applying ai in business - industrial electrical engineering and … · 2020-06-16 · industrial...
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CODEN:LUTEDX/(TEIE-5441)/1-62(2020)
Applying AI in Business -
A Framework for AI Implementation in Product Offerings
Viktor Regefalk
Division of Industrial Electrical Engineering and Automation Faculty of Engineering, Lund University
Viktor Regefalk
Supervised by:
Gunnar Lindstedt
Examined by:
Ulf Jeppsson
Course: EIEM01
Spring 2020
2020-06-09
Applying AI in Business - A Framework for AI Implementation in Product Offerings
Master Thesis, 30 ECTS
Industrial Electrical Engineering and Automation
Abstract Artificial Intelligence (AI) is on its way to enter the world of business and there are huge
potentials for companies who take advantage of the technology. However, to succeed with a
new technology like AI there is a need for guidance. This thesis examines how businesses can
implement AI in their product offering.
This thesis project investigates the implementation of AI in product offerings by first examining
academic books and research papers about implementation strategies and product innovation
methods. In the second part, empirical observations from Company X and their work with AI
are collected. A framework for AI implementation is constructed considering the findings from
the literature study and the empirical data from Company X. This framework aims to give
guidance to organizations in their work with AI and product development.
The main conclusion from the study shows that implementing AI is more of an organizational
difficulty than a technical one. Firstly, implementing AI in product offerings requires close
cooperation with the product-user during the whole process. Secondly, organizations need to
have a committed management team that dares to invest in AI resources. Lastly, having the
necessary competence within AI and data science is crucial, which is a scarcity in today’s labor
market. However, organizations should not feel obligated to develop all competencies
internally, the optimal strategy might be to collaborate with external technology partners for
accessing needed capabilities.
Keywords: Artificial Intelligence, business implementation, AI-framework, product
development
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Table of Contents
1 Introduction ................................................................................................................................................... 1 1.1 Background ............................................................................................................................................ 1 1.2 Problematization .................................................................................................................................... 2 1.3 Purpose .................................................................................................................................................. 2 1.4 Study Question ....................................................................................................................................... 2 1.5 Limitations ............................................................................................................................................. 2 1.6 Company X and their Product Offering ................................................................................................. 3
2 Method ............................................................................................................................................................ 4 2.1 Study Design .......................................................................................................................................... 4 2.2 Literature Review Stage ......................................................................................................................... 5 2.3 Interview Stage ...................................................................................................................................... 6
3 Literature Review .......................................................................................................................................... 9 3.1 Artificial Intelligence ............................................................................................................................. 9 3.2 AI in Businesses ................................................................................................................................... 11 3.3 Key Factors to Succeed with AI in Businesses .................................................................................... 12 3.4 Developing AI Internally or Externally ............................................................................................... 21 3.5 Innovation Methods ............................................................................................................................. 22 3.6 Summary .............................................................................................................................................. 25
4 Developing an Initial AI Framework ......................................................................................................... 26 4.1 Initial Framework Design .................................................................................................................... 26 4.2 Initial AI Framework ........................................................................................................................... 27
5 Empirical Result from Interviews .............................................................................................................. 29 5.1 Interviews ............................................................................................................................................. 29
6 Developing a Final AI Framework ............................................................................................................ 44 6.1 Final Framework Design ..................................................................................................................... 44 6.2 Final AI Framework ............................................................................................................................ 44
7 Discussion and Conclusions ........................................................................................................................ 54 7.1 Findings of the Study ........................................................................................................................... 54 7.2 Critique ................................................................................................................................................ 54 7.3 Further Studies .................................................................................................................................... 55
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1 Introduction This section gives an introduction to the study area. The problematization is presented followed
by purpose, study question, and limitations with the study. Lastly, an explanation of which types
of products that apply to this work is presented.
1.1 Background In recent years there has been an Artificial Intelligence (AI) hype. All over the newspaper, you
can read about AI and how it will impact our daily life and the way we work and interact with
each other. Moreover, AI has slowly started to move from academia into the world of
businesses. In a global survey from 2017, with more than 3000 executives and managers as
respondents, almost 85 % believed that AI will give their businesses competitive advantages,
but at the same time, only 20 % had incorporated it into their businesses (Ransbotham et al.,
2017). The industrializing of AI is definitely under formation and while the upsides of what AI
can provide for businesses are enormous, there are also risk with investing in new technology.
While almost all of the published cases where AI is introduced into businesses are about success
stories, there are also cases where expectations have not been met. Kartik and Apoorv (2017)
state that most companies’ AI initiatives will fail. The reason AI projects fail is not that AI is
an overhyped technology, but because companies are approaching AI-driven innovation
incorrectly (Kartik and Apoorv, 2017).
The few guides available for AI implementations are either too focused on one specific area or
failing to cover the whole picture of the implementation process. A guide specialized for AI
implementations in product offerings does not exist at all. Besides, the available AI guides from
the literature are focusing on what could be done but lacking the practical aspect of how it
should be done. This gives companies no other choice than to investigate and implement AI
without a map showing how to do it (Kolbjørnsrud et al., 2017). Companies put their trust in
AI to solve all their business problems, even though a majority of their employees have no idea
what the technology is about, and the management teams have difficulties implementing it. This
thesis aims to give clearance for organizations on how they can implement and work with AI
in their product offering.
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1.2 Problematization Even though AI can contribute with value for most businesses, difficulties with the
implementation of AI leads to wasted money, and unfulfilled expectations.
1.3 Purpose The objective of this project is to provide guidance for organizations with the implementation
of AI in their product offering. This guide should highlight important aspects to consider with
the implementation and the continuous work with AI in product offerings.
1.4 Study Question The following study question has been outlined:
I. Which is the optimal way for organizations to implement AI in their product
offerings?
The study question has been divided into the following sub-questions:
I. What aspects are important to consider during the implementation of AI in an
organization’s product offering?
II. During which phase of the implementation should these aspects be considered?
1.5 Limitations To find an optimal way for organizations to implement AI in their product offering, lots of
testing and feedback are needed. In this thesis, the empirical data comes from employees
working at a particular company. Since the goal of this study is to find a generic method for AI
implementations in organizations’ product offerings, the results need to be tested and analyzed
on several organizations to confirm the validity. Due to confidentiality reasons, the organization
which this study was conducted with, as well as all the interviewees, will remain anonymous.
The company where this work was conducted will in this thesis be called Company X.
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1.6 Company X and their Product Offering Since Company X will be anonymous, an explanation is needed to get an understanding of what
types of products this company are producing, and thereby how AI in product offerings may
look like. The work in this thesis assesses products that have some sort of data collection which
enables analysis. Some examples of such products could be industrial machines, cars, heavy
trucks, busses, excavators, and lawnmowers. The list of connected products where AI can be
integrated is enormous and the given examples are just a few to give the reader an idea of what
they may look like.
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2 Method In the following section, the methodology to solve the study question is described and a
discussion about why this method is chosen presented.
2.1 Study Design To answer the study question, a case-study with Company X is conducted. The choice of doing
a case-study for answering the study question is supported by the in-depth and precision needed
to answer several of the questions around AI implementations in product offerings. Yin (2018)
argue that the more in-depth questions needed to answer a certain issue, the better the choice of
using a case-study. Even though Yin (2018) also states the importance of doing multiple case
studies to get a more valid result, time constraints have limited this study to only focus on one
organization and their work with AI. Another reason for performing a case-study is because
studies about AI in business is a relatively new research area, Eisenhardt (1989) argue that
performing a case-study is good when the research area is new.
To answer the study question, a qualitative method was executed. A qualitative method, in
opposite to a quantitative method, focuses more on text-based data compared to number-based
data. Data generated from interviews are mainly of qualitative type (Walliman, 2011). Besides,
this report follows an inductive study method in contrast to a deductive method. Using an
inductive method is common when analyzing qualitative data (Thomas, 2006). An inductive
method means starting from specific observations and going to broader generalizations and
theories, while a deductive approach does the opposite (Burney and Saleem, 2008). The
observations can in this thesis be seen as the input from both the literature review and the
employees working at Company X. An inductive method provides a simple and straightforward
way to evaluate and come to conclusions about the observations (Thomas, 2006).
In this study, the analysis and the collection of data were done iteratively. The choice of
performing an iterative method during the study was to be able to adjust the scope of the
interviews depending on the analysis from past interviews. This approach is supported by
Bryman (2018), who states that qualitative research often is performed with an iterative method,
which means shifting between collection and analysis of the data.
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To answer the study question, an AI framework was developed. The developed AI framework
provides guidance for organizations during the process of implementing AI in their product
offerings. An initial framework was created after the literature review. The initial framework is
grounded in academic reports and reports from management consultant agencies. With input
and feedback from the interviews with employees at Company X, the framework is evaluated
and updated. The main reason for discussing the initial framework during the interviews was to
be able to get concrete feedback from the interviewees on the framework. The procedure to find
an appropriate framework for AI implementation in organizations product offerings is shown
in Figure 1.
2.2 Literature Review Stage In the literature review stage, reports about AI implementation processes from both academia
and consultant agencies were reviewed. To avoid spending time developing knowledge that is
already known, it is important to investigate the current state of knowledge within the field
(Walliman, 2011). The objective of the literature review was to get an understanding of the
most important aspects organizations should consider during AI implementations, and with this
information, construct an initial guiding framework.
During the literature review, LUBsearch and Google Scholars were used to find relevant books
and reports. At the beginning of the literature review, only the most cited books and articles
Literature Review Stage: Interview Stage: Final Stage:
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were reviewed, however, since the area of study is a relatively new field, and most of the studies
are not older than five years, the literature scope was broadened to include consultant agencies
reports and less cited reports as well.
