (DBT) is defined as the use of technology to radically improve
the performance of organizations, redefine and recreate value propositions using Enterprise
Resource Planning (ERP) and Customer Relationship Management (CRM) or, leveraging
digital frontiers, such as smart devices, mobility or analytics for intra-/extra-/inter-business
processes (Westerman et al. 2014). In a similar vein, von Leipzig et al. (2017) defined DBT
focusing on transforming business models while Li (2018) highlighted new ways of doing
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business. Similarly, Basole (2016) and Singh and Hess (2017) explored emerging technological factors driving digital transformation. Sebastian et al. (2017) identified social, mobile,
analytics, cloud and Internet of things (SMACIT) as fundamental driving forces of DBT.
However, these studies primarily focused on the technological dimensions rather than linking them to business value, firm performance or strategic alignment. To address this gap,
Nadeem et al. (2018) conducted a systematic review and found that digital transformation is
intricately interlinked with digital business strategy (e.g., cross-functional integration, structural changes) and organizational capabilities (e.g., talent and operational capabilities). DBT
focuses not only on incorporating robust technologies but also articulating a clear vision,
transforming the business model, developing dynamic capabilities and understanding customers. In defining digital transformation, Kumar et al. (2020) focus on digital thinking across
all operations, Davenport and Spanyi (2019) highlight customer-centric digital products and
services and Verhoef et al. (2019) shed light on a new digital business model to create more
value. Overall, we define DBT as the reconceptualisation of a business model using digital
technologies to create, communicate and deliver value. Table 1 shows various dimensions of
DBT definitions and their applications across industries.
2.2 Digital business transformation in various industries
Digital business transformation using ABCD has already impacted various industries. For
example, the healthcare industry has achieved positive outcomes from digital transformation
enabling high-quality patient care, electronic health records (EHRs), digital imaging and
prescriptions, giving more access to historical and real-time information to provide better
and secure services (Haggerty 2017). DBT can transform health care in various ways, such as
avoiding unnecessary hospital stays, improving care delivery models using big data analytics
and reductions in cost. For example, Kaiser Permanente uses an electronic medical record
service which is more consistent and provide better clinical practice than previous paperbased systems. The introduction of digital transformation in health care is said to reduce
cost, improve patient outcomes and improve efficiency, thereby providing a benefit of $1.76
billion in Australia (Forsythe et al. 2016).
In addition to healthcare, sectors such as manufacturing where the main reason for digitalization is the reduction of cost, cloud applications play a vital role in internal management
and communications (Schwertner 2017). Manufacturers are also using analytics to make the
best use of equipment, reduce waste of materials and other inputs, advance supply chain networks and improve efficiency. For example, the automobile industry is struggling to compete
against disruptive car manufacturers like Tesla and Faraday Future. Any long-established
manufacturer now understands that it is vital to combine digital technologies with traditional
processes to stay ahead of their competitors. For example, Audi gained massive advantages
by applying digital transformation in sales, marketing and operations, enabling them to better
meet local demand (Dremel et al. 2017). Metal plant companies use the power of digitalization
to increase production rates by visualizing performance, streamlining operations and obtaining insights into causes of failures (Hartmann et al. 2015). Pharmaceutical manufacturers are
now using less manufacturing space, and quality control has increased as easy detection of
counterfeit medicines and chemicals can be provided. The consumer packaged goods industry has also achieved improvements through digital transformation by becoming closer to
their customers and forming longer-lasting relationships that equate to repeat business and
higher satisfaction (Kumar et al. 2020). Consumers experience faster response times through
better channels of distribution with a huge reduction in cost. For example, instead of 40
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Table 1 Definitions of digital transformations
Study Definitions Purpose(s) Research areas
Ashwell (2017) DBT can be referred to
as the
interrelationship
between data, digital
technology and
people
Activity based
intelligence (ABI)
models for better
understanding data
and the use of
information
technology for
understanding and
countering organised
criminal networks
Small and medium size
companies.
Consumer behaviour
Basole (2016) DBT occurs through
four tectonic
technological factors
such as mobile,
social, analytics and
cloud for reshaping
businesses
Accelerating digital
transformation
through application
programming
interfaces (API)
Strategies for adopting
an API ecosystem in
business. Adopting
API in machine
learning and
marketing analytics
Gölzer and Fritzsche
(2017)
DBT can be explained
in terms of Industry
4.0 which includes
components such as
Internet of things and
big data solutions
Big data implication in
industrial operations
management
Customer service,
e-commerce,
customer demand
Heilig et al. (2017) DBT can refer to
digitalization and
transformation of an
organisation or a
network of
organisations through
a variety of contexts:
cultural,
technological,
governance strategy
Using game theory in
maritime logistics
environment through
intra, inter and
meta-level analysis
for driving digital
transformation in
seaports
Operational
management
Li (2018) DBT refers to transforming/replacing
traditional ways of
doing business into a
digital one
Transforming the
creative industry
through digital
transformation
Creative industries such
as architecture,
advertising,
publishing, design,
fashion design,
software, games
development
Nadeem et al. (2018) DBT links digital
technologies with
business strategy and
organizational
capabilities
Identify various
sub-dimensions of
digital technologies,
digital business
strategy and
organizational
capabilities
E-commerce
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Table 1 continued
Study Definitions Purpose(s) Research areas
Reddy and Reinartz
(2017)
DBT is defined as the
use of the Internet
and computers for
achieving economic
value. In other words,
it is the
transformation of
operations,
interactions,
configuration and
wealth creation
Creating value through
digital transformation
Manufacturing
companies
Schwertner (2017) DBT in business can
refer to the
application of
technology for
making new business
models through
processes and
software systems,
which can result in a
profitable outcome
Applying technology in
all aspects of
business, especially
in mature digital
businesses through
digital integration
General business
management
Sebastian et al. (2017) An aging company with
legacy technology,
needs to be digitalised
by considering
SMACIT (social,
mobile, analytics,
cloud and internet of
things) to achieve
digital transformation
Customer engagement,
digitalized solution
and
technology-enabled
assets for
transforming business
towards digitalization
General business
management
Singh and Hess (2017) DBT occurs when a
company applies
digital platforms
using social media,
mobile access,
analytics and
embedded devices
The roles that chief
digital officers (CDO)
should have in a
company when
shifting to digital
transformation
Skills and competency
needed for the chief
digital officer.
Identifying the
important role CDOs
should play in a
company.
Human-resource
training on
digitalisation
von Leipzig et al.
