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Journal of Open Innovation:
Technology, Market, and Complexity
Article
Emerging Technology and Business Model
Innovation: The Case of Artificial Intelligence
Jaehun Lee 1, *, Taewon Suh 2, Daniel Roy 2and Melissa Baucus 2
1AI Center, Samsung Research, Seoul 05510, Korea
2McCoy College of Business, Texas State University, San Marcos, TX 78666, USA
*Correspondence: i.am.jaehun@gmail.com; Tel.: +82-10-5155-3425
Received: 6 June 2019; Accepted: 16 July 2019; Published: 22 July 2019
Abstract:
Artificial intelligence (AI) has been altering industries as evidenced by Airbnb, Uber and
other companies that have embraced its use to implement innovative new business models. Yet we
may not fully understand how this emerging and rapidly advancing technology influences business
model innovation. While many companies are being made vulnerable to new competitors equipped
with AI technology, this study attempts to focus on the proactive side of the use of AI technology
to drive business model innovation. Describing AI technology as the catalyst of business model
innovation, this study sheds light on contingent factors shaping business model innovation initiated
by the emerging technology. This study first provides a brief overview of AI, current issues being
tackled in developing AI and explains how it transforms business models. Our case study of two
companies that innovated their business models using AI shows its potential impact. We also discuss
how executives can create an innovative AI-based culture, which rephrases the process of AI-based
business model innovation. Companies that successfully capitalize on AI can create disruptive
innovation through their new business models and processes, enabling them to potentially transform
the global competitive landscape.
Keywords: business model innovation; artificial intelligence; case study; emerging technology
1. Introduction
Companies around the globe are seeing their industries disrupted by new technologies that
result in business model innovation [
1
]. Artificial intelligence (AI)—“Intelligent systems created to
use data, analysis and observations to perform certain tasks without needing to be programmed to
do so” [
2
]—represents the most important technological development. AI disrupts industries and
companies when companies use it to create innovative new business models [
3
]. Companies such as
Amazon, Uber, Tesla, Google, Alibaba and UPS, along with many other companies have innovated
their business models and enhanced their competitive advantages using AI. Top executives need to
embrace an entrepreneurial and innovative mindset and instill this mindset using AI throughout their
organizations to remain competitive and viable.
The concept of business model innovation has been put to the forefront of the debate of how
companies may preserve their market position [
4
,
5
]. The present literature of business model innovation
mainly focuses on external antecedents, which may pressure companies to engage in business model
innovation [
6
]. This pressure may also arise through technological disruptions. Researchers argue that
the process of business model innovation is prone to being affected by their environment [
7
]. That
is, while the literature mainly focuses on external factors that may pressure companies to engage in
business model innovation, one recently blossoming research stream examines how the introduction
of new technology engages companies to innovate their business model [
8
,
9
]. Regretfully, however,
studies investigating the direct impact of emerging technologies on business model innovation are
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J. Open Innov. Technol. Mark. Complex. 2019,5, 44 2 of 13
few and far between. A few studies have only recently explored AI technology meticulously in
consideration of business model innovation (e.g., [
10
,
11
]). For instance, Garbuio and Lin [
10
] identified
seven emerging business model archetypes in their timely and critical analysis of AI-driven health
care startups.
However, more studies need to be added to this particular topic area. The expected consequences
of AI technology to business model innovation would be much more intense in the multifaceted
elements of business model innovation. Therefore, endeavoring to generate a holistic view on how
companies, particularly using AI technology, create value and how they perform the activities needed
to do so, we attempt to discuss the plausible consequences of AI technology in business model
innovation. It is noticeable that this study considers a business model to be an activity system or a
set of interdependent activities spanning firm boundaries [
12
]. Thus, our case study on the human
resource space may be able to shed light on further understanding the business model innovation
initiated by AI technology.
The following section explains the types of AI, which are symbolic and neural AI. It also addresses
two of the most important issues currently affecting the implementation of AI technology, namely,
combining symbolic and neural AI and dealing with the quality and quantity of data in AI systems. Our
case study in Section 3exemplifies the use of AI technology for business model innovation, showing
ways that AI can help managers tackle and rethink key organizational problems, altering their business
model in the process. The brief overview of AI and the following case study provide the basis for our
discussion in Section 4in regard to how executives can create an innovative AI-based business model.