After the literature review stage, an initial framework was established. This initial framework
was constructed by the author and contains the aspects which were highlighted the most by the
reviewed literature. The selection process for the initial framework is described in further detail
in chapter four.
2.3 Interview Stage The interviews were held with employees at Company X who in some way are involved with
AI. A broad group was interviewed which includes employees from different functions and
from different geographical areas. By interviewing individuals with different backgrounds, a
broad understanding of the company’s view on AI is attained. Implementing AI in an
organization is a challenge that affects a broad part of the company, for example, both people
working with business strategy and data management. Because of the wide scope of expertise
needed, one employee will rarely be able to discuss all areas of AI in-depth. For this reason,
interviews with a broad target group were conducted.
Before the interviews were conducted, all interviewees received a one-pager that contained
information about the interview and the purpose of doing it. All interviewees were promised
anonymity as well as the possibility to read through their answers before publication. Most of
the interviews were held in Swedish except for a few of them that were held in English. The
interviews were conducted during the period early March until mid-April 2020. At first, the
interviews were held in-person, but due to the covid19 outbreak, the later interviews were held
using Microsoft Teams. All interviews were one hour long and directly after the interviews
were finished, the answers were transcribed.
The interview process is divided into three parts. During the first part of the interview, the
interviewee was identified. In this part, questions about the interviewee’s profession and
involvement with AI were asked. The second part of the interview assessed the interviewee’s
thoughts and concerns about AI in general and with the current AI projects at Company X. This
part involved questions about potential risks the interviewee identifies, challenges, strengths,
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weaknesses, and critical factors for succeeding with the implementation. The third part of the
interview was focusing on the framework itself and aimed to assess the framework and its
ability to evaluate the readiness of the company for the implementation of AI. The initial AI
framework was not shown until the third stage of the interview. By first discussing the current
situation regarding AI and Company X, the respondents gave their own input about important
factors and potential risks to consider during the implementation. By doing so, the interviewee’s
input was not affected by the factors mentioned in the framework, and they were able to criticize
and come with suggestions about the framework afterwards. The full list of the interview
question can be found in the appendix. In Figure 2 below, the interview process is shown.
Figure 2 The interview setup.
During the interviews, open-ended and neutral questions were asked. By doing so, the
respondent can give their own opinions about the answers without influence from the
interviewer. Because of limited time with the interviewees, questions that could be replied with
yes or no were avoided because of the reduced information gained with these types of questions.
Critics about interviews as a source of data state that there is a risk of bias in the result. However,
Eisenhardt and Graebner (2007) argue that these risks can be mitigated by conducting several
interviews with persons that view the question of interest from different perspectives. These
different perspectives can mean informants from different hierarchical levels, business
functions, and geographies (Eisenhardt and Graebner, 2007). In this study, the risks with biases
during the interviews are mitigated by conducting several interviews with informants from
different hierarchical levels and different business functions. Most of the interviewees had their
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working base in Sweden, but several of the interviewees also worked in other geographical
areas.
To be able to get the most out of the interviews, pilot interviews were conducted. The purpose
of the pilot interviews was to get feedback on the interview concept. With the input from the
pilot interviews, interview questions were adjusted to better reflect the purpose of the
interviews. The pilot interviews were also conducted with employees at Company X.
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3 Literature Review
This section presents the theoretical framework used in the study. It begins by introducing
artificial intelligence and machine learning. Thereafter are studies of how these technologies
have been implemented in businesses presented. The main part of the literature review is
thereafter presented which discusses important aspects to address during AI implementation.
A discussion about innovation methods used in businesses is also presented. The literature
review ends with a summary of the findings.
3.1 Artificial Intelligence The research about AI goes all the way back to 1950, where Alan Turing implemented the term
artificial intelligence as a computer’s ability to act and think like a human. Turing (1950)
explains that the idea behind a digital computer is to be able to handle any operations that could
be done by a human. Another classification of AI defines it as “the study of the computations
that make it possible to perceive, reason and act” (Winston, 1992).
AI has been a research topic for a long time, but only recently reached the general interests of
business. This is due to several reasons. Firstly, the volume of accessible data that could be
used in AI applications has reached sufficient levels. The future growth of available data seems
to follow a general trend which doubles every second year. Secondly, the data generated need
to be stored. With today’s new technologies the cost of storing data has decreased, making it
affordable to store large amounts of data. A third driver behind this AI explosion is faster
processor speed, making it possible to process large amounts of data in a short period of time.
Lastly, with today’s improvements in broadband and 5G, it is possible to distribute large
amounts of data between servers and devices in a short time. This connectivity improvement
means that most of the processing can be carried out at data centers or in the cloud and that the
user device is acting only as a front-end platform. (Burgess, 2018)
AI is a broad term and could be broken down into several subcategories depending on what the
goal is to get out of the process. Some of these subcategories are machine learning, natural
language processing, and computer vision. AI could also be divided depending on what type of
technical approach that is used, examples of these are neural networks, deep learning,
regression analysis, and Bayesian networks.
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3.1.1 Machine Learning Machine Learning is a subset of AI and uses statistical methods to extract information and
predict outcomes from data. A machine learning algorithm analyzes the current state, and
depending on the state, performs an action. What is unique with machine learning compared to
a standard software program that is performing an action is that the machine learning algorithm
learns and improves itself continuously as more data are generated (Gerbert et al., 2017).
The learning process in machine learning can be divided into three different types depending
on what feedback it is getting to learn from (Russell and Norvig, 2010). The three types of
learning are supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, both input and output values are given to learn from. In the learning
process, the system maps a function between input and output (Russell and Norvig, 2010). An
example would be to determine whether a picture taken shows a dog or a cat. An industrial
example of supervised learning could be to detect failures with the help of data over past
failures.
In unsupervised learning, input values are given, and the task is to find similarities within the
input. The learning process occurs even though no obvious feedback is given. The task in these
cases is often to detect clusters in data (Russell and Norvig, 2010). An industrial example of
unsupervised learning could be to govern the operations to make sure it works as it is supposed
to.
In reinforcement learning, only a small amount of data is given, making it hard to make good
predictions. The learning process takes the form of trial and error together with rewards and
punishments, reinforcements. It is up to the system itself to evaluate which of the actions before
the reinforcement that was responsible for the outcome. (Russell and Norvig, 2010)
A simple example of where machine learning could be used is to predict the price of a house.
One way to do so is to give the computer information of past house prices together with features
about the house. These features could be numbers of bathrooms, size of the house, number of
floors, distance to the closest large city, and so on. With the data about the features and the
selling price, an algorithm could be built that predicts the price of the house when given the
features. After the house is sold and the selling price is known the algorithm is updated. The
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algorithm is said to learn and adjust the contribution each feature is having on the selling price.
This type of learning is described above as supervised learning.
Machine learning is only a subset of AI, however, in this report, AI will be used to describe
machine learning as well. This is done even though machine learning might be the part of AI
which has the most potential for businesses right now. Grouping machine learning under AI
does not mean that they are equivalent but is instead done to make it easier for the reader to
follow the thesis when only one terminology is used.
3.2 AI in Businesses The spreading of AI from academia to businesses has not been going as fast as could be
expected when reading about AI and the hype around it. In a recent study, more than 80 % of
executives asked said to believe that AI will help them achieve competitive advantages.
However, only 20 % of these executives said to have incorporated AI so far in their businesses
(Ransbotham et al., 2017). The same study also reports that 14 % of the asked executives say
that AI plays a big part in their business at the moment and that 63 % expect AI to play a big
part in their business in 5 years.
Commonly in the literature, stories are told about successful AI implementations and that AI
has increased their companies’ sales, cut their costs, and enhanced their businesses. Brock and
von Wangenheim (2019) give an example of a hospital in Spain which with the help of AI was
able to implement a solution to diagnose patients more effectively. The results of this
implementation were both a higher accuracy in the preliminary assessment of patient records,
but also time savings for their medical staff. Another success story is about the sports company
Under Armour, who created a fitness app to provide customer-made training programs. Besides
collecting weather and time data when suggesting training program, they also consider
behavioral and psychological factors as well as similar profiles and their training habits when
designing the training program (Burgess, 2018). In contrast to these success stories, there is
also strong evidence telling that most of the AI implementations fail, and do not reach the
desired targets (Kartik and Apoorv, 2017). Either their expectation was set to high, or their
implementation strategy was insufficient.
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Even though the AI gap between academia and businesses is shrinking, there are several factors
companies need to consider to be able to implement AI in their business in an efficient way.
Both technical and organizational factors need to be considered in order to implement AI.
3.3 Key Factors to Succeed with AI in Businesses There are several factors to address when implementing AI in businesses. Below, key findings
from five selected reports and books about how to succeed with AI implementations are
presented. The following literature review is presented study by study.
3.3.1 Burgess (2018): The Executive Guide to Artificial Intelligence Burgess (2018) presents a guide for how organizations can implement AI. Burgess (2018) also
discusses several pitfalls that could happen during the implementation process, which could be
interpreted as warnings or important issues to address during the implementation. AI should be
deployed where it can create the most value for the company, which often means through
existing products. The following steps in the following order are advocated by Burgess (2018)
for organizations’ AI implementation projects:
1. Align AI projects with business strategy
The single most important activity for achieving success with AI is to have the companies
AI plans aligned with the overall business strategy. AI should be implemented to reach
business strategic objectives and to be able to deliver real value to the business.