(2017)
Digitisation shapes a
part of Industry 4.0,
which reshapes a
business model for
better efficiency and
effectiveness through
overcoming barriers
of digitalisation
Analytics to gain
competitive
advantage,
Developing a model
for digitalisation for
companies without a
clear vision of
incorporating digital
strategy
Service sectors,
reinventing their
business model
toward the creation of
a strategic model that
is driven by digital
transformation
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Table 1 continued
Study Definitions Purpose(s) Research areas
Westerman et al. (2014) DBT focuses on using
emerging
technologies to
radically enhance
firm performance
Identifies the core
aspects of DBT as
follows: operational
processes (i.e.,
process digitisation,
worker enablement,
performance
management),
business models (i.e.,
digitally modified
businesses, new
digital businesses,
digital globalization)
and customer
experience (i.e.,
customer
understanding, top
line growth, customer
touch points)
Operations, marketing
and sales, new
business models and
digital consumer
behaviour
employees, ten employees can now do the same task within a shorter time frame, eventuating
in far lower operating costs. The defence industry has also gained tremendous advantages
through the introduction of digital tools which help their complex supply chain networks
by enabling information sharing and collaboration among suppliers (Hartmann et al. 2015).
Table 2 shows digital transformation as implemented in diverse industry sectors.
3 Drivers of digital transformation
3.1 Artificial intelligence (AI)
AI can be traced back to 1950 when English polymath Alan Turing invented a test to determine
if the machine could mimic human cognitive functions (Batra et al. 2018), thus giving the
world a preview of the possibilities which might become available with the advent of higher
computing processing power. The theory of AI has been in development for many years,
with its roots in 1956 (Cohen and Feigenbaum 2014). Several authors have explored the
implications of AI (Nilsson 2014). It can be defined as machines which have human-like
intellectual capacities (McGettigan 2016). It is a combination of computing technologies
converging to enable rational decision-making in complex situations and contexts (Tredinnick
2017).
Over the years computers have been increasingly able to perform high level tasks which
are comparable to humans, like solving mathematical problems, driving vehicles, understanding languages, and conducting commonsense reasoning. A machine that has AI capabilities
must have a few core components, including the ability to conduct natural language processing (NLP), data retrieval from massive databases, proving mathematical theories, automatic
programming, solving critical problems and diagnosing diseases (Nilsson 2014). Although
three-quarters of executives believe that AI will help businesses further develop and enable
them to achieve a competitive advantage, research states that companies are yet to put AI into
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Table 2 Digital transformation in various industries
Firm Type of industry Product/services Application of digital
transformation
Audi (“Audi” 2016) Automobiles Audi City The German giant
gained 60% more
sales by providing a
digital experience in
traditional showrooms
in given locations,
such as Berlin,
London and Beijing
McKinsey Solutions
(“Audi” 2016)
Management consulting Software and
technology-based
analytics
McKinsey solutions
provide software and
analytic solutions to
business for
improving
benchmarking,
pricing and
promotional strategies
KPMG (“Audi” 2016) Business consulting Watson cognitive
computing platform
KPMG uses IBM’s
Watson computing
platform to improve
its professional
services such as
auditing. KPMG can
now analyse a large
amount of data
providing the
company with more
insights
Kensho (World
Economic Forum
2017a, b, c)
Technology Analytic software The company uses big
data and machine
learning for analyzing
real-world events on
financial markets,
providing complex
financial queries
Argos (World Economic
Forum 2016)
Retail Digital stores The UK based retailer
transformed five of its
stores, providing
customers with a
quick and easy way to
shop
Disney’s magic bands
(World Economic
Forum 2016)
Entertainment Smart wrist band The company provided
smart wrist bands for
personalized customer
experience in Disney
World resorts, which
led to a 20% increase
in profit in 2014
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practice (Rai 2020; Davenport and Ronanki 2018). One of the key reasons for this is data
extraction. AI can only work through learning from a vast amount of existing data. For example, Airbus used their AI system to examine a production problem, calculate a vast amount
of data, and come up with a solution and a recommendation (Ransbotham et al. 2017). Companies such as Bridgewater Associates are planning to use AI to automate key parts of their
operation, while KPMG Australia is going to automate some of its auditing services, and
law firm Baker & Hostetler will use AI to help boost their legal searches (Tredinnick 2017).
In order to achieve fully-fledged AI: the first step is to use big data, the second is to apply
analytics, and the third is prediction. AI needs data collection and storage in order to analyze
and make predictions. Companies specializing in IT, marketing, finance, accounting and sales
are using AI to become more competitive and efficient (Oana et al. 2017). For successful
AI transformation, business needs to adopt a better data ecosystem with data governance,
use cases with business value, analytics techniques and tools, workflow integration and an
ambidextrous organizational culture (Chui 2017). As shown in Table 3, AI can be used in a
wide range of different applications. Next, we consider one particular application in detail.
3.1.1 A case study on AI based digital transformation by Afiniti
Afiniti uses AI to predict patterns of interpersonal behavior for companies who are looking
for success in human interaction. The aim is to replace the first in first out (FIFO) caller
system which can cause drawbacks for customer service. Afiniti uses AI, big data analytics
and machine learning (ML) algorithms to analyze human behavior and uses the outcomes
for better pairing of customers with agents. Through their enhanced understanding of their
customers, Afiniti’s clients are able to tailor their services, ensuring better revenues and
improved retention rates for companies like T-Mobile and Virgin (Afiniti 2018).
Afiniti collects data from different vectors of communication, call history and CRM
records for customers around the world. It then combines interaction-level results from the
client’s data and uses specialized ML algorithms to identify different consumer behavior
patterns and predicts outcomes from their historical behavior. As the number of interactions
is quite low compared to the available data (which includes demographic data, interaction
data and internal analytics), relying only on the ML algorithm can produce results that are
unreliable. The system, therefore, runs the algorithm in real-time triggered by a consumer
call. Afiniti runs the process in under 200 ms which allows the caller to be connected with the
right agent. The outcomes of the call are recorded for future interaction with the customer,
leading to a better service experience. Figure 1 shows the AI-based operations of Afiniti using
private branch exchange (PBX) or automatic call distribution (ACD) software systems.
3.1.2 Machine learning as an application of AI
The term ‘Machine Learning’ (ML) was coined by Arthur Lee Samuel in 1995 (Syam and
Sharma 2018). ML is widely considered the prerequisite for developing AI applications. It
requires vasts amounts of data. It can be categorized as supervised learning where certain data
are provided to have an outcome, but it is a different case for unsupervised learning where data
are unstructured and unlabeled (Syam and Sharma 2018). Unsupervised machine learning
trains a machine to discover hidden patterns and structures without a target variable (Lim et al.