We conclude our discussion in Section 5with advice on how to manage the large-scale organizational
change required to embrace business model innovation associated with the use of AI systems.
2. AI Technology: Definitions and Trends
The inspiration of artificial intelligence (AI) was to create an autonomous machine capable of
human-like thinking [
13
]. In 1956, John McCarthy organized a research group and coined the concept
of AI. The group assumed every aspect of learning or any other feature of intelligence can be so
precisely described that it could be simulated by a machine. Applications include determining how to
make machines use language, form abstractions and concepts, and solve complicated problems [
14
].
The massive and growing data available today and the steady improvements of computational power
and algorithms have generated numerous applications of AI across many diverse industries. Although
definitions and concepts of AI vary with the goals and domain, the main characteristic of AI is
mimicking human cognitive function, particularly learning and problem solving. It is notable that the
concept proposed in 1956 is considered still relevant to current AI research.
2.1. Types of AI Technology Development Approaches: Symbolic vs. Neural
Recently, the deep neural network known as deep learning has emerged as a dominant force among
AI development approaches with outstanding performance in the fields of image/voice recognition,
natural language processing and predictive techniques. However, deep neural networks are not
suitable for solving all AI-related problems.
2.1.1. Symbolic AI
Before the era of deep learning from AlphaGo [
15
], symbolic AI had been the predominant
paradigm among AI development approaches. It assumes that high-level representation of knowledge
(symbol) and combinations of symbols can achieve human-like AI by performing reasoning in a manner
similar to how humans express their thoughts and draw conclusions from deductive reasoning [
16
].
The example of deductive reasoning from symbolic AI is like the old Roman saying:
All men are mortal. Caius is a man. Therefore, Caius is mortal.
Symbolic AI includes any programming methods and systems that use symbols such as letters and
numbers to encode a human’s knowledge, rule-based operations, and determined policy. Symbolic AI
J. Open Innov. Technol. Mark. Complex. 2019,5, 44 3 of 13
can provide companies with competitive advantages by producing results that humans can interpret,
predict and use quickly. Expert systems, although the degree of success they achieve as AI is
controversial, represent the most well-known and widespread AI systems. Specifically, in the fields of
manufacturing and production, many expert systems enable companies to automate time-consuming
and knowledge-intensive tasks in various operations such as design, process planning, production
control and diagnosis [17].
IBM’s Deep Blue chess-playing computer that defeated the world champion in 1996 is one of the
outstanding symbolic AI systems that exploits symbolic, rule-based knowledge [
18
]. More recently,
Google has capitalized on symbolic AI systems to provide the most relevant and important information
in the top box under the queries delivered via the Google search engine. Figure 1shows the search
result from the term, ‘Seoul’ by Google’s Knowledge Graph. Google’s Knowledge Graph illustrates an
outstanding symbolic AI system, incorporating knowledge and reasoning methods to enhance search
results. Note that the current knowledge graph contains more than 18 billion facts and 570 million
entities [
19
]. Information from Knowledge Graph and reasoning technologies are used to produce
answers in Google Search Engine, Google Assistant and Google Home voice queries [20].
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Symbolic AI includes any programming methods and systems that use symbols such as letters
and numbers to encode a human’s knowledge, rule-based operations, and determined policy.
Symbolic AI can provide companies with competitive advantages by producing results that humans
can interpret, predict and use quickly. Expert systems, although the degree of success they achieve
as AI is controversial, represent the most well-known and widespread AI systems. Specifically, in the
fields of manufacturing and production, many expert systems enable companies to automate time-
consuming and knowledge-intensive tasks in various operations such as design, process planning,
production control and diagnosis [17].
IBM’s Deep Blue chess-playing computer that defeated the world champion in 1996 is one of the
outstanding symbolic AI systems that exploits symbolic, rule-based knowledge [18]. More recently,
Google has capitalized on symbolic AI systems to provide the most relevant and important
information in the top box under the queries delivered via the Google search engine. Figure 1 shows
the search result from the term, ‘Seoul’ by Google’s Knowledge Graph. Google’s Knowledge Graph
illustrates an outstanding symbolic AI system, incorporating knowledge and reasoning methods to
enhance search results. Note that the current knowledge graph contains more than 18 billion facts
and 570 million entities [19]. Information from Knowledge Graph and reasoning technologies are
used to produce answers in Google Search Engine, Google Assistant and Google Home voice queries
[20].