2. Understanding your ambitions with AI
Understand what the business objectives are regarding AI. The firms’ ambitions with AI
will help steer the AI projects going forward and affect several of the upcoming decisions
during the implementation stage.
3. Assessing your maturity for AI
It is important to provide an overview of the current situation of the business process areas
such as HR, operations, finance, etc. The analysis of the current situation should give
clearance of how digital each process area is. If a process area already is chosen for
implementing AI, that chosen process area should be broken down into all the different
stages of that specific process.
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4. Creating an AI Heat Map
An AI Heat Map should act as a map for AI opportunities. The AI heat map should highlight
the opportunities for each business process area, the same areas as the maturity was checked
for in the previous stage. It should show where AI is desirable and viable, both economically
and technically.
5. Develop an AI business case
Creating a business case for the AI project should be done in similar ways as a regular
business case. The benefits should be compared to the costs. A difficulty with AI projects
is the uncertainty that comes with how to calculate return on investment and other benefits.
6. Understand change management
Prepare for potentially upcoming challenges with automation and AI. There will be
fundamental changes in the way people work, and there might also be a reduction in the
workforce due to increased automation that comes with AI.
7. Develop an AI roadmap
A roadmap that shows the medium to long plan regarding AI should be developed. The
roadmap should be closely linked with the company’s AI ambitions and business strategy.
Besides providing guidance for how AI should be implemented, Burgess (2018) also addresses
several pitfalls that could happen during the implementation. It is important to both highlight
benefits and potential risks with the implementation of AI to give a balanced view (Burgess
2018). The pitfalls mentioned cover both specific and general considerations and are presented
below.
• The challenge of poor data – Available data quality is not sufficient to reach desired
results.
• Understanding the lack of transparency – The way AI algorithms provide guidance is
not transparent, for example, which features that influenced the decisions is not clear.
• The challenge of unintended bias – The data used for training the algorithm might
have biases, which in turn will provide a biased result.
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• Understanding AI’s naivety – AI might be able to find patterns in data that correlate
but are not casual, for example, the color of a person’s eyes and the likelihood to buy
yogurt at the store.
• Becoming over-dependent on AI – Having too much dependency on a particular
function is a risk, it might stop working or stop working effectively unnoticed.
• Choosing the wrong technology – Committing to one particular AI technology is a
risk, how AI systems of tomorrow will be shaped are unknown.
• Preparing for malicious acts – Data is a valuable source, losing it might be costly.
Therefore, addressing data security questions are important.
3.3.2 Brock and von Wangenheim (2019): Demystifying AI Brock and von Wangenheim (2019) conduct a study where they survey senior managers across
several industries to find out how companies that have been successful with their AI
implementation managed to do it. Their study compares how companies that are successful
with AI differ from companies that are not as successful. They find that there are eight factors
that are significantly different among companies that are successful with AI compared to the
ones that are not. These are organizational agility, engagement of skilled staff, leadership,
support from technology partners, investment, culture, alignment of new digital technologies
with existing IT, and learning from failed projects (Brock and von Wangenheim 2019).
To assess companies in terms of these eight factors, the authors have constructed a
questionnaire companies could use to check how ready they are for AI. Their guidelines for
successful AI applications have the acronym DIGITAL, and for each letter, Brock and von
Wangenheim (2019) provide some questions to assess companies’ AI maturity. The more yes
or clear answers a company has on the following questions, the more DIGITAL the company
is, and therefore, the more likely the company is to succeed with their AI projects (Brock and
von Wangenheim, 2019). The following questions are suggested by the authors for evaluating
a company’s AI maturity:
Data
Do we own or have access to data that are relevant to analytically solve the business problem
we are addressing?
Are the data sets sufficiently large to be efficient and effective?
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Are the data sufficiently complete, consistent, accurate, and timely?
Intelligent
How can AI help defend, grow, or transform our business?
How can AI improve operational efficiencies?
Do we have a digital strategy in place?
Do we have the managerial and technical skills required to support successful digital
transformation with AI? If not, how do we develop or acquire these skills?
Are we willing and able to tolerate investing in an emerging rather than deterministic AI digital
transformation journey, including accepting failure?
Grounded
Are we experienced enough and resourced properly for the scope of the project?
Are we following an incremental, current business-focused approach with our AI projects?
Do we have an AI project roadmap?
Integral
Have our firm’s core business processes been digitalized?
Has our firm analyzed what existing/new offerings can benefit from AI?
Has our firm integrated all data into one single data repository?
Is our firm’s existing IT compatible with the AI technology we plan to adopt?
Teaming
Does our firm know with whom to partner in support of our AI success?
Does our firm know with whom competitors’ partner in their AI projects?
Did our firm develop or join an ecosystem to enhance its offerings?
Agile
Compared to our competition, how quickly and frequently are we adapting our processes and
offerings to stay competitive?
Compared to our competition, how flexible are we to accommodate small, medium, and large
changes to our processes and offerings?
Leadership
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Is our executive team and middle management comfortable and supportive of the changes that
AI will bring to our firm?
Is our executive team and middle management actively endorsing and continuously
communicating the status and progress of our AI activities to all stakeholders?
3.3.3 Ransbotham et al. (2017): Reshaping Business with Artificial Intelligence Ransbotham et al. (2017) are conducting a global survey where they investigate companies’
ambitions and efforts with AI. Their report contains answers from more than 3000 executives,
managers, and analysts from organizations all over the world. Based on the adoption level of
AI in the surveyed organization, Ransbotham et al. (2017) divided the organizations into four
groups going from pioneers, which contains organizations that understand and already have
adopted AI, to passives, which are organizations with no AI adoption nor much understanding
about AI.
Ransbotham et al. (2017) compare AI pioneers with AI passives to see what types of barriers
these organizations see with AI and how these differ between the organizations. They find that
attracting, acquiring, and developing the right AI talent, and security concerns regarding data,
are ranked as two of the largest barriers for AI adoption for pioneers, while these are seen as
two of the smallest barriers for passives. Competing investment priorities are ranked as a barrier
for AI adoption for all asked firms, no matter how successful the company is with AI. Unclear
or no business case for AI projects is ranked as the largest barrier for AI adoption for companies
seen as AI passives but as the smallest barrier for companies seen as AI pioneers.
One of the most telling differences between AI pioneers and AI passives is the understanding
of the link between data and AI. AI pioneers are 12 times more likely to recognize and
understand the process for training the AI algorithms. In general, most of the surveyed
organizations have little understanding of the importance of training the algorithms on
company-specific data and instead believe that a sophisticated algorithm can reach desired
results without a sufficient amount of data. In addition, some forms of data scarcity often go
unrecognized by organizations, for example, positive results alone are seldomly enough for
training an AI algorithm. Data of negative results are often critical for building an algorithm,
and negative data is often unpublished, which could lead to biases in the data and therefore in
the algorithms as well. (Ransbotham et al. 2017)
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Ransbotham et al. (2017) discuss data ownership and access to data. They state that companies
sometimes believe they have access to the data needed for AI which in fact they do not. Data is
proprietary and the organization that owns it might have no incentives to give it away to others.
To get access to customer data, organizations need to ensure customer trust and also show
customers how they will benefit from the AI initiatives to be built. Besides these external data
concerns, there are also considerations to be worked out with the internal data systems. For
organizations, especially larger organizations, data is often fragmented across multiple systems
and storages, which can delay and obstruct the process of training the algorithms (Ransbotham
et al. 2017).
Ransbotham et al. (2017) also present three challenges that management teams will have to deal
with regarding AI. Firstly, management teams need to develop an intuitive understanding of
AI. Executives and managers need to understand the basics of AI, what it can achieve, but also
its limitations. The authors suggest taking an online course to find out how AI programs learn
from data, which they see as the most important factor for understanding how AI can benefit
organizations.
Secondly, organizations need to start organizing for AI. Adopting AI will change companies’
organizations and new forms of collaboration will be formed. These collaborations might be
project teams of both humans and machines. Organizing for AI also requires finding the right
people. Ransbotham et al. (2017) mention three types of people who will be needed: technical
people who experiment with ways of working with AI, technical people with business domain
knowledge, and people with project management skills who can bring them all together.
A third challenge management teams will be facing is to re-think the competitive landscape. Of
the asked respondents, more than 60 percent say that a strategy for AI is crucial, but only half
of them have one (Ransbotham et al. 2017). Having access to data sources is key for competitive
advantages, which organizations need to understand and integrate into their business strategy.
3.3.4 Gerbert et al. (2017): Putting Artificial Intelligence to Work Gerbert et al. (2017), management consultants at Boston Consulting Group, presents a guide
for how companies should implement AI and spread their organization around the technology
in an easy way. The conceptual way for introducing AI to a process is intuitive, AI algorithms
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absorb data from sensors, the data is processed and then an action is performed (Gerbert et al.,
2017). For the implementation of AI, they suggest three steps in the following order:
1. Ideation and testing
Businesses should focus on four areas: customer needs, technological advances involving
AI, data sources, and systematic breakdown of processes. These areas should give guidance
to find the most promising use cases for AI. By considering customer needs, companies are
making sure that their AI application creates value for the customer. A smart way for finding
attractive opportunities for AI is to systematically breakdown processes into isolated
elements, and from there see which parts can be automated with AI. (Gerbert et al., 2017)
2. Prioritizing and launching pilots
Company leaders should prioritize the pilot projects on expected return and when the return
could be realized. The pilot projects should be run as test-and-learn projects to identify
capabilities that need to scale up. At this stage, companies will not have the data
infrastructure to run these pilot projects smoothly, but the pilots will instead provide
information and help companies prioritize the future work and which processes that need to
be scaled up. Each pilot should have a clear scope of how to deliver concrete customer
value, but also define the required infrastructure and architecture needed to reach there.