2017). For example, the company M6D uses this technology to target potential consumers by
displaying targeted advertising for hundreds of brands (Perlich et al. 2014). With the growth
of real bidding exchanges, an advertiser can target specific customer leads, known in social
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Table 3 Digital transformation cases using AI
Firms using AI Type of industry and
product
Applications of AI Context
AIME (Artificial
Intelligence Medical
Epidemiology)
Healthcare The AI-algorithm uses
research and data such as
insect borne-diseases,
population density, wind
speed direction, rain
volume and other
parameters to calculate
the outbreak of a disease
in a given area. This
helped the startup to
predict outbreaks such as
the dengue virus 3 months
in advance
USA
ROSS Intelligence Legal research application The AI app architected in a
supercomputer uses
natural language
processing to answer legal
questions. A similar task
would take a legal
assistant longer to
complete. Law firms such
as Baker and Hostetler are
already using the app for
their bankruptcy practice
USA
McCann Japan Advertising and marketing
agency
AI-CD β is an actual
AI-based robot that works
like an employee in the
company, tasked to
provide creative direction
for the creation of
commercials. The robot
can recall historical
adverts and help
employees make better
commercials
Japan
IVO Technology product: mood
box
A speaker called the mood
box, which uses human
mood to determine what
kind of music a person
might want to listen to.
Users interact with the
device by voice control
and keep a diary of their
moods using a
speaker-controlled app
Hong Kong
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Table 3 continued
Firms using AI Type of industry and
product
Applications of AI Context
NotCo Food manufacturing The company uses AI to
replicate meat and dairy
products by using plant
material. This is done by
examining the molecular
structure of the non-vegan
products. The company’s
aim is to provide
sustainable protein and
help reduce water wastage
and cruelty to animals
Chile
Webpage.ly Web page design and digital
service
The technology provides
affordable alternatives for
startup firms for their SEO
operations by using
algorithms. It analyses
users’ search behaviour
and page rankings to
suggest keywords that
enables developers to
produce higher impact on
SEO content
Canada
Tes4Startup Application By using AI, Test4Startup
can test the ability for a
business to succeed by
recommending strategies
on pricing and
competitors
Russia
apps as creating an impression. ML plays a vital role in the task by computing a massive
amount of data about consumer behavior, making a decision and then finally delivering
advertisements in near real-time. AI and ML have a positive impact on personal selling and
sales management. While many believe that AI and machine learning will eliminate jobs,
others believe that it will actually create over 2 million new jobs by 2025 (Syam and Sharma
2018). Sales management can become very efficient through ML with timely iterative detailed
reporting, and service data that can ease a salesperson’s job, allowing companies to translate
discoverable patterns and trends into action.
In healthcare, ML can prescribe howmany days a patient will stay in a hospital. This benefit
not only helps a patient plan for home care requirements but also provides the hospital with
efficient use of human resources and facilities. ML can significantly increase hospital bedding
efficiency thereby enabling a hospital to serve more patients, improve doctor-nurse-theatre
and scheduling of elective surgeries, and ultimately help with hospitals’ long term strategic
planning (Turgeman et al. 2017). Table 4 lists examples of the application of ML in a range
of industries.
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Fig. 1 How Afiniti operates using AI
3.1.3 A cases study on ML-based digital transformation by Netflix
Netflix does most of the computation for its recommendation system offline. It starts by
mining data from the user, creating hypotheses to decide which model to initiate, and finally
using different models to identify the match that is most appropriate to a user (Basilico and
Amatrian 2013). The next step is to train the model through supervised machine learning algorithms. Netflix uses both supervised and unsupervised algorithms for their recommendation
system (Basilico 2012). The learning happens in online, offline and nearline (an intermediary between online and offline computations) contexts, running massive amounts of data
through Hadoop, a software application for storing and processing big data. In this process,
the term signal is used for fresh information inputs in the algorithm, which can be done both
online and offline. These data are gathered from live services related to user information,
for example, data on what each customer has been watching (Basilico and Amatrian 2013).
Figure 2 shows how Netflix ML algorithms are used for model training, offline computations,
nearline computations and online computation (adapted from Basilico and Amatrian 2013).
All the data is stored using Cassandra, EVCache and MySQL, for real-time usage and also
for the purpose of prospective usage. Finally, the data is used to make recommendations to
customers (Basilico and Amatrian 2013).
3.1.4 Deep learning as an application of AI
The power of deep learning (DL) was first realized in 2011 when an algorithmic breakthrough occurred providing better visual patterns that were six times more efficient than a
human (Lemley et al. 2017). DL has the power to process data in their raw form, which is an
ability absent in conventional machine learning. The powerful use of a complex algorithm set
by DL has made it possible to improve tasks like visual object and speech recognition, object
detection, drug discovery and genomics and much more. DL uses a backpropagation algo-
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Table 4 Digital transformation cases using ML
Firms using machine
learning
Type of industry and product Applications of machine
learning
Context
PlayerXP Automated customer and
community intelligence
Player XP uses ML to aid in
the identifation of
constructive feedback in
mobile video game
reviews by analyzing
natural language, and
helps filter unhelpful
reviews
UK
Stanford University Product: Autism Glass The glass helps an autistic
person to recognize
emotion which makes
social interaction easier
USA
Amazon E-commerce ML is used for product
recommendation, supply
chain management,
forecasting and capacity
planning. It scans sensitive
data
Worldwide
Netflix Online movie streaming An algorithm called
Dynamic Optimizer, helps
reduce the amount of data
it takes to stream videos
Worldwide
Google Search engine Google is taking a step
further in ML by image
enhancement, which fills
in missing details in an
image by zooming into
project an enhanced reality
Worldwide
Salesforce CRM The company uses ML to
predict customer behavior,
recommend next actions
to users and automate
tasks. It uses customer
data, captures sales
activity, scores leads,
delivers content and sends
messages when customers
are most likely to engage
Worldwide
Walmart Anticipating customer need Anticipate customer need by
using facial recognition.
By adopting biometrics,
Walmart’s ML technology
can predict customer
emotion and provide
personalized services to
them
USA
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Table 4 continued
Firms using machine
learning
Type of industry and product Applications of machine
learning
Context
North Face Outdoor clothing retail Highly personalized
shopping experience by
using IBM Watson.