Figure 1. Search result by Google’s Knowledge Graph.
Symbolic AI works best with static problems, but it has some stumbling blocks. It is labor
intensive, challenging and expensive for developers to create symbolic AI systems as they depend on
gathering and understanding complicated and implicit expertise and knowledge. Additionally,
symbolic AI is not suitable in situations requiring recognition, since it is difficult to represent
recognition knowledge efficiently.
Figure 1. Search result by Google’s Knowledge Graph.
Symbolic AI works best with static problems, but it has some stumbling blocks. It is labor
intensive, challenging and expensive for developers to create symbolic AI systems as they depend
on gathering and understanding complicated and implicit expertise and knowledge. Additionally,
symbolic AI is not suitable in situations requiring recognition, since it is difficult to represent recognition
knowledge efficiently.
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2.1.2. Neural AI
A huge part of AI’s explosive growth has been made possible with the contribution of machine
learning. Technically, machine learning approaches involve using algorithms to improve learning
performance on a specific task by relying on patterns generated from practice or sample data. Neural
AI relies on an artificial neural network (ANN) or an aggregate of machine learning algorithms that
work in sync to mimic the human brain to solve more complex problems and learn in a manner
similar to neurons in a human brain. It enables computers to learn from data without being given
explicit knowledge. Technically, machine learning approaches can be classified into three areas: (1)
supervised learning, that involves learning from correct answers (labeled data); (2) unsupervised
learning, defined as finding knowledge or information when given some raw data (unlabeled data);
and (3) reinforcement learning that entails how agents in an environment take action to maximize
their rewards.
The details on artificial neural networks can be found in [
21
]. Recently, deep neural networks
have been receiving a lot of attention and application in industry, as well as in academia. As a
part of machine learning, it is based on deeper layers of the ANN that allows it to solve more
complicated problems and to learn from raw data without much hand-based preprocessing of data.
It has shown outstanding performance on speech/image recognition, fraud detection, providing
recommendations, and natural language processing. Thus, leading technology companies such
as Google and Facebook have announced innovative developments based on deep learning-based
speech/image recognition [
22
,
23
]. Netflix, Spotify and Amazon use machine learning algorithms
to generate personalized recommendations [
24
,
25
]. Neural AI has been successful in dealing with
well-defined problems such as recognition and perception with a lot of labeled data. At the current
stage of development, neural AI could be criticized since it is far from human-like thinking or reasoning
and it needs a lot of labeled data [26,27].
2.2. The Current Trends in AI Technology Development
Experts agree that AI is still in the early days. Much work remains to reach a level of efficiency
that allows for scaling learning and reasoning capabilities across broader applications. In this regard,
Yoshua Bengio claimed that AI research needs to extend the capability for reasoning, learning causality,
and exploring the world in order to learn and acquire information [
28
]. Approaches that combine
symbolic AI with neural AI appear promising for addressing these issues.
2.2.1. Marrying Symbolic AI and Neural AI
Symbolic AI joined with neural AI would help address the fundamental challenges of human-like
reasoning, knowledge representation, learning with lack of data or transfer learning, and interpretability
to resolve the black-box issue [
29
], which is associated with unknown internal structure of AI to lower
the degree of the AI’s transparency as well as the extent to which the AI is supervised by humans [
29
].
Studies related to this are not limited to the area of computer science, but extend to cognitive science,
neural science, philosophy, psychology, and several other areas. A few notable studies could be found
in [
30
–
33
]. Specifically, the MIT-IBM Watson AI Lab launched a project titled, ‘Empowering AI with
symbolic reasoning’, to perform more complicated tasks [30]. Garcez et al. proposed a novel method
for neural symbolic integration [
31
], and they verified the feasibility of their proposed method by
implementing logic tensor networks [
32
]. Pedro Domingos is leveraging a combination of symbolic AI
and neural AI in the machine reading area [
33
]. Marta Garnelo developed an end-to-end reinforcement
learning architecture comprising a neural back-end and a symbolic frond-end with the potential to
address the challenges [
34
]. These examples illustrate various ways that researchers are progressing in
developing solutions that marry symbolic and neural AI.