(Gerbert et al., 2017)
3. Scaling up
Lastly, the pilots should be scaled up into run-time processes and offerings. Scaling up the
pilots will consist of building the right competencies, processes, organizational structure
and data infrastructure. (Gerbert et al., 2017).
In addition to the described implementation guide for AI, executives should also prepare
themselves and their organization with the following activities:
• Understanding AI
Executives and managers need to understand the basics of AI and what is possible to
achieve. They need to develop a functional understanding of AI. A way of doing so would
be to start experimenting with development tools themselves or take an online course about
it. (Gerbert et al., 2017)
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• Performing an AI health check
The management team should evaluate where the company is at the moment in terms of
technology infrastructure, organizational skills, setup and flexibility. It is also important to
understand the accessibility to both internal and external data. (Gerbert et al., 2017)
• Adding a workforce perspective
With the introduction of AI to the workplace, both emotional stresses for the employees and
their need for retraining could happen. Communication, education, and training are
important factors to think about already from the initial design. The workforce will have to
adjust their working methods when robots will do parts of their usual work (Gerbert et al.,
2017)
3.3.5 Mohanty and Vyas (2018): How to Compete in the Age of Artificial
Intelligence In their book, How to Compete in the Age of Artificial Intelligence, one of the things Mohanty
and Vyas (2018) present is a guide for how to implement AI. They define three broad
prerequisites, access to data, capabilities to interpret the data, and a way to make predictions
out of the data that can contribute to business value. Mohanty and Vyas (2018) present a four-
step guide for succeeding with AI projects. These steps are presented below.
1. Identify potential AI use cases
The management teams need to quickly find use-cases for AI, where AI can contribute to
business value. It can easily happen that companies become observers when new
technologies emerge, to avoid this, AI use-cases need to be formulated. (Mohanty and Vyas,
2018)
2. Assess adoption scenarios
With high-level AI use-cases developed in step one, the next step is to further assess these
cases to find which type of adoption scenarios the AI projects will go through. In this stage,
market studies, surveys with existing customers and industry experts will be helpful. The
objective is to find early signals of customer readiness, low implementation costs, low
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switching barriers, and so on. The more positive these indicators are, the more ready the
initiative is for mass adoption. (Mohanty and Vyas, 2018)
3. Assess your internal capabilities
Knowing the adoption scenarios of the AI projects, the next step is to assess the company’s
internal capabilities. It is essential to know the maturity level of the internal processes,
technology landscape, and the skills of the employees. For companies in more conservative
industries regarding digitalization, there might be trouble bringing these AI ideas into life,
therefore it is important to work through expectations and prioritizing the ideas, create a
roadmap and a starting point. (Mohanty and Vyas, 2018)
4. Launch the AI transformation program
Mohanty and Vyas (2018) argue that there are three areas an organization needs to work
with during the transformation program.
I. Establish sponsorship and governance
AI is less of a technology revamp and more of a cultural shift between the workers.
Technology leadership needs to advocate how AI will help solve business problems.
A requirement needed is executive sponsorship with the initiatives, to get the
support, the executives that are more skeptical towards AI are the ones that need to
be engaged the most. (Mohanty and Vyas, 2018)
II. Action plans: Experiment, fail, and learn
AI is a rapid and moving technology, companies need to follow the movement. Fail
fast and learn, construct working prototypes for customers and use the feedback to
improve the products. (Mohanty and Vyas, 2018)
III. Invest and develop AI capabilities
The fact that AI is a new technology means that the capabilities and competencies
needed to capture AI are rare. Companies need a plan for how to attain these
capabilities, and the necessary infrastructure to enable AI. A centralized AI center
of excellence could be a good start to support departments across the firm. Given
the scarcity of competencies within AI, companies should create partnerships across
company borders for developing their AI projects. (Mohanty and Vyas, 2018)
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3.4 Developing AI Internally or Externally The previously presented guides for AI implementations describe what should be done and
critical factors to address but they do not state who should do it. Companies implementing AI
generally stand with two options, developing the technology internally or buying it from a
vendor. A third alternative is, of course, in-between, where some part of the technology is
bought, and some developed internally.
To achieve the desired results with the implementation of AI, the project group needs to have
the right competencies. Companies seen as AI pioneers’ ranks attracting, acquiring, and
developing the right AI talent, as their biggest challenge to adopting AI (Ransbotham et al.,
2017). Moreover, Brock and von Wangenheim (2019) show that companies seen as AI leaders
see support from technology partners as a key factor to success. If a company decides to team
up with a technology partner in their AI projects, it mainly provides them with two advantages
(Brock and von Wangenheim, 2019). Firstly, they get accompanied by a partner who has built
up experience within AI and do not have to start from scratch. Secondly, it gives them a quick
start to a new technology that is still emerging and therefore could be hard for a company to
build by themselves and keep up with (Brock and von Wangenheim, 2019).
Even though buying an AI solution gives the company a fast-paced start on an AI project, the
choice of buying a solution is not that simple. Generating value from AI requires a variety of
tasks such as collecting and integrating relevant data, training the models, developing
algorithms, and supervising them (Ransbotham et al., 2017). If a company decides to go
externally for their AI projects, there is still a need for in-house knowledge. Companies need to
have their own people knowing how to structure problems and process data to be able to capture
upcoming opportunities with AI (Ransbotham et al., 2017). Mohanty and Vyas (2018) argue
that a good way to start the work with AI for a larger firm is to establish a centralized center of
excellence. This unit should provide guidance and support across the whole firm with AI
projects. However, due to the fact that AI capabilities are scarce, companies should also
collaborate with partners to create innovation labs to get the most out of their AI initiatives
(Mohanty and Vyas, 2018).
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By outsourcing the whole, or some part of the company’s AI processes there are risks.
Traditional risks such as dependence on the supplier, hidden costs, internal loss of knowledge,
and service provider’s lack of necessary capabilities should be considered. In addition to these
risks, AI solutions themselves are becoming a competitive space, meaning that companies can
distinguish themselves from their competitors with their AI solutions (Ransbotham et al., 2017).
Going externally for AI solutions could potentially destroy this advantage due to a more
mainstream, off-the-shelf, product.
For the future, organizations believe that the adoption of AI is going to affect their workforce.
Almost 85 % of asked executives say that existing workers will need to change their skill set
(Ransbotham et al., 2017). Therefore, a strategy for how management teams should educate
and recruit a future organization is useful.
3.5 Innovation Methods Even though the process of implementing AI in product offerings is a new area, there are several
studies covering product development and innovation processes in businesses. Furr and Dyer
(2014) cover the whole process from idea generation until business scale up in their innovation
process. The authors state that the process could be divided into the following sections, see
Figure 3.
Figure 3 The Innovator’s Method (Furr and Dyer, 2014).
The goal of the first section is to generate insights about a problem worth solving. This could
be finding insights about problems that others have missed or to find new solutions to a well-
known problem. There are four key actions to trigger an insight, these are questioning,
observing, networking, and experimenting. By constantly question the usual way of doing
things, ideas and insight is generated. Also, by observing how the products are used in action,
insights about improvements are generated. Through networking, other people’s ideas and
thoughts are captured which also is a key for developing new insights. Lastly, by experimenting
and testing how things work, ideas and insights are generated. (Furr and Dyer 2014).
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After an insight is generated, the next part of the innovation method is to discover the problem
and what the job is to be done. Furr and Dyer (2014) discuss traditional ways to discover
problems like marketing studies, analyst reports, and surveys, and state that these methods
cannot be counted on. The problem with these methods is that they will not provide deep enough
information to observe real customer problems. Instead, the authors introduce a method called
pain-storming. Summarized, their method focuses on the customer value chain and identifies
complications along the chain. The most critical obstacles for the customers are analyzed deeper
with root cause analysis to identify assumptions behind these causes.
Having identified and analyzed the customer's most critical causes, next is to come up with a
solution. Furr and Dyer (2014) mention looking both closely and far away to find possible
solutions. By looking closely, they are referring to workarounds, where the customer already
has developed duct-tape solutions. On the contrary, solutions far away could be solutions
applied in other industries which could be applicable in their process as well. Furr and Dyer
(2014) develop a prototype scheme that starts with theoretical prototypes followed by virtual
prototypes, minimum viable prototypes, and lastly, minimum viable products, see Figure 4. The
authors also mention to regularly test the developed prototypes with customers and collecting
their feedback. Positive customer feedback is what should move the ideas to the next prototype
stage in the scheme.
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Figure 4 The advancement of ideas into products (Furr and Dyer, 2014).
Having a customer verified product, or a minimum awesome product according to the authors,
the next step is to develop a business model around it. The most important dimension of the
business model is the value proposition, which describes what value the targeted customers will
gain with the product. Furr and Dyer (2014) are advocating a modified version of Osterwalder
and Pigneur (2010) and their Business Model Canvas template for developing a business model.
The model used by Furr and Dyer (2014) contains six areas divided into three groups, cost
structure, customer acquisition, and provided solution. Within the cost structure, activities and
resources required to deliver the solution are discussed. Customer acquisition focuses on
customer communication and through which channels customers can be reached. Lastly, in the
provided solution, value proposition and pricing strategies are discussed.
With the business model finished, Furr and Dyer (2014) also present how to scale the processes.
They talk about the importance of standardizing and assigning each task needed to be fulfilled
to individuals. Also, a visual map or plan is helpful to show the linkage between tasks and to
highlight responsibilities. Finally, by connecting each task and process to a measurable metric
it is possible to track the progress, which is important.