Consumers have to use a
mobile application
through which a virtual
assistant can ask the
consumer a series of
questions to provide the
best-personalized service
Worldwide
Fig. 2 Operations of Netflix using ML
rithm, which instructs how a machine can change its internal framework. It has outperformed
ML at the prediction of potential drug molecules through better processing and recognition
of image, videos and natural languages (LeCun et al. 2015).
The algorithms in DL have the capability of extracting high levels of data. DL enables the
analysis and learning from a huge amount of unsupervised data, which makes it an important
tool for big-data analytics. The algorithms used by DL are a deep architecture of consecutive
layers where each layer provides a nonlinear transformation of its input and then provides a
representation of its output (Najafabadi et al. 2015). These algorithms are significant because
of their capacity to generate multiple representations with high-level features representing
more abstract aspects of the data (Bengio 2013).
Neural Networks are often called DL as they perform more complex functions than traditional neural networks. Neural networks evolved through the availability of massively
advanced hardware like commercial graphic processing units (GPUs) which helped speed
up calculations in ML (Monroe 2017). Artificial neural networks are able to learn from what
they see and then can generalize that knowledge to provide an example of something that
they have never previously known. The networks have an input layer, an output layer and one
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Table 5 Digital transformation cases using DL
Firms using deep learning Type of industry and product Applications of deep
learning
Contexts
Affectiva Automated customer &
community intelligence
It uses DL to help identify
human emotion from
videos and images
UK
Gridspace Product: Autism Glass It uses DL networks for
sophisticated speech
recognition. DL is also
used for reconditioning
sophisticated speech, that
identifies speakers,
keywords, critical
moments and time spent
talking
USA
IBM Watson Predictive modelling IBM developed a computer
system called Watson,
which has the capability to
process unstructured data
and provide a solution to a
problem from the findings
Worldwide
Novartis Pharmaceutical company The pharmaceutical giant is
working with Intel to use
deep neural networks for
accelerating high content
screening which will help
to discover drugs faster
Worldwide
Zebra medical vision Medical imaging startup The company is raising
money to use deep
learning for building
radiologist equipment
Israel
Atomwise Pharmaceutical The company is using deep
learning for shortening the
process of drug discovery
and has raised over $45
million for the project
USA
Reason8 Mobile application AI-powered service for
automatic note-taking and
preparation of summaries
for in-person business
meetings
Australia
or more hidden layers, and are full of nodes which are connected to each other (Lemley et al.
2017). In business, DL has become very popular in business processes such as CRM, human
resources management (HRM), financial analysis, supplier management, fraud detection and
in managing distribution channels (Necula 2017). DL has also been used to predict financial
problems, such as risk management, construction portfolios, designing and pricing security
which often involve large data sets. By applying DL, financial modeling can be done with
far more precision than standard applications (Heaton et al. 2017). Table 5 lists examples of
DL in different contexts.
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Fig. 3 Operations of deep instinct using DL
3.1.5 A case study on DL based digital transformation by Deep Instinct
Deep Instinct, one of the first deep learning companies in the world is focused on cybersecurity. Using the power of DL based predictive capabilities, Deep Instinct can help companies
protect themselves from cyber threats, advanced persistent threats and zero-day threats, and
can run on servers, mobile devices and across a company’s endpoints. At the preparation
stage, data samples are prepared for the deep learning neural network which contains several
labeled files like malware, mutation etc. (Deep Instinct. 2018). Second, at the training stage,
raw data is trained through Graphics Processing Units (GPUs) which is faster than using
central processing units (CPUs). For example, data can be trained within 3 days as opposed
to weeks. Third, at the deep learning stage, the data is run through DL algorithms. Fourth, at
the detection stage, the neural networks begin to detect cyber threats through a continuous
training process. Fifth, at the prediction stage, the deep learning brain can now predict the
level of cyber threat a file may pose. Sixth, at the agent creation stage, the brain can turn
terabytes of insight into megabytes of instincts. At the seventh stage, at the agent insertion
stage, the agent is domain agnostic; hence it can be used for mobile device endpoints and
servers. Finally, at the agent protection and prevention stage, the agents check each and every
file, macros, scripts etc. The process is so fast that the users are not affected by its processing
which takes less than a millisecond. The ability of the agent allows the uncorrupted files to
run freely in the system with the ability to detect any type of threat (Fig. 3).
3.2 Blockchain as a driver of digital business transformation
Drawing on advanced cryptography, blockchain works as an open-source distributed database
(Kirkland and Tapscott 2016). Bitcoin is one of the most popular applications of blockchain
that runs on an open ledger (Kumar et al. 2020). This open-source platform allows anyone
to change the underlying code providing the opportunity for all participants to see what
is actually happening. In other words, it is a true peer-to-peer (P2P) system which does
not require intermediaries to authenticate or settle transactions. The system can record any
structured information, for example, who paid whom, who owns money to whom or which
light sourced power from which power source (Iansiti and Lakhani 2017). Blockchain is
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typically unhackable, which makes it a trusted platform although recent studies (e.g., Orcutt
2019) have reported the security concerns on some platforms.
The blockchain can actually reduce costs, for example, the cost of verifying the details
of a transaction and remove the cost of intermediaries (Michelman 2017). A blockchain
transaction works by representing a transaction as a block in the system, which is then
broadcast to every party in the network. When those who are in the network approve the
transaction, the block gets added to the chain, providing an ineradicable and transparent
record of a transaction, e.g., moving money from one party to another (Crosby et al. 2016).
The architecture of blockchain consists of continuous blocks in a sequential form which
holds transactions and records like those in a traditional public ledger. Blockchain is made up
of decentralized ledger technology (DLT), which is maintained by a peer-to-peer networks,
thus not being controlled or owned by any one particular authority. It is tamper-resistant,
and the user cannot lose control of the digital identities even if they lose access (Dunphy
and Petitcolas 2018). In addition to decentralization, blockchain technology has three further recognized characteristics: persistency, anonymity, and auditability. Persistency in the
blockchain is where falsification can be captured easily as transactions are checked, recorded
in blocks and distributed to the whole network. Anonymity in the blockchain supports users
as they are able to generate as many addresses as they want to avoid real identity exposure. Finally, auditability in the blockchain allows users to track and trace any transaction
by accessing any nodes in the distributed network providing tracing improvement and transparency of the data (Zheng et al. 2016). Overall, blockchain works on five principles that
determine the operation of this technology: irreversibility of records, computational logic,
transparency with pseudonymity, distributed database and peer-to-peer networks (Iansiti and
Lakhani 2017). Table 6 shows digital business transformation cases using blockchain.