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2.2.2. Addressing Data Quality and Quantity Issues
‘Garbage-in, garbage-out’ is a basic principle for AI. To learn or reason properly, historical data
should meet the standards of high-quality and sufficient quantity. Unfortunately, most data do not
meet these standards; thus, AI researchers and data scientists have no choice but to use 80 percent
of their time on data preprocessing to ensure high quality. In order to address the data quality issue,
leading AI startups have been developing tools to automate data preprocessing. Paxata and Trifacta
have developed a cloud-based solution for ensuring data quality and enrichment [
35
,
36
], whereas,
Tamr and Alation created a method and solution for addressing data silo issues [37,38].
Data quantity issues are also being solved by combining symbolic AI and neural AI. This can
ensure performance without the volume of data since symbolic AI does not require as much data as
neural AI. Additionally, Zoubin Ghahramani proposed a probabilistic machine learning method to
resolve data quantity issue [
39
]; this method addresses more issues than data quantity, because it uses
a new machine learning paradigm to resolve data uncertainty.
3. Case Study: Need for AI to Innovate Business Model
3.1. Case #1: A Bigger Manufacturing Company
We use an exploratory case study with the aim to understand how the phenomenon of AI-based
business model innovation takes place. A case study is a qualitative research method that allows
an in-depth investigation of a contemporary phenomenon within its real-life context. Case studies
attempt to understand the dynamics present with single settings by using a variety of lenses to reveal
multiple facets of the phenomenon [40,41].
This section presents an example case in a large manufacturing company (Company A, hereafter)
where the introduction and use of AI and analytics led to business model innovation, specifically
in the area of human resources or talent management. Note that this case is illustrating a machine
learning approach involving using algorithms to improve learning performance on a specific task. In
other words, this example case is associated with the application of a type of neural AI, deep neural
network, the type of AI in which we learn from data to solve more complicated problems without
much hand-based preprocessing of the data.
Company A was struggling to add over 600 production employees. The labor market was
exceptionally tight, and efforts to attract and hire talent, which had not changed in many years, were
proving ineffective. Market conditions, tools and resources relating to attracting and retaining talent
had changed dramatically, but Company A’s approach had not; placing it at a significant competitive
disadvantage. Company executives were unaware of the dramatic changes that had taken place in the
talent management space but did recognize they lacked the internal expertise to innovate to solve the
people related challenges. Company A’s executive leadership reached out to an external company
with expertise in the talent management space. The external company was known for its innovative
approaches including the use of AI to improve Human Resource processes and performance.
Historically, the company’s efforts to attract and hire talent focuses on finding candidates with
three to five years of prior experience in the manufacturing area. When proprietary technology (AI
and algorithms) was used to analyze years of employee related data, which included prior work
experience, educational attainment, job-related performance evaluations, and turnover—it produced
surprising results suggesting that many of the company’s best performing production employees had
little to no prior manufacturing experience. The vast majority, however, had at least three consecutive
(uninterrupted) years of experience working in fast food.
Based upon the results, the company altered its recruiting strategies focusing instead on identifying
and reaching high performing employees working in the fast food industry. It is considerably easier to
find fast food workers than it is to find people with manufacturing experience. The company was
able to achieve its hiring objectives, reduce turnover and lower its cost of employee acquisition. The
company now uses a fully integrated approach to managing the employee lifecycle using advanced
J. Open Innov. Technol. Mark. Complex. 2019,5, 44 6 of 13
software with AI to strengthen organizational alignment ensuring the people strategies are effective
and fully aligned with the organizational objectives.
The use of AI, in this case a simple algorithm, enabled the company to quickly hone in on the
human resources necessary for growing the company and improving productivity. This case illustrates
that deep learning from data can produce innovative solutions that create value.
3.2. Case #2: A Ready-Mix Concrete Company
We present an additional case of a company to cross-sectionally understand how business
models can be innovated through AI technology. This case involves a sizeable family-owned company
(Company B, hereafter) that produces ready-mix concrete, with over 30 locations across Texas. Company
B hires people with a commercial driver license (CDL).
In this case, the business owner had trucks sitting idle and idle trucks do not generate revenue. A
key operational metric at Company B involves making sure every truck is being utilized daily; this
metric revealed a people problem. The shortage of commercial drivers represents a market condition
resulting from structural and non-structural factors [
42
], some of which are external to Company B but
the company can influence internal factors such as recruiting strategies, pay, signing bonuses, work
environment and so on.