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3.6 Summary AI is on its way to establish itself in businesses, but before the breakthrough will happen,
companies need to be able to handle the implementation process of AI. Since the research
surrounding AI in business is a new area, the information is limited. However, the ones
available show that there are several factors that companies need to consider before and during
the implementation of AI.
From the presented studies, it is clear that AI is not only a technical difficulty. Other aspects
such as the long-term strategy and organizational aspects regarding the adaption of AI has to
be considered as well. Foremost, AI has to be deployed where value can be achieved. For AI
implementation in product offerings, feedback from customers are crucial. Having a close
relationship with customers is advocated in regular product development and innovation
processes, and there is no difference when AI is to be deployed.
The technical aspects of AI are closely connected to data and data infrastructure. Having a high
quality of the data is a prerequisite for getting good output from the AI algorithm. The learning
process of AI algorithms depends on data and often on company-specific data, AI is not an off-
shelf product.
Having the right organization and AI capabilities is a requirement for achieving value with AI
projects. The capabilities needed for AI could be attained internally or externally through
consultancies or technology partners. To get access to these needed resources, leadership
support is a requirement. Therefore, getting the management team involved with the projects is
a key factor.
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4 Developing an Initial AI Framework This section describes how the initial AI framework is developed followed by a presentation of
the framework.
4.1 Initial Framework Design The initial framework contains five subcategories which each of these, in turn, contains three
subcategories. The parameters for the initial AI framework are generated from the literature
review. Table 1 presented below shows in which reports each of the chosen parameters are
discussed and mentioned. A capital letter X indicates that the factor was mentioned as important
in that study, likewise, a minus sign means it was not mentioned. In several cases, the exact
names of the chosen factors are not the same in all the studied reports and in the created
framework. As long as they are addressing the same issue the cell in the matrix shown below
will contain an X-mark.
Logically, most of the parameters chosen for the initial AI framework are discussed in several
of the studies from the literature review. This means that several studies agree that it is
important. However, in some cases, there might be only a couple of the presented studies that
consider the parameter as important. The reason behind this could be that some of the studied
reports dig deeper into some implementation aspects, while other have a broader scope with
their studies.
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Table 1 An overview of the chosen factors for the initial AI framework, and which of the literature studies that also highlight these factors.
4.2 Initial AI Framework The initial framework contains five subcategories. These five subcategories are business
strategy, data infrastructure, AI progress, management team, and organization. The full initial
framework is presented below.
Business AI Framework Burgess (2018)
Brock and von Wangenheim (2019)
Ransbotham et al. (2017)
Gerbert et al. (2017)
Mohanty and Vyas (2018)
Business Strategy Long-term Strategy with AI X X X - -
AI Deployment X X X X X Value Creation with AI X X X X X
Data Infrastructure Data Management System X X X X -
Access to Data X X X X X Data and Learning X X X X X
AI progress Align AI with Business Strategy X X X - -
Current Situation Analysis X X - X X AI Plan X X X X X
Management Team Top Management Support X X - - X
Learn the Basics of AI - - X X X Get Everyone On-board - X - X -
Organization Right Competencies X X X X X
Outsourcing vs In-House X X X - X Future Organization X - X X X
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Figure 5 The initial AI Framework developed with input from the literature review.
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5 Empirical Result from Interviews In this section, the results from the interviews are presented. The interviews are presented one
by one to highlight each interviewee’s thoughts.
5.1 Interviews In total, 16 interviews were made and an overview of the interviewees as well as their
professions are shown in Table 2. The next section will present the main findings from each
interview one by one.
Table 2 The interviewees and their professions.
Interview Number Profession Interviewee 1 Product Portfolio Development Manager Interviewee 2 IT Architect Interviewee 3 AI Technical Support Interviewee 4 Chief Enterprise Architect Interviewee 5 Backend Developer Interviewee 6 Lead Engineer and Technical Support Interviewee 7 Business Development Manager, Automation Interviewee 8 R&D Director Interviewee 9 Automation Engineer Interviewee 10 Head of IT Security Interviewee 11 Director Finance & Business Control Interviewee 12 Product Portfolio Manager Interviewee 13 Product Manager Service & Performance Interviewee 14 Product Management Interviewee 15 Strategy and Transformation Manager Interviewee 16 Information Architect
5.1.1 Interviewee 1, Product Portfolio Development Manager The interviewee works with product portfolio development and defines AI as a self-learning
tool that can be used to optimize processes and products. The interviewee interacts with AI
through customer requests. Customers ask for product features and improvements that often
require data analytics or AI. However, having customers requesting improvements is not always
30
the case. The interviewee says1 that sometimes the customer does not know what they want
until you show them a solution, which puts pressure on us to analyze our current products to
find improvement areas.
A difficulty the interviewee identifies with AI is the cultural change that needs to take place
when integrating AI into the business. The interviewee adds that it will take time to normalize
AI and have it adopted into the daily routines. This cultural change will need to happen
externally as well, customers that are using products with AI features need to be comfortable
with it. The interviewee mentions that the industry where they operate is not the most digital
industry, which makes it even harder to adopt AI. Operating in a conservative industry makes
it more challenging and we need to collaborate closely with the customers to make them
comfortable with AI. Another difficulty the interviewee sees is the productization of AI, how
to identify what value the customers get, and how to charge customers for the AI features in
practice.
The interviewee also mentions two challenges that need to be addressed for the long-term, the
need for organizational changes, and new product teams. We need to not only succeed with the
implementation part of AI but also to be able to integrate AI as a part of our core business, the
interviewee says.
The most important factor for succeeding with AI is according to the interviewee to have AI
projects grounded in the organization. The interviewee adds that the whole company needs to
be on-board with the changes that are happening with the implementation of AI. To succeed
with these organizational and cultural changes, the management team needs to advocate AI and
communicate with the rest of the organization about it.
5.1.2 Interviewee 2, IT Architect The interviewee works as an IT Architect. Tasks include coordinating the frontend and backend
developers on how to implement the desired system architecture. During the initial discussion
about a definition of AI, the interviewee clearly mentions that an AI system should have a
feedback loop that enables it to learn and get better as it is used. The interviewee adds that today
1 This report contains no direct citation from the interviews. The author of the thesis has written and formulated the text which represents the interviewee’s thoughts and ideas.
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simple statistics is seen as AI, there is no clear distinction of what should be classified as
intelligent.
A challenging area the interviewee mentions is the timing around the AI product launch.
Waiting too long for launching the product might be dangerous because no feedback and data
is given to improve the products, the interviewee says. The interviewee adds, on the other hand,
if the product is launched too early, without providing enough initial value for the customer, it
will also be problematic. The timing is crucial and having pilot customers to test the features
before the real launch is a key for mitigating this challenge. Another challenge the interviewee
mentions is getting the feedback loop for the learning part of AI to work. The user of the product
needs to provide feedback on what is happening for the learning process, we are dependent on
customer feedback, which means we have to collaborate continuously with the customers.
When discussing the most critical factors to succeed with AI projects, the interviewee says that
having a close relationship between the data scientists building the algorithms and the experts
in the processes, the people with domain knowledge, is important. A close connection between
these functions is needed, both to develop the right things and to make sure what is being
implemented is logical according to the processes. Another factor the interviewee identifies as
critical is having customers involved in the model development, having early feedback is a key,
fail fast, and learn from it.
5.1.3 Interviewee 3, AI Technical Support The interviewee works with AI development and supports the data scientists with their work.
AI is described as a technology automating what humans could do, replacing repetitive and
manual tasks done by humans. The interviewee comes in contact with AI through follow-up
work with the AI models, making sure they work as they are supposed to and not drifting away
from their normal state.
The interviewee identifies three main challenges with AI implementations. Firstly, data quality
is a key, having bad data quality into the algorithms will result in bad output. The interviewee
also mentions having the right type of data, if an AI task is to identify failures, the dataset used
for training the algorithm needs to contain failures as well. Secondly, one cannot just deploy
the models and expect them to work errorless, follow-up work to make sure the algorithms are
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producing desired results are required. Thirdly, having too high expectations about what AI
could accomplish is a problem, management teams today believes AI is a plug-in solution that
could solve any problems, which is wrong.
The most important factors the interviewee identifies for succeeding with AI implementation
projects are the following:
o Having high data quality;
o Implementing AI where value could be created;
o Having a strong business case behind the implementation project.
Another important factor discussed during the interview was the need for having the right
approach when undertaking AI projects. The interviewee talks about starting small and letting
the project expand slowly, giving room for all involved stakeholders to get familiar with the
project and changes that will occur.
5.1.4 Interviewee 4, Chief Enterprise Architect The interviewee works as an IT architect and AI is described as a tool which is using data to
gain insights about the future. The interviewee also adds that the learning process of AI is the
part which distinguishes AI from regular programming. The interviewee comes in contact with
AI through digital offering strategies and the required IT architecture needed to succeed with
these offerings. The work the interviewee is doing includes planning and putting together the
right infrastructure for enabling AI. The interviewee mentions an opportunity to be more
proactive and planning their processes with the help of AI, being able to take advantage of
available data streams to be more forward-looking.
One of the biggest challenges the interviewee sees with AI projects has to do with data quality.
The interviewee states that having high data quality is crucial to be able to capture value with
the AI initiatives, garbage in gives garbage out. Another challenge the interviewee mentions is
the internal data management system, storing data on several different servers and databases
leads to fragmentation of the data, making it hard both to access the data and scale the AI
models. A third challenge the interviewee identifies is attracting and hiring the right AI talent
for succeeding with the projects, skilled data scientists are hard to find. The interviewee states
the importance of both attaining and keeping data scientists close to the products and processes.