3.2.1 A case study on blockchain-based digital transformation in banking
There are several issues with cross-border payments. One of the biggest challenges is the
payment investigation time which could be reduced by using distributed ledger technology
(DLT) (FARGO 2016). An example of blockchain as used by FARGO with ANZ bank is the
Nostro Reconciliation process, where two banks are involved in transactions using different
currencies for cross-border payments. Figure 4 shows the steps in the process which are
discussed in the following.
The first step of the process is to connect two or more entities via nodes. The purpose
of the nodes is to keep connection with each entity thus requiring a peer-to-peer network to
be established (Mills et al. 2016). Figure 4 shows the Nostro reconciliation process of two
banks, Bank A and B, where Bank A holds a Nostro account with Bank B and trades in Bank
B’s currency. In the first step, Bank A will start its transaction with Bank B via SWIFT (a
network which is used for transferring funds). At the same time, Bank A must create a linked
request in the distributed ledger for starting the transaction with Bank B, which records each
and every transaction via SWIFT. The records can later be confirmed by Bank B during cash
disbursement (Fargo 2016). Now Bank B can view all the transactions between Banks A and
B in real-time which previously used to take 24 h (Fargo 2016). In the third step, Bank B
disburses funds and simultaneously confirms requests in the distributed ledger system which
notifies Bank A immediately. The advantage of distributed ledger technology in payment
transparency is that it confirms the settlements between the financial entities and identifies
delays or problems with the transactions more quickly given the transparency.
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Table 6 Digital business transformation using blockchain
Firms using blockchain Type of industry and product Applications of blockchain Contexts
FedEx Courier delivery service Use blockchain to track
high-value cargo and solve
problems regarding
payments
World Wide
Burger King Fast food chain The brand uses blockchain
technology by introducing
Whopper coins to fuel their
reward program. Customers
can hold on to their reward or
sell them
Russia
Mastercard Financial services Using blockchain for secure
payment at point-of-sale
(PoS). Although this is yet to
be established the financial
giant is heavily exploring
blockchain technology
USA
JP Morgan Financial services The financial giant wants to
use blockchain to tackle the
issue of international
financial transactions, and
lower the cost of operation
World Wide
Huawei Technologies Mobile company The mobile giant wants to use
blockchain for privacy and
security
World Wide
Bank of America Financial services Bank of America hopes to use
blockchain for more
transparent financial services
for both consumers and
business. Recently they have
patented 9 more blockchain
related technologies
World Wide
EY (Ernst and Young) Professional services In April 2018 one of the big
four audit firms, EY,
announced a pilot test for
their blockchain technology
which will analyze
cryptocurrency transactions
World Wide
Ubiquity Legal services The complication in the legal
process of transferring real
estate has been simplified
using blockchain
USA
Transactivgrid Energy distribution Reducing the costs of energy
distribution by allowing
members to locally produce
and sell energy
USA
Essential Travel This firm is developing a new
system for the Dutch
government to securely store
passenger data
Netherlands
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Fig. 4 Blockchain operation between two banks
3.3 Cloud computing as a driver of digital business transformation
Microsoft has reported a 36% growth in their net income in the last quarter of 2019 to
$11.6 billion through the growth of its cloud business model (i.e., Azure) competing against
Amazon, Salesforce and Oracle. Benlianet al. (2018) state that cloud computing is an evolved
computing system and business model for providing information technology, infrastructure
components and applications. According to Bhushan and Gupta (2018), cloud computing is
a computational model that has the ability to process on-demand access to networks with a
shared pool of resources (hardware/software), which are customizable and where a minimum
amount of intervention is required from the service provider. Avram (2014) identifies some
commonalities in defining cloud computing, such as the business model is based on pay per
use, the space in the cloud is elastic and can have the illusion of being an infinite resource,
and finally, it is a self-service interface where resources are virtualized.
Cloud-based computing has emerged as a mixture of three major trends of a computing
system through the Internet: service orientation, virtualization and standardization (Sharma
et al. 2015). It has the power to provide highly scalable distributed computing systems (Wang
et al. 2020; Xia et al. 2020). It can be described as a platform that enables on-demand network
access from a shared pool of computing resources which are configurable and requires minimum management effort (Almorsy et al. 2016). Undoubtedly, it is a disruptive technology
which has transformed the IT sector and Internet services (Botta et al. 2016). The ultimate
benefit of using cloud computing is that the user can scale up or down the usage of cloud
according to their needs and are charged accordingly, minimizing the cost of doing business
globally (Sabi et al. 2016). Cloud computing offers three types of services: (1) Infrastructure
as a Service (IaaS), such as cloud-based storage services available on demand (e.g., Amazon
Elastic Computing Cloud) (2) Platform as a Service (PaaS), such as operating system supports and software development frameworks (e.g., Google AppEngine), and (3) Software as
a Service (SaaS), such as storage processing and network resources allowing consumers to
control applications (e.g., Joyent and Salesforce CRM). Along with the three service models,
cloud computing has five characteristics and four deployment models. The five characteris-
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tics are on-demand self-service, broad network access, resource pooling, rapid elasticity and
measured service (Battleson et al. 2016). The deployment models are private cloud, community cloud, public cloud and hybrid cloud. Kushida et al. (2015) depict cloud computing as a
revolutionary technology that has transformed the location of computing and how software
and tools are produced for business processes.
Businesses across the world are being transformed through cloud technology due to lower
infrastructure costs, more innovation and significant digitization (Lemley et al. 2017). Bo
(2018) states that cloud technology enables necessary business agility by increasing efficiency
in the system. Cowen et al. (2016) report that cloud technology increases return on capital
by improving operations and quality of service. Markovi´ c et al. (2014) describe how cloud
computing can transform businesses in healthcare and education. Kasemsap (2015) reports
that it is important to combine cloud computing with the supply-chain process to achieve
maximum efficiency with customers and suppliers. Cao et al. (2017) link cloud technology
with supply chain optimization by highlighting demand access, security and back up, sharing real time inventory and sales information, scalable services and payment arrangements.
Table 7 lists examples of cloud computing applications.