New technologies have compressed the hiring cycle. In an exceptionally tight labor market, for
example, the speed with which a company can move to attract and hire talent becomes increasingly
important. Company B was responding too slowly, relying on strategies, systems and processes that in
many cases had remained unchanged for decades. Paper applications simply cannot compete with the
ability to apply almost instantly using your phone. These days qualified talent is snapped up with
incredible speed. A reliance on traditional systems placed Company B at a tremendous disadvantage
in the labor market, challenging their ability to find new drivers quickly. Management realized that
their company’s current systems were not enough, and most importantly, could not scale or deliver the
speed required.
The same external company that worked for Company A with expertise in the talent management
space was also invited to solve the problem. The external company examined Company B’s current
perceptions and processes with an emphasis on how technological advances and new business models
(innovations) might be applied. Senior management was aware of the importance of the issue but not
aware of the modern tools available to address it. After receiving inputs from outside expertise, they
began to perceive the opportunities and importance of business model innovation using AI technology.
However, they also agreed that the implementation and exploitation of new business model based on
a new technology remain challenging given the Company’s deliberately conservative culture which
was reluctant to change.
As in many industries, the talent management space has evolved dramatically and rapidly. In the
past, most of recruiting spending went towards classified ads until pioneering companies like Monster
and CareerBuilder used technology to innovate their business models in a way that all but wiped out
the use of classified ads as a recruiting tool. Indeed and other companies then began aggregating job
postings, crawling the internet to find postings from multiple job boards and displaying them on a
single site, a simple but powerful new approach. Companies like Ziprecruiter blended job postings
with a more robust applicant tracking system, thus adding powerful analytics and machine learning
to facilitate the hiring process. Advanced recruiting systems now tie directly into comprehensive
information management systems, allowing companies to capture data throughout the employee life
cycle and relate it directly to financial performance.
These new approaches open new possibilities for Company B, driving not only efficiency, but
providing data that can connect a company’s human resource strategies with its profitability strategies.
The employee life cycle took on new meaning when systems captured data and the data were used
strategically to influence outcomes in a positive way. When companies are laser-focused on their
own core business, ready-mix concrete in this case, they may have a hard time recognizing how
J. Open Innov. Technol. Mark. Complex. 2019,5, 44 7 of 13
new technologies in recruiting and hiring have changed and how they might gain advantages from
effectively adapting to these changes.
In the human resource space, the most important questions have traditionally been very difficult
to answer. These include questions such as: who are the company’s best employees; how do we
identify them; where and how did we find them; what motivates these employees; what investments
in training and development best increase their performance and value to the company; how long,
on average, do these employees remain with the company; why do they leave; and how does their
compensation relate to productivity. When Company B answered these questions utilizing solutions
based on AI technology, they could propel the innovation of their business model, which allowed them
to exploit new opportunity [
43
]. As known in the literature, business model innovation also ensures
sustainable, competitive advantages [
44
], facilitates adaptive changes in the environment [
45
], and
enables companies to stay competitive in new environmental settings [46].
4. Developing AI-Based Business Model
Business model denotes an activity system or a set of interdependent activities spanning firm
boundaries [
12
], and business model innovation is defined as a significant change in the company’s
operations and value creation, typically resulting in an improvement in firm performance [12,47]. AI
has been fostering business model innovation across industries including technology/media, consumer
products, financial services, health care, industrial, energy, public sector, and so on [
32
]. Interviews
with more than 3,000 business executives revealed that 84 percent think AI will enable their companies
to obtain or sustain a competitive advantage, and 75 percent state that AI will allow them to move into
new businesses and ventures [
48
]. In this context, what should we consider to develop or innovate our
business model with AI?
Andrew Ng released an AI playbook to transform companies with AI by drawing insights from
his experiences leading Google Brain and Baidu AI [
49
]. The five steps outlined in [
49
] are discussed
below, along with issues that we believe must be raised for each step. The deliberation in this section
further illustrates the process of business model innovation in the actual cases in the previous section.
Figure 2summarizes the discussion and issues.
J. Open Innov. Technol. Mark. Complex. 2019, 5, x FOR PEER REVIEW 7 of 13
identify them; where and how did we find them; what motivates these employees; what investments
in training and development best increase their performance and value to the company; how long,
on average, do these employees remain with the company; why do they leave; and how does their
compensation relate to productivity. When Company B answered these questions utilizing solutions
based on AI technology, they could propel the innovation of their business model, which allowed
them to exploit new opportunity [43]. As known in the literature, business model innovation also
ensures sustainable, competitive advantages [44], facilitates adaptive changes in the environment
[45], and enables companies to stay competitive in new environmental settings [46].