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By having decentralized data scientists, they will be able to work closely with the products and
the customer, which according to the interviewee is important to succeed. The interviewee adds
that operating in a traditional industry makes it even harder to attract talent compared to tech
companies.
The most critical factors to address during AI implementation projects are according to the
interviewee:
o Having a scalable data management system;
o Having high quality data to use in the algorithms;
o Having a clear value proposition behind the projects;
o Aligning the AI projects with the business strategy;
o Understanding which capabilities that are needed to succeed and attaining these
capabilities.
5.1.5 Interviewee 5, Backend Developer The interviewee works as a backend developer, implementing the data infrastructure needed
for enabling AI. The interviewee compares AI to traditional programming and says that
traditional programming is deterministic, you know what will happen, AI is more dynamic. In
general, the interviewee sees AI as a tool to solve complex problems with the help of statistics
and large amounts of historical data.
The main challenges the interviewee identifies with AI implementations are all connected to
the data used in the algorithms. Having sampling times on the sensors that are short enough to
be able to catch what is going on is a prerequisite for capturing any sort of value with AI. Having
too long sampling frequency will miss the shorter trends, the interviewee adds. Another
challenge to consider is the data transfer, which is linked to the previous challenge, having lots
of data and short sampling times will require a well-working data transfer system. The
interviewee adds that keeping the data transfer times short is crucial, especially if the data is
used for real-time control.
During the discussion about critical factors for succeeding, the interviewee again mentions data
and data quality. The data that is being used to learn from needs to have high quality, having
bad data input will give a bad output, the interviewee says. Another factor the interviewee
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mentions is the need for experimenting with the AI algorithms. There are lots of different
technologies available on the market and to find the optimal algorithm structure, testing is
required. During the testing, the AI developers need to work closely with the process experts.
There will be lots of synergies getting process input and not only focusing on data crunching
when developing the algorithms.
5.1.6 Interviewee 6, Lead Engineer and Technical Support The interviewee works as a lead engineer in the research and development division. The
interviewee supports with process knowledge in the development of AI and sees AI as a
machine that could find patterns and information from data which is not possible by humans.
The interviewee adds that AI can find patterns without having advice where or what to look for,
which the interviewee sees as a strength.
The interviewee sees the industry where they are operating as conservative towards
digitalization projects such as AI and states that this puts more pressure on us. The interviewee
says that we need to provide guidance, be convincing, and work closely with the customers to
both develop the right models, but also develop them in a customer-friendly way. The
interviewee adds that customers feel unsafe because they do not understand how it works,
therefore our front-end employees, such as service technicians and salespeople, need to be able
to learn and guide the customers to mitigate this feeling.
A challenge the interviewee sees with AI implementations within product offering is data
ownership. The interviewee says that if a customer is willing to share their data, they want
something back. Customers see a risk in sharing their process data and we need to provide value
worth more than the increased risk. However, to be able to train the AI models and provide
customer value, the interviewee identifies a need for customer data. To get past this, the
interviewee advocates a close collaboration with a few pilot customers and experimenting with
these to find a minimum viable product to be launched to a broader customer group.
The most critical factor for implementing AI is according to the interviewee having the right
people and competencies for the work. The interviewee says that both data scientists specialized
in AI are needed, but likewise people with project knowledge in digital transformation. The
interviewee also says that AI is still a new area, and AI projects will need time to develop.
35
Letting the projects take time and not having too high expectations in the short-term are
important, AI models will get better and better with training, the interviewee says. Lastly, the
interviewee says that having a close collaboration with the customers to identify what a
minimum viable product the customer is willing to accept is important.
5.1.7 Interviewee 7, Business Development Manager, Automation The interviewee works with business development within automation. In the later years, the
interviewee has had a more strategic role, but have previously been working with more hands-
on automation. The interviewee sees AI as a moving technology and says that what we saw as
AI a few years ago is today seen as common technology without any sort of intelligence. The
interviewee adds that AI today is focusing on predicting and finding patterns with the help of
data.
The interviewee mentions a problem with a particular AI model, predictive maintenance and
failure prediction for machinery products. The products may be exactly the same when sold,
but after a few service inspections and a few changed spare parts there might be a large degree
of uniqueness, the interviewee adds. The interviewee questions how scalable the data really is
in these cases, and if the data used to train the AI model on one product is applicable to another.
With this said, the interviewee states that the biggest risk for companies approaching AI is
doing nothing. In today’s global business climate, the rivalry is high, and companies need to
take risks to stay on top. Even though doing nothing could be costly for companies, another risk
the interviewee mentions is the cost of failing. The interviewee mentions that in business-to-
business markets, a failure could be very costly both in terms of money and in terms of
reputation. Compared to business-to-customer companies, business-to-business companies
have a more concentrated customer base, which means each customer is worth more, the
interviewee adds.
When talking about the most critical factors for succeeding with AI, the interviewee mentions
having the right people and competence as the most important factor. This is a challenge at the
moment since data scientists are wanted. The interviewee also states that having a management
team with the courage to invest in new technologies and that supports the projects is a must. To
get needed resources for succeeding with AI you need to have support from the management
team.
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5.1.8 Interviewee 8, R&D Director The interviewee works as head of the research and development department. AI is a tool that
can be used to find patterns in data, which is not possible for humans to detect, the interviewee
says. The interviewee adds that the difference between a regular control system and AI is
sometimes a bit unclear, and what was seen as AI a couple of years ago is not seen as AI today.
The interviewee sees AI as a way to enhance the products with deeper insights by combining
the current product expertise with data analytics. A possibility the interviewee mentions with
AI is gaining knowledge about the products and therefore be able to offer even better service
to customers. By analyzing datasets about the products in-depth, there will for sure be things
we will learn. A challenge the interviewee mentions with AI is attracting the right
competencies. The interviewee says that tech companies are more attractive compared to
traditional industry companies in terms of hiring AI competence. The interviewee says that the
solution to this problem might be to look for other companies to partner with in terms of AI
competences. According to the interviewee, this partnership could range from buying a whole
package to only buying a small portion of the solution and develop the rest internally. In terms
of keeping and gaining as much knowledge internally, only buying a small part of the solution
externally is advocated.
With AI project there is a higher uncertainty of the outcome compared to regular projects,
according to the interviewee. This requires clear communication about expectations and risks
with the technology. Another critical factor the interviewee mention is having a structured plan
for how to approach AI projects as well as developing it step-by-step, working methodically.
5.1.9 Interviewee 9, Automation Engineer The interviewee works as an automation engineer with continuous automation improvements
and with the development of new automation platforms. The interviewee says that there is an
increased demand for having automation platforms that support functions such as data modeling
and AI.
During the discussion about expectations, the interviewee identifies a learning curve that needs
to be accomplished before value could be obtained. The interviewee says that AI is strongly
dependent on data to improve, and that data is generated only while the product is up running.
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Therefore, AI projects need to be given a generous timeline before it has to deliver business
value.
A risk the interviewee identifies is not having a clear scope of where the AI projects are aiming
and therefore not being able to detect if the organization has the required resources. An
extensive evaluation of existing data sources and whether these data sources are enough for the
objectives are essential according to the interviewee. The interviewee also talks about having a
plan to get access to other necessary data sources as important. If a data source such as a
machinery sensor is missing, a plan of how to build and integrate this sensor into the product
must be established, the interviewee says. Another challenge with the data the interviewee
mentions is having data with high quality. Organizations believe that having large amounts of
data is all that it takes to succeed, however, the data needs to have high quality and right format
as well, the interviewee adds.
The interviewee identifies three factors as especially critical for succeeding with AI projects.
Firstly, having a clear and synchronized plan where we are today and where we want to be in
the future with AI. Secondly, communicating and involving all parts that will be affected by
the upcoming changes within the organization. Thirdly, start the projects small and scale it up
slowly. Work closely with the customer and use the feedback to modify the products. Find a
solution that the customer would be willing to pay for, the interviewee adds.
5.1.10 Interviewee 10, Head of IT Security The interviewee works with IT and information security. AI is defined as a tool used to enhance
and automate repetitive tasks done by humans. AI can detect anomalies and abnormalities in
the ongoing operations, the interviewee says. The interviewee adds that AI is still early in its
maturity and needs time to be more applicable for companies and organizations.
The winning tactic approaching AI projects is according to the interviewee taking it step-by-
step, letting the project mature and not rushing into it. The interviewee again mentions that AI
is early in its development and that organizations will need time to adopt. The interviewee sees
a challenge with data ownership if AI is utilized in product offerings, who will be owning the
data, and which incentives do each part have to let the other part get access to it? The
interviewee states that one part of the solution is to develop trust and having a close relationship
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with the customers. The other part of the solution is to show the customer concrete evidence
that they will gain value with the AI implementation. The interviewee also mentions a risk with
the technical environment used for AI and that there are risks with these especially if some parts
are outsourced. How do we keep the data secure and how do we know which platform to use?
In the discussion about success factors, the interviewee mentions three as particularly
important. Firstly, it is important to be able to measure achieved progress during the project,
highlighting progress will send signals and get stakeholders on-board on the projects. Secondly,
AI needs to be deployed where it can create value, whether internally or externally, investments
in AI needs to deliver value. Thirdly, the AI models should be scalable. Approaching the project
slowly and starting small is the desired strategy, but when the time is right it should be easy to
scale up. This requires a solid infrastructure that is supporting scalability, the interviewee says.