3.3.1 A case study on cloud computing-based digital business transformation
Figure 5 shows a cloud computing-based service as presented in the Adobe Creative Cloud
system. The illustration derived from Armbrust et al. (2010) depicts the relationship between
the cloud provider and the user. As a provider of SaaS, Adobe Creative Cloud provides
a subscription to the user to install the application system. The cloud provider and SaaS
provider could be the same entity, as in this Adobe Creative Cloud instance. The SaaS user
is the end end-user of the cloud, such as an Adobe Photoshop user. Adobe Creative Cloud
for enterprise runs on the Amazon Web Service (AWS), which makes collaboration and
operation easier and flexible for the users. The users can run Adobe Creative Cloud services
such as Adobe Photoshop and Illustration in desktop and mobile applications, where an enduser can download the app from Adobe Cloud service using a license. The cloud service
provides various features such as collaborating in the cloud service for project management,
accessing fonts and stock images. The services in the cloud can be accessed through users’
unique identification, hence only the users entitled to the service has the power to access it
and share content with the chosen audience.
3.4 Data analytics as a driver of digital business transformation
Data analytics has gained momentum in recent years due to the emergence of big data.
According to Akter and Wamba (2016, p. 178), it is a “holistic process that involves the
collection, analysis, use, and interpretation of data for various functional divisions with a
view to gaining actionable insights, creating business value, and establishing competitive
advantage”. Big data is beyond the capacity of conventional database systems (Dumbill
2013) as the data do not fit the structure of databases’ architecture, hence an alternate way
is required to process and gain value from the data. Due to the size and incompatibility of
processing big data using existing information systems, advanced information technologies
are required to extract maximum value from the data.
With the constant progression and evolution of data and computing power, big data has
been effectively used for business or data analytics (Wamba et al. 2015). Both big data and
traditional analytics explore ways to extract value-added information from different data
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Table 7 Digital transformation cases using cloud computing
Firms Uses of cloud computing References
Microsoft Microsoft has successfully leveraged
cloud-based innovations to host machine
learning and artificial intelligence to give an
edge to its business customers through
customer relationship management and
supply chain management software
Duff (2020)
Adobe system Offering cloud-based subscription called
creative clouds, Adobe has experienced
customer growth and stronger customer
relationships. Adobe studied years of
industry trends before moving towards
cloud-based product and service offerings
Cohen (2017)
Goldman & Sachs Adopting a private cloud infrastructure has
led Goldman & Sachs to improve risk
management for their derivative products
and business
Seth and Kaplan (2016)
The Hartford Using a private cloud to reduce costs, The
Hartford was able to bring products and
services to the market faster and meet the
needs of customers and agents
Guido (2014)
Delhaize America The company is using big data analytics on
cloud computing to learn the impact of local
weather on category sales
Guido (2014)
Pearson The education group is using an enterprise
wide cloud strategy to provide web-oriented
educational products to South Africa and
China
Guido (2014)
Intercontinental Hotels The Intercontinental Hotel Group is using
cloud computing to boost its customer
service and email marketing activities to a
greater extent
McDonald (2016)
Commonwealth Bank Australia The Australian banking giant is planning to
move 9000 virtual machines to the private
cloud for better operational activity and for
better delivery of services
Sharwood (2018)
DHL The supply chain giant uses SAAS to upload
data which can provide real-time insight to
risk management
(Murphy (2015)
Capital One Using AWS has helped Capital One to
support faster innovation and enhance the
customer experience. The company made
savings through shifting resources, found
value in data and recovered faster from
failures. Working with AWS helped the
bank to launch products within weeks
instead of months, providing cutting edge
customer service solutions by using data to
feed machine learning analysis
(Amazon 2020)
Qantas By using Microsoft Azure, Qantas provides a
unified solution for better operations and
customer service. Azure helped generate
personalized services and connected
employees all over the world
Doniz (2018)
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Fig. 5 Cloud computing-based service by Adobe
sources in order to gain a competitive advantage (Battisti et al. 2019; Camilleri 2019; Shams
and Solima 2019). However, traditional analytics differ from big data analytics on four
dimensions: volume, variety, velocity and accessibility (Morabito 2015). Volume represents
a disproportionately large amount of data and the smaller data storage requirements of businesses. These entities need to obtain large quantities of data from ubiquitous, heterogeneous
and constantly evolving sources and devices to generate effective and meaningful information
for accurate and precise decisions (Wamba and Akter 2019). Variety refers to different types
of data collected from business entities, which could include structured, semi-structured and
unstructured data. Due to the dynamic nature of big data, velocity relates to the rate of data
generated and analyzed, and sometimes includes real-time analytics. Accessibility is defined
as the capacity to acquire data from various sources (Ohlhorst 2013; Sathi 2012). However,
many researchers tend to replace accessibility and include veracity as the fourth dimension of
big data, and describing the dimensions as the 4Vs. Veracity is related to trustworthiness and
access to a complete set of data as the uncertainty, complexity, inconsistency and anonymity
of big data could influence its reliability. In recent times, another two dimensions, value and
variability, have been proposed by other authors, characterizing big data as 6Vs (Akter et al.
2019). Variability is linked to heterogeneity of big data as they could be generated due to
differential velocity. Lastly, the economic value related to the type of data dictates the value
dimension of big data. Data in the unprocessed form is useless until it is examined using
appropriate analytics to extract meaningful information.
Consumers, automation and monetization are considered the three main drivers for big
data (Sathi 2012). In recent times, big data has experienced further growth due to the Internet
of Things (IoT), which includes machine intelligence. Due to the interconnected nature of
networked technologies and smart devices, IoT can facilitate rapid and constant exchange of
realtime data, with the potential for improving functionality and upscaling processes, leading to the generation of new and better products and services (Xia et al. 2012; Kopetz 2011;
Gubbi et al. 2013). Big data opens up new opportunities and generates added operational and
financial value (Ohlhorst 2013; Morabito 2015; Sathi 2012). As a result, companies can use
their resources to achieve better outcomes utilizing the potential of big data. Cost-efficiency
and effectiveness, improved decision making and exploring new opportunities are considered the three main benefits of big data analytics (Davenport 2014). Technologies related
to big data can be adopted in large companies to strengthen traditional technologies. It can
significantly improve efficiency by increasing productivity and product quality by improving
values (Manyika et al. 2017). Production data can be analyzed to map the optimum use of
resources, i.e., time, human workforce and raw materials. Big data can improve pre/postproduction stages of the supply chain and combine production data with other functions,
thereby improving overall efficiency and effectiveness (Feinleib 2014; Ohlhorst 2013). Big
data analytics can be utilized in more effective and faster decision-making and provide opportunities to reach evidence-based decisions. Data-intensive companies such as Google, eBay,
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Amazon and Facebook generate additional revenue and adopt new value-streams through big
data analytics. Information from large data sets can transform business models, boost innovation capabilities and productivity, and open up new markets using data-driven approaches
(Gobble 2013; Davenport 2014; Chen et al. 2012). Table 8 shows use cases of big data
analytics.