4. Developing AI-Based Business Model
Business model denotes an activity system or a set of interdependent activities spanning firm
boundaries [12], and business model innovation is defined as a significant change in the company’s
operations and value creation, typically resulting in an improvement in firm performance [12,47]. AI
has been fostering business model innovation across industries including technology/media,
consumer products, financial services, health care, industrial, energy, public sector, and so on [32].
Interviews with more than 3,000 business executives revealed that 84 percent think AI will enable
their companies to obtain or sustain a competitive advantage, and 75 percent state that AI will allow
them to move into new businesses and ventures [48]. In this context, what should we consider to
develop or innovate our business model with AI?
Andrew Ng released an AI playbook to transform companies with AI by drawing insights from
his experiences leading Google Brain and Baidu AI [49]. The five steps outlined in [49] are discussed
below, along with issues that we believe must be raised for each step. The deliberation in this section
further illustrates the process of business model innovation in the actual cases in the previous section.
Figure 2 summarizes the discussion and issues.
Figure 2. Developing an Artificial intelligence (AI)-Based Business Model.
4.1. Execute Pilot Projects to Gain Momentum
Early in the use of AI, a company needs to have pilot projects that succeed in order to build
momentum toward business model innovations. Successful small-scale pilot projects allow
employees to become familiar with AI technology, recognize that it does not mean the loss of their
jobs, and generate enthusiasm for the use of AI. Commonly, many companies are so soaked in the AI
syndrome that they fail to achieve their technical goals. Companies should instead focus on
technically feasible smaller projects that are readily achievable. The adoption of AI represents and
Figure 2. Developing an Artificial intelligence (AI)-Based Business Model.
J. Open Innov. Technol. Mark. Complex. 2019,5, 44 8 of 13
4.1. Execute Pilot Projects to Gain Momentum
Early in the use of AI, a company needs to have pilot projects that succeed in order to build
momentum toward business model innovations. Successful small-scale pilot projects allow employees
to become familiar with AI technology, recognize that it does not mean the loss of their jobs, and
generate enthusiasm for the use of AI. Commonly, many companies are so soaked in the AI syndrome
that they fail to achieve their technical goals. Companies should instead focus on technically feasible
smaller projects that are readily achievable. The adoption of AI represents and should be managed as a
major organizational change effort [
50
], including making information that explains the why and how
of the pilot projects available to all employees in understandable language. Furthermore, in designing
a team for the projects, AI experts should be mingled with domain-experts such as human resource
managers, marketing or social media experts, or employees well-versed in operations. It is obvious
that the knowledge from domain experts is so essential for symbolic AI and helpful for neural AI and
these experts can offer diverse perspectives on the problem being tackled.
4.2. Build an in-House AI Team
Andrew Ng recommended building an in-house AI team to execute projects efficiently [
49
]. This
is natural if companies want to build a more unique competitive advantage or have tremendous
confidential data such as a customer usage log. Small and medium sized companies or new startups
often cannot afford to hire a significant number of AI researchers and data scientists so they will need
to consider alternative strategies. They may need to out-source AI to another company or form a joint
venture with an AI company to get the necessary expertise. This reliance on “outside” experts will
have to be managed carefully so competitors do not gain access to the company’s activities.
4.3. Provide Broad AI Training
Most companies do not have enough AI researchers and experts, and companies find it difficult to
hire them due to a shortage in the AI field. Thus, Andrew Ng suggested educating employees—all
the way from training business executives down to AI researchers by utilizing digital content such
as MOOCs [
49
]. Digital content is relatively affordable and allows a more personalized experience
so it could be applied in small and medium sized companies. Companies may want to help develop
additional content for AI education since it not only solves the current issue—the insufficiency of AI
researchers—but it also fuels lasting AI business model innovation.
4.4. Develop an AI Strategy
The key to an AI strategy is to create the virtuous cycle of AI shown in Figure 3. For instance,
Google has tremendous data, so it can build an accurate search engine as a product (A). This product
enables Google to acquire more users (B), who then create more data on Google (C).