5.1.11 Interviewee 11, Director Finance & Business Control The interviewee works with finance and investment management. AI is described as a way to
use data to create smart processes and products. The interviewee sees AI as problematic at the
moment and states that AI technologies are still early in its development and need time to be
standardized. The interviewee interacts with AI through investment decisions regarding AI and
follow-up controls of the current AI projects and their progress.
During the discussion about challenges, the interviewee states that it is crucial that the progress
made with AI can be measured in some quantity. The interviewee adds that it does not
necessarily have to be in terms of monetary measurements in the beginning. Another challenge
the interviewee highlights is the importance of having contact with customers to find
opportunities where AI can create customer value, but also to get feedback on the current way
of working. The interviewee adds that by having early contact with customers during the
projects, the risk of developing something unwanted is limited.
To find which processes or products to optimize with AI, the interviewee suggests a value chain
analysis that covers the whole business process should be done. Breaking down the value chain
into smaller parts makes it easier to find AI opportunities, the interviewee says. The interviewee
also states that succeeding with AI is not only about the implementation, you also need to be
able to act on the decisions when it is up running. The interviewee says that if we, with the help
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of AI, would predict that a customer is going to need a product in 5 days, we need to make sure
that this product has reached the customer by that time.
The main risk the interviewee identifies is the uncertainty with the outcome of the projects.
Large amounts of money are invested in a relatively untested technology. It is of high
importance to get customer feedback to be sure that progress is made with the AI projects. The
earlier you get this proof of value, the better, the interviewee says. The most critical factor for
succeeding with AI projects is according to the interviewee to have a proven customer value in
the projects where AI is deployed. You have to make sure that the projects will create business
value for the customer.
5.1.12 Interviewee 12, Product Portfolio Manager The interviewee works with product portfolio optimizations through close contact with the
customers. AI is described as a tool that can predict future events and detect patterns in data not
possible by humans. The interviewee says that in today’s world it is easy to copy hardware and
that there is a need to find other areas for product development, which could be by the help of
AI. The interviewee's interaction with AI comes through finding opportunities where AI can be
deployed to create customer value, this is done from a more strategic level. The work includes
customer collaborations but also benchmarking to see what the competitors are doing and how
they are deploying AI.
The interviewee sees AI as a way to find new business models, going from a transactional
customer relationship to a broader relationship covering service, support, and other possible
customer requests. The interviewee argues that with the help of AI they will be able to move
the expertise closer to the customer and cut costs while also achieving better customer service.
The interviewee states that in the short-term, the main focus is not to deliver monetary value
with the AI projects. It is more important to make sure that what is developed is something that
potentially will deliver value for the customer.
A challenge the interviewee identifies is the new capabilities needed to succeed with AI.
Lacking the required AI capabilities could potentially lead to unsuccessful implementations and
bad customer experience. To avoid this, the interviewee mentions two options for attaining
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these competencies, internal recruitments and support from technology partners. In the case of
outsourcing some parts, benchmarking is required to make sure the right partner is selected.
A risk the interviewee mentions is the danger of not working close enough with the customers
and therefore not being able to find value-creating activities. Collaborating with the customers
in the development is crucial, getting feedback helps us improve the models to fit the customer
needs. The interviewee also talks about the importance of looking at the whole process and not
isolate AI models by itself. The interviewee says that AI will be an enabler, but that AI has to
be integrated into their daily work before the real value can be gained with the technology.
The interviewee sees the following factors as the most important ones for succeeding with AI
projects:
I. Establish a close collaboration with the customer to find what needs they have and to
make sure there is a demand for what is being developed;
II. Having the right competencies to develop the technology the customers are asking for;
III. Getting the whole organization on-board with the changes that will happen with AI,
especially the front-end personal with customer contact;
IV. Be able to measure the progress of the projects in monetary value.
5.1.13 Interviewee 13, Product Manager Service & Performance The interviewee works as a product manager with close collaboration with the customers. AI is
defined as a method to systematically analyze and replace what humans are doing. The
interviewee sees AI as a way to find patterns and predict events faster than possible for humans.
The interviewee interacts with AI through existing AI projects with customer input and possible
new functions to develop for creating customer value. For the short-term, the interviewee’s
expectations about what AI will deliver are low. There is a learning curve at the beginning of
AI projects that needs to be passed before any real value could be obtained from the models.
The interviewee sees the large initial investments which often are needed to build the right
infrastructure for enabling AI as a risk. Companies might struggle to put large amounts of
money in without generating any earnings. Another risk the interviewee identifies is not
working close enough with the customer during the development. According to the interviewee,
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having close contact with the customers, collecting feedback, and modifying the products to fit
the customer needs, are of high importance.
When talking about the most important factors needed for succeeding with AI projects, the
interviewee mentions user-centric development, but also the importance of AI models always
being correct. The interviewee adds that an incorrect decision taken by AI will be hard to
forgive and mentions that if an AI model is not a hundred percent sure, it should say so. The
interviewee adds that they are operating in a conservative industry which puts even more
pressure on AI decisions to be correct.
5.1.14 Interviewee 14, Product Manager The interviewee works as a product manager, managing both hardware products and service
offerings. During the initial discussion about AI, the interviewee mentions that machine
learning is the part of AI which has the most potential right now. With machine learning, high
accuracy predictions can be performed to find patterns from historical data.
The interviewee sees enormous potential in AI for businesses in general but says that data
transfer might be a problematic area. If the computations are made in the cloud there is a need
to transfer lots of data in a short amount of time, especially if the data is used for real-time
control, the interviewee says. The interviewee sees a potential solution in edge computing but
states that this technology needs to mature to be more commercial for businesses.
The interviewee sees a risk in developing AI as an external project in organizations, not
developed within any of the regular business functions. According to the interviewee, AI
projects should be integrated into the regular business and involve as many as possible of the
stakeholders. If not, there is a risk of missing important aspects, and problems might arise when
integrating it in the later stages. Another risk the interviewee sees is to not have customer
interaction during the AI development. By continuously getting feedback from customers you
are making sure that what is being developed is what the customers are requesting. The
interviewee says that developing AI models because it is technically possible is the wrong way
to go, you need to have someone who requests what is being developed.
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During the discussion about critical factors to consider during AI implementation projects, the
interviewee does not see AI projects as much different from regular projects. Having a common
goal, the right resources and competence, and a time plan is always going to be needed. The
interviewee adds that a difference for AI projects is the need for finding the right one to partner
with because it is rarely the optimal way to develop all AI capabilities internally.
5.1.15 Interviewee 15, Strategy and Transformation Manager The interviewee works with business transformation on a strategic level. The interviewee
defines AI as a way to emulate human thinking and with the usage of historical information
predict future outcomes. The interviewee’s interaction with AI comes through current and
potential projects, where the interviewees participate on a strategical level to find use-cases for
AI.
The interviewee mentions that a fail-fast culture, where the organization learns from past
failures to improve the existing and upcoming projects, is desired. To find AI opportunities, the
interviewee states the importance of looking at the whole value chain and break it down into
smaller parts. By doing so, which features that affect the output, and how the output can be
optimized with AI is understood. Performing an inventory of current data sources is also a
method for finding opportunities for AI. The interviewee argues that finding deployment areas
for AI should be executed by the people who know the processes and products and not
necessarily by hiring more data scientists. Lastly, the interviewee also mentions the option of
hiring external help for finding use-cases for AI or for education in what to look for to find
these AI use-cases.
The interviewee says that a challenge with the implementation of AI is the organizational
culture. The organizational culture in their particular company is not necessarily more difficult
to change than other companies, but instead, the interviewee sees the whole industry where the
company operates as conservative towards digitalization. The interviewee mentions that both
the employees and the customers using the products need to develop an acceptance towards
decisions and suggestions made with the help of AI. There might arise problems where the
decisions from the existing product experts will rule out the suggestions coming from AI. To
handle this, there is a need for learning and communication about AI to get an understanding
of how it develops the answers. It might also be necessary to give AI some limitations of how
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it generates the solutions. The interviewee means that by limiting the freedom of AI, the
answers generated might be easier to understand and therefore more acceptable.
The single most important factor for succeeding with AI projects is, according to the
interviewee, to make sure there is business logic behind the projects. The AI projects need to
be grounded in the business strategy and have a concrete value proposition, the interviewee
says. To reach there, the interviewee again states the importance of looking at the whole value
chain to find business opportunities where AI models can create value.
5.1.16 Interviewee 16, Information Architect The interviewee works as an information architect on an overall level, supporting several
divisions with the right framework and information governance to succeed with their digital
projects and strategies. The interviewee describes AI as a way for machines and computers to
learn and e.g. predict the future by the usage of data. The interviewee says that AI could be
used to find patterns and trends in data to gain insights about product improvements.
A risk the interviewee mentions with AI projects is not having the right capabilities to succeed.
AI is an emerging business area and it is hard to attain expertise in the area at the moment.
Another challenge the interviewee mentions is getting the whole organization on-board with
the changes that will occur with the introduction of AI to the workplace. The interviewee adds
that the management team needs to take the lead and guide the organization forward.
When talking about the most critical factors for succeeding with AI projects, the interviewee
mentions that having support from the management team as the most critical. To get the
necessary resources to succeed you need leadership support, the interviewee adds. The
interviewee also mentions having project management skills and knowledge about how to
implement AI as well as having the required resources and talent to do it as critical factors.
Lastly, letting the AI projects mature and not rushing into it is also identified as a factor to
succeed by the interviewee. Different stakeholders need different amounts of time to get on-
board with the upcoming modifications that will occur to their work environment, the
interviewee says.