4 Discussion and conclusions
While this study provides some primary understanding of ABCD technologies drawing on
literature and case studies, the applications of these technologies demonstrate that more
research is required to understand their dynamic nature. The findings show that although
the four technologies have individualized benefits, more business value could be derived
from harnessing their interconnectivity to accelerate business growth and productivity. Also,
ABCD technologies are driving the development of transformative business models with new
platforms that automate processes, match demand and supply, dynamically price and make
real-time decisions. This section discusses some of the challenges and limitations of these
technologies from various stakeholders’ point of view.
With regard to AI, this paper highlights positive business-oriented use cases and applications. However, there are widespread concerns, such as ethics, privacy and algorithmic bias
(Larson 2019). The danger of AI has already been highlighted by Elon Musk in the context
of regulatory oversight and its safe use (Metz 2018). Due to these issues, customers tend
to have less trust in AI because they think AI cannot “feel” (Gray 2017). Research shows
that people do not trust AI-based decisions or answers such as medical diagnosis, financial
planning or hiring (Davenport 2018). Although AI, ML and DL are at the peak of their hype
cycle, it is argued autonomous systems should not replace humans. In a similar spirit, the
CEO of IBM proposes AI equates to man “plus” machines instead of man “versus” machines
(Carpenter 2015), in what could be described as human–robot teaming and collaboration.
According to Davenport (2018), full disclosure and transparency about the intelligent agent
or hybrid systems (both human and automated device) should clarify the roles of human and
machine, because the majority of customers have a negative perception towards bots and
virtual assistants, though studies have shown that certain demographics would much rather
chat in an online window with both than actually speak to a call center representative and
be on long hold waiting times. Also, ensuring accuracy, replicability and reliability in AI
algorithms is critically important whether in self driving cars or diagnosing patients with
AI. Yet some of the grandest challenges will have to do with AI-based business productivity
tools that listen to every conversation and translate speech to text, see every movement and
process that movement against a set of known behaviours, and identify individuals without
their consent (even if they are a single suspect amongst many). While AI has a high degree of
accuracy based on a quality and diverse data set, it can also make mistakes which are generally known as false negatives and false positives in the literature. Individuals who are subject
to errors through ill-defined algorithms can experience asymmetric effects. Through longer
term training of diverse data sets, it is said that errors will decrease in size and frequency,
making systems more reliable. Controversy has struck AI systems presently being used in
law enforcement, and courts of law, where AI is used to determine both eligibility for arrest
or even sentencing time periods (Re and Solow-Niederman 2019). As unstructured data in
the form of visual analytics becomes analyzable through sophisticated AI, the algorithms
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Table 8 Digital transformation cases using data analytics
Firms Cases on analytics applications References
McDonald’s Corporation Sales data is utilized to optimize the
drive-thru experience, trickle down to
kitchen operations, the supply chain, menu
suggestions, personalized menus and deals
based on customer purchase history
Elmes (2019)
JP Morgan Chase & Co Several artificial intelligence and machine
learning programs optimize some of JP
Morgan Chase’s processes, including
algorithmic trading and commercial-loan
agreements interpretation. There is a
reduction of the time needed to review
documents: tasks which previously
required about 360,000 h of work, now take
just a few seconds to complete
Aleksandrova (2019)
CitiBank Use of real-time machine learning and
predictive modeling to analyze big data to
pinpoint fraudulent behavior and minimize
financial risk for online banking providers.
CitiBank can spot suspicious transactions,
e.g., incorrect or unusual charges, and
promptly notify users about them. Apart
from being useful for consumers, the
service also helps payment providers and
retailers monitor all financial activity and
identify threats related to their business
Aleksandrova (2019)
Microgaming Accurately determines the odds and
personalizes games for different types of
players, tracking player statistics and
incorporating these into the personal
gaming experience
Vickery (2016)
Netflix Netflix collects data from its 151 million
subscribers, and implements data analytics
models to discover customer behavior and
buying patterns. Then, they use that
information to recommend movies and TV
shows based on subscriber preferences
Dixon (2019)
Booking.com Data contained in the “Booking.com
Analytics” is harnessed by a proprietary
logic that converts it into a prioritized list
of actionable business advice. Also,
Booking.com thinks that partner hotels can
quickly peruse the opportunities, select the
most relevant options for their property,
and instantly implement them to enhance
their listing and grow their business
through their Booking.com portal
Sklyar and Kharchenko (2019)
Dignity Health Uses a big data and advanced analytics
platform to predict potential sepsis cases at
the earliest stages, when intervention is
most helpful
Beall (2020)
Express scripts Analyze individual patient data and alert
health care workers to serious side effects
before a medication is prescribed
Beall (2020)
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for example, conducting people searches may well be governed at a state/provincial level
supported by legislation.
The implications of blockchain technology are fascinating due to the decentralization of
user data and achieving consensus through a public network of participants to ensure the accuracy of information (Kumar et al. 2020). However, it is critical to evaluate its core promises,
such as transparency, security, decentralization and immutability in transactions. While firms
across the world are experimenting with meaningful scalability of this technology, further
work needs to be completed, such as establishing a common standard, technical capability,
and digitization of assets (Carson et al. 2018). Kumar et al. (2020) suggests that blockchain
technology needs to pass three tests, viz. the decentralization test (i.e., political, architectural,
commercial and contractual), the crypto asset test and finally, the business model test. Since
assets, trust, ownership, money, identity and contracts (ATOMIC) are all programmable in
the blockchain domain, it is important to manage how to create and capture value from
each of these components. According to Wamba and Queiroz (2020), “[d]espite the numerous potential benefits of blockchain, blockchain related concepts (e.g., enablers, adoption,
implementation, etc.) are still to be well mastered by a good number of managers. The challenges about how they can ensure that blockchain adds value to their organizations and the
supply chain management [SCM], remain unanswered”. While many tout the auditability of
the blockchain to be one of its greatest strengths, re: transparency, there are inevitably cybersecurity matters to address. The main challenge for the blockchain arguably is ensuring that
a trusted system is not riddled with counterfeit blocks or counterfeit data permeating from
fake and illegitimate transactions or sources of transactions that is not only unchangeable
on the public ledger, but is used to drive vital decisions that then further corrupt the digital
ecosystem. Auditability becomes near impossible in an environment that bases everything
on the digital with no way to recalibrate what is fact and not fiction.