The key factor in AI is to have good quality and sufficient quantity of data. Companies too often
attempt to drive AI without appropriate data like building a palace on quicksand. Data acquisition and
data infrastructure are vital to transforming the business model. Approaches such as the lean startup
method [
51
] encourage companies to develop minimum viable products that they get customers to
use, gathering data in order to test which designs and features are most viable: this virtuous cycle is
essential for building an innovative business model and is used in a variety of contexts including new
venture startups. Companies using the virtuous cycle will recognize that building a good platform (or
new business model) becomes an open-ended challenge.
J. Open Innov. Technol. Mark. Complex. 2019,5, 44 9 of 13
J. Open Innov. Technol. Mark. Complex. 2019, 5, x FOR PEER REVIEW 8 of 13
should be managed as a major organizational change effort [50], including making information that
explains the why and how of the pilot projects available to all employees in understandable language.
Furthermore, in designing a team for the projects, AI experts should be mingled with domain-experts
such as human resource managers, marketing or social media experts, or employees well-versed in
operations. It is obvious that the knowledge from domain experts is so essential for symbolic AI and
helpful for neural AI and these experts can offer diverse perspectives on the problem being tackled.
4.2. Build an in-House AI Team
Andrew Ng recommended building an in-house AI team to execute projects efficiently [49]. This
is natural if companies want to build a more unique competitive advantage or have tremendous
confidential data such as a customer usage log. Small and medium sized companies or new startups
often cannot afford to hire a significant number of AI researchers and data scientists so they will need
to consider alternative strategies. They may need to out-source AI to another company or form a joint
venture with an AI company to get the necessary expertise. This reliance on “outside” experts will
have to be managed carefully so competitors do not gain access to the company’s activities.
4.3. Provide Broad AI Training
Most companies do not have enough AI researchers and experts, and companies find it difficult
to hire them due to a shortage in the AI field. Thus, Andrew Ng suggested educating employees—
all the way from training business executives down to AI researchers by utilizing digital content such
as MOOCs [49]. Digital content is relatively affordable and allows a more personalized experience so
it could be applied in small and medium sized companies. Companies may want to help develop
additional content for AI education since it not only solves the current issue—the insufficiency of AI
researchers—but it also fuels lasting AI business model innovation.
4.4. Develop an AI Strategy
The key to an AI strategy is to create the virtuous cycle of AI shown in Figure 3. For instance,
Google has tremendous data, so it can build an accurate search engine as a product (A). This product
enables Google to acquire more users (B), who then create more data on Google (C).
Figure 3. The virtuous cycle of AI.
The key factor in AI is to have good quality and sufficient quantity of data. Companies too often
attempt to drive AI without appropriate data like building a palace on quicksand. Data acquisition
and data infrastructure are vital to transforming the business model. Approaches such as the lean
Figure 3. The virtuous cycle of AI.
4.5. Develop Internal and External Communications
The company’s stakeholders need to be informed about how AI is transforming the business
model and the consequences this has for them. Protecting the privacy of customers’ and employees’
data has become a major issue for companies, as well as ensuring that their actions and decisions
comply with laws, regulations and ethical standards, and use of AI to process data and make decisions
will create bigger challenges in these areas. Business model innovations developed through the use of
AI will increase the company’s value to stockholders and provide opportunities to enhance value for
customers. Innovations often arise from communications with a company’s customers who suggest
ideas and highlight what they do not like about current offerings and processes [
52
] so two-way
communication may lead to greater business model innovations. Since AI is not well understood and
AI technologies are changing very rapidly, companies will need to inform and educate all of their
stakeholders as to how they use AI, the benefits of its use and potential drawbacks or limitations of AI.
5. Discussion and Conclusions
The current study contributes to academia and practice in two ways. First, this study attempted
to describe AI technology as the catalyst of business model innovation. More studies are called for
to associate emerging technologies to business model innovation. Second, this study sheds light on
contingent factors shaping business model innovation initiated by the emerging technology. Our
case study and subsequent discussion on the creation of AI-based business model innovation provide
insights on such contingent factors. However, for future study, more of those factors need to be fully
listed and quantitatively tested.
Advances in AI technology and data analytics will continue to create opportunities and challenge
delivery systems. Effective leaders need to find new and innovative ways to leverage these advances
to transform their organization and drive growth. Many leaders will find that these advances take
them in directions they had never considered. The focus on a company’s core competencies and
business strategy remains imperative but remaining open to and encouraging innovations that shift
the company’s business model represents a major challenge for leaders.
According to KPMG’s 2017 CEO Survey, nearly 60 percent of leaders indicated their organizations
lack the sensory and innovative processes to respond to rapid disruption. It follows that innovation
has become a key focus of business leaders, but companies struggle to position themselves to detect
emerging signals of disruption and to respond. This is particularly true for companies that have been
in business for a number of years and achieved remarkable success, i.e., legacy businesses. Accenture
J. Open Innov. Technol. Mark. Complex. 2019,5, 44 10 of 13
recently published an article, “Make Your Wise Pivot to the New.” In it, they discuss the fact that
“C-level executives know that clinging to their legacy business can undermine their company’s future
health.” The authors go on to suggest that three preconditions pave the way to success: transform the
core business, grow the core business, and scale new business. We believe that for many companies,
transforming and growing the core business will take priority over scaling a new business since
companies must focus most of their attention on the business generating the most revenues. If
companies start new businesses, these will likely be a byproduct of attempts to innovate around legacy
businesses or perhaps more likely, legacy systems. Companies are increasingly looking for employees
who can innovate and who have an entrepreneurial mindset because they recognize the need for
employees who see major problems, view them as opportunities and implement innovative solutions.
In our ready-mix example, Company B had some of the most advanced technology in its trucks,
allowing the drivers to deliver product more efficiently and thus more profitably; yet Company B had
some of the most antiquated systems for managing the people side of its business. Without the people,
the trucks sat idle. Only when the people side of the business began to affect the delivery side did they
seek outside expertise to drive innovation through their talent management processes. Likewise, only
when Company A needed to ramp to 800 employees, did it become hyper-focused on the people side
of its business and seek knowledge and expertise it did not have. Applying data analytics to its people
processes produced dramatic results, but it took an outside perspective and technological resources to
achieve it.
It would be natural to ask why the two companies in our cases were unable to innovate internally
to apply the latest advances in technology and update antiquated and ineffective systems. In part, the
answer is that as with many companies today, these companies do not have AI teams on hand and
have not considered how to effectively use AI according to the five recommendations we discussed
earlier. If companies foster greater learning about AI and use of AI within their operations, this will
help drive innovations across their companies.
Additionally, the culture of the organization likely plays a major role as the previous section of
developing AI-base business model connotes. The literature suggests that organizational factors play a
critical role in shaping the process of business model innovation [
6
]. The debate frequently revolves
around contextual factors such as organizational design, organizational culture, and organizational
values [6].
It seems developing a culture of innovation often runs contrary to an intentionally conservative
and constrained view such as that expressed by the founder of the ready-mix company who indicated
that he prefers to stay laser focus on core activities. Significant pivots require a certain degree of
freedom to take risks and exposure to a lens or frame that is unfamiliar. Top managers play a key
role in establishing the culture of an organization so they must model innovation and a willingness
to continually learn and innovate. They could begin by learning about AI and how it can enhance
the company’s business model and systems, as well as encouraging and rewarding employees who
acquire AI expertise and launch pilot projects in the organization.
This study has its limitations and thus calls for future study. The main limitation was the
small number of cases in our case study. Although we conducted in-depth investigation and close
observation to reveal both general and specific factors of the issues, the idiosyncratic conditions of
different industries should be considered for the generalization of our findings. Future study also
needs to exemplify different types of emerging business model archetypes. Different delivery models
such as platform, SaaS (Software as a Service), and PaaS (Platform as a Service) can be discussed [
10
,
53
]
in the context of AI-based business model innovation. Quantitative empirical studies are subsequently
recommended, taking more organizations into account and providing another dimension of insights.
Various methodological techniques can be utilized: e.g., Survey method will be able to introduce
the detailed perceptions of industry leaders regarding the role of AI technology on business model
innovation. Lastly, being absent in our study, ethical issues and challenges around a potential
J. Open Innov. Technol. Mark. Complex. 2019,5, 44 11 of 13
contradiction between rule-driven AI rationality and innovation uncertainty [
54
] should not be ignored
in future study.
Author Contributions:
Investigation, writing (original draft), J.L.; design, supervision, writing (original draft),
writing (review & editing), investigation, writing (original draft), writing (review & editing), T.S. and D.R.; writing
(review & editing), M.B.
Funding: This Research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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