Although cloud computing has evolved dramatically, challenges revolve around standards
and interoperability of this platform. According to Kathuria et al. (2018), cloud computing can be based on technology capability, cloud service portfolio capability (cloud service
offerings, market offerings), or cloud integration capability (legacy synchronization, legacy
consistency) to influence business value and firm performance. Since cloud computing is the
backbone of digital transformation, it is critical to research the interconnectivity between
cloud computing and the Internet of Things, AI, blockchain, data analytics, and crowdsourcing to develop an innovative business model. Recent failures in cloud implementation have
been caused by poor integration and lack of business value. Cloud computing hacks have
also been behind some of the biggest retail data breaches in online customer history, rendering service level agreements (SLAs) between the business and cloud computing providers
as void. When data of hundreds of millions of customers is compromised, it is a serious
issue to be dealt with through the courts, although end-users are left scrambling when their
identity credentials are stolen, and class action law suits take 5 or more years to determine.
Mandataory data breach notification (MDBN) principles and regulations seek to empower
Privacy and Data Commissioners around the world to enforce disclosure of data breaches to
consumers who have been compromised in deep hacks where personal identifying information (PII) has been stolen (e.g., credit card numbers, name, date of birth, login and password
details), but critics of MDBN note that after a commensurately small penalty to ecommerce
service providers, it is back to business as usual. The same can be said for IOT devices that
are placed in key public locations within business and customer sites to drive innovation,
producing speech or video analytics of sentiment among employees, visitors and customers
(e.g., museums). What are the means by which business can convey to citizenry that their
tools are conducting real-time data collection and analysis? What are the legal and ethi-
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cal considerations and how can businesses keep pace with evolving societal expectations?
Thus, future research should investigate these factors and their implications for the strategic
business value of digital transformation and clear processes towards consent.
Although data analytics are transforming business operations, firms need to address challenges in both managerial and technological contexts to extract value from large data sets
(Michael and Miller 2013). With regard to technology, incompatible IT infrastructure and
data architecture can impede the ability to store, analyze and derive effective information
from data sets, which comprise structured, semi-structured and unstructured data. In addition,
there are serious challenges arising from incompatible technologies related to enterprise-wide
platforms for sharing big data and its analytics with a given organization and its sectoral system as well as the inconsistency of internal and external databases. Acquisition of data from
third parties can also pose the risk of data being outdated and of diminished value. Missing,
incomplete or inaccurate data, often known as “dirty data” can also act to corrupt models and algorithms, simply by skewing results. It is important that acquired data meet two
important criteria: understanding and quality. To extract insights from collected data, it is
essential that analytics possess the ability to comprehend and to differentiate relevant data
from unconnected and misleading data so that appropriate decision-making processes can
happen.
Overall, DBT implementation needs to focus on how to integrate ABCD and other emerging technologies (e.g., Internet of Things) for various business functions in hybrid modes,
integration, recombination and in convergence. For example, cloud based accounting gains
momentum if it is fuelled by AI, big data and blockchain based financial reporting (Ionescu
2019). In order to develop a holistic platform using innovative technologies, Gill et al. (2019)
propose a framework showing how to integrate AI, IoT and blockchain for next-generation
cloud computing environment. Similarly, recent studies highlight the connection between
AI, deep learning, and blockchain as complementary technologies for digital transformation
(Arora et al. 2020; Ekramifard et al. 2020). This integration can help firms develop customer
relationship management, supplier relationship management and innovative business models. For example, cutting edge, cloud-hosted AI platforms like Microsoft’s Genee, Oracle’s
Crosswise or Salesforce’s Einstein aim to achieve competitive advantage in their respective
marketplaces through predictive and prescriptive analytics (Kumar et al. 2020). The fundamental applications of emerging technologies (e.g., AI, augmented technology, sensors,
IOT and robotics) and insights into how to integrate these processes can better explain the
behavioral consequences for customers and employees (Davenport and Spanyi 2019; Grewal
et al. 2020; Verhoef et al. 2019). Table 9 lists some of the research questions that are relevant
to the development and deployment of ABCD technologies and their interconnectivity for
business transformation and operational excellence.
5 Conclusion
Using a multidisciplinary perspective, this study puts forward ABCD technologies as the fundamental building block for the future of digital business transformation (DBT). To answer
the research questions on DBT using ABCD technologies, we started with a discussion clarifying the DBT concept and its implications for various industries. Next, we introduced AI,
blockchain, cloud and data analytics with operational use cases and applications. Since their
operational effectiveness will determine the future of DBT, the findings shed light on various
challenges and opportunities. A critical question for firms is to establish interconnectivity
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Table 9 Future research questions for digital business transformation using ABCD technologies
Digital transformation research
streams
Relevant theories Future research questions
Digital transformation strategy,
culture, leadership, and
organization
Resource-based theory (Barney
1991)
How to ensure fairness and
ethics in AI, trust in the
blockchain, cloud security and
privacy of analytics?
Dynamic capability theory
(Teece et al. 1997)
How can organizations ensure
digital transformation and
strategic business alignment
between ABCD technologies?
Competitive strategy (Porter and
Millar 1985)
How can organizations better
incorporate functional
differences into their digital
transformation culture?
Operations management of
ABCD technologies
Transaction cost theory
(Williamson 1979, 1981)
How can organizations better
use ABCD technologies to
achieve operational excellence
and sustainable growth?
How to develop and deploy AI
systems that prevent and detect
an algorithmic bias?
ABCD technology
infrastructure, privacy and
security of digital
transformation
IS success theory (Delone 2003;
DeLone and McLean, 1992)
What are the capabilities of data
governance, security and
privacy for digital
transformation using ABCD
technologies?
Sociomateriality of IT
(Orlikowski 2007)
How can a firm leverage ABCD
technologies to enhance firm
performance?
What should be the drivers of
integration, hybridization,
recombination and
convergence of/between
ABCD technologies?
Business value IT business value (Melville et al.
2004), the business value of
analytics (Wixom et al. 2013)
How does ABCD
adoption/continuance vary
across firms/industries?
How do ABCD and
organizational
decision-making process
jointly influence business
value?
How can firms leverage ABCD
technologies to adapt to
business models?
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among these technologies to reap the ultimate benefits. In essence, these processes of innovation include: hybridization, integration, recombination and convergence. Due to the nascent
stage, this study summarized the initial emergence of ABCD technologies and their impact
on digital transformation through business use cases. We hope researchers will explore these
cases in greater depth in addressing the research questions posed in Table 9.
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