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6 Developing a Final AI Framework In this section, the final AI framework will be presented. The chosen parameters for the
framework will be reasoned for why they were selected.
6.1 Final Framework Design The final AI framework is created with the ideas from the initial framework, but with
improvements from the interviewees. The final framework includes an idea generation stage
plus three phases that represent the progression level of the AI implementation projects. The
three phases of AI implementation projects are understanding phase, developing phase, and
launch & scale phase. The reason for having the framework divided into phases is mainly due
to two reasons. Firstly, considering aspects of the later phases at the beginning of the
implementation process will complicate the work. For example, aspects in the launch & scale
phase affect how to launch the project, considering these aspects in the earlier phases would
most likely delay the work. Secondly, several aspects of the later phases are based on aspects
in earlier phases.
The order in which the factors are shown in each phase is not strictly defined. The current
ordering of the factors is what the author seems to believe is the logical ordering. However,
organizations applying the framework might feel differently. In addition, it might be needed to
iterate between some factors before having them fulfilled.
To transfer between phases, each of the aspects in the current phase should be fulfilled.
Quantitative measurement of fulfillment will not be established. Instead, each organization that
is utilizing the framework should judge by themselves if they met all the criteria for the current
phase or not.
6.2 Final AI Framework The final framework contains an idea generation stage followed by three maturity phases which
in turn contains five aspects to consider during each phase. The full final AI framework is
presented below followed by an explanation for each aspect to consider.
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Figure 6 The final AI Framework developed with input from the literature review and the interviews.
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6.2.1 AI Framework Idea Generation The first part of implementing AI is to understand what to implement, therefore, ideas are
needed. The goal of the idea generation stage is to generate ideas of which product to implement
AI in, and what type of AI function that could be of value. To find ideas for AI, several methods
can be used. The following list gives suggestions on how to find ideas for AI implementation
in product offerings.
• Systematically work through the existing product offerings to find opportunities for AI
(Interviewee 1);
• Breakdown the customer value chain into smaller parts and investigate how each part
can benefit from AI (Gerbert et al., (2017), Interviewee 11 & 14);
• Investigating the existing data sources (Interviewee 14);
• Benchmark how competitors are deploying AI in their products (Interviewee 12);
• Observing how the products are used by the customer (Furr and Dyer, 2014);
• Get help from external AI experts for consultancy (Interviewee 14).
Before moving to the understanding phase, an idea of where AI can be deployed, and what type
of function the AI model should contain needs to be known.
6.2.2 AI Framework Understanding Phase After generating ideas for AI implementation, the first phase of the AI implementation process
is the understanding phase. This phase aims to assess the generated ideas and compare these to
where the company is right now, and what it is aiming for. This phase should not be associated
with any larger investments, instead, further insight into whether to deploy the ideas or not
should be attained. The following aspects should be considered during the understanding phase.
6.2.2.1 Management Team Support
To capture the value that AI can provide, there is a need for the right resources in terms of both
competences and cash. In most cases, to reach these resources the way is through the
management team. Having a management team supporting the AI projects is therefore crucial.
The management team also plays a big part in advocating and communicating about the AI
initiatives within the organization. The management team needs to promote how AI will help
solve business problems for the rest of the organization. Before moving to the next phase, the
management team needs to be involved and support the initiative. Both interviewees and studies
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from the literature review argue that support from the management team is a critical factor to
succeed, see for example Brock and von Wangenheim (2019).
6.2.2.2 AI Implementation Plan
Establishing a plan for how AI should be implemented is an activity that provides both guidance
and communication internally. The plan should show what needs to be accomplished, by who
and when this should be done. The AI plan should be in sync with the long-term strategy the
company is targeting to ensure that the maximum value is extracted from the AI initiatives.
Several interviewees mentioned that having a clear plan where the company is heading is
crucial, also having a plan or roadmap is advocated by several of the studies from the literature
review, see for example Burgess (2018), Mohanty and Vyas (2018).
6.2.2.3 Competence Inventory
To succeed with the company’s AI initiatives there is a need for having the right competences.
In this phase, an inventory of the company’s existing competencies should be performed and
compared with the required competences for succeeding with the AI initiative. To develop the
technical aspects of AI, capabilities in data science and programming is required. Also, people
with technical project management skills will be needed for keeping everything together.
However, technical competencies are only a portion of all competencies needed for succeeding
with AI, other affected business functions and the people working there need to be prepared for
doing their part as well. Before moving to the next stage, the company should have attained the
competence internally, or more likely, established partnerships with external companies for
getting access to the required technical AI competences. Having the right competence is seen
as a key factor from almost all interviewees and studies, see for example Mohanty and Vyas
(2018), Burgess (2018).
6.2.2.4 Data Inventory
AI is driven by data and to capture value with AI, the data used is fundamental. Therefore,
before taking on a project with AI, an inventory over the available data should be performed.
First of all, assuring that the data is accessible is fundamental, sometimes companies believe
they have access to customer data which they, in fact, do not have. The data inventory should
be done with the project scope in mind, the data needs to be sufficient for its purpose.
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During the data inventory, questions like sampling times and completeness of data should be
answered. Other data considerations around the initiative such as the amount of available data
and potential data transfer times should also be analyzed. Also, how the data is stored internally
needs to be understood, having too fragmentized data could be a problem. Besides, a primary
analysis of the data should be done to see if correlations exist in the data worth analyzing
further. Before moving to the next phase, the company should be assured that the available data
and data quality is adequate for the purpose of the project. All literature studies and several of
the interviewees’ sees data quality aspects as critical factors for succeeding, see for example
Mohanty and Vyas (2018), Burgess (2018), Brock and von Wangenheim (2019).
6.2.2.5 Customers Confirmation of Value
The essence of any sort of product development is to develop a more valuable product for the
user. With AI solutions for enhancing product offerings, there is no difference. To find which
types of AI models that create customer value require customer input. To help the customers
get an understanding of what the product will look like, showing the model in a test environment
could be a method to get more concrete feedback. Iterations between customers and small
adjustments with the product idea might be needed before the customers are really onboard
with the concept. When the customers see concrete value gains with the idea, which they are
willing to pay for, it is time to take the idea into the development phase. Most of the
interviewees mention the importance of making sure what is developed is providing value for
the user, this is also true for all of the literature studies, see for example Burgess (2018), Gerbert
et al. (2017), Brock and von Wangenheim (2019). Also, in the innovation method created by
Furr and Dyer (2014), engaging with the customers for confirmation of value is advocated.
6.2.3 AI Framework Developing Phase The development phase is the second phase of the AI implementation project. During this
phase, the company will develop the foundation surrounding AI product development. This
means activities such as setting up the right architecture for data exchange, productizing the
concepts, and getting more stakeholders on-board. This phase is generally associated with
notable investments, therefore, fulfilling all requirements in the earlier phase is essential before
starting to develop.
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6.2.3.1 Data Infrastructure
Having data sources that satisfy the purpose of the AI initiative, the next step is to develop a
working infrastructure for data transfer and algorithm processing. Companies have to choose
which type of architecture to deploy and where data processing should occur. Several cloud
solutions are available for data processing, but other alternatives exist as well. Companies also
have to consider which type of platform that should be used to deliver the AI models to the
customers. AI models could, for example, be installed into the automation system or accessed
through a computer or smartphone application.
Other questions such as cybersecurity and questions affecting GDPR also need to be addressed
when setting up the infrastructure. There are several aspects to consider with the security of the
data, but a deeper analysis will not be performed in this study. Lastly, the installed infrastructure
needs to fulfill the requirements for scaling up. In this phase, only a handful of pilot customers
are used to test the models, but for the next phase the AI models should be launched for all
customers. Establishing a working data infrastructure is a prerequisite for being able to deliver
the AI models to the customers.
6.2.3.2 Experimenting with the Algorithms
Knowing that the data contains some sort of useful correlations, the next step is to dig deeper
into the data to find the optimal AI models. The AI algorithms used for processing the data
could be created in tons of different ways. Often, machine learning is used to find patterns in
data and predict trends. However, which type of statistical model that should be deployed for
achieving the best results is not that easy to understand beforehand. Splitting the data into
training and testing datasets are required to test different models against each other to find the
optimal one for the purpose. Depending on the data source, it might be required to clean the
data before any processing can be done. Experimenting to find the optimal algorithms is
advocated by some interviewees and studies, see for example Mohanty and Vyas (2018).
6.2.3.3 Customer Participation in the Development
From the understanding phase, customers should know about the AI models to be developed
and also have confirmed the need for them. However, to make sure that the right models are
being developed in a customer-friendly way, continuous collaboration needs to be established
with the customers. Iterating the AI model and design development with customer input will
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make sure that not only the right function is developed, but also having it designed in a way
desired by the customers. Whether a collaboration with only a few pilot customers should be
carried out or having a larger number of customers involved depends on the variety in the
customer base. Companies need to understand that customers might have different needs and
satisfying one customer does not mean all will be pleased. Working closely with the customers
in the development of AI is advocated by most interviewees and literature studies, see for
example Mohanty and Vyas (2018), Gerbert et al. (2017). Also, in the innovation method
described by Furr and Dyer (2014), working closely with customers is a key aspect to consider.
6.2.3.4 Productizing AI
Before launching the AI products, questions regarding pricing models and launching strategies
needs to be understood. Launching the AI model too early might damage the functionality,
however, since the models tend to get better with data, and data is generated when used, waiting
too long with the launch might also be devastating. A strategy might be to launch the product
stepwise, possibly a geographical region at the time or other customer segment groups at the
time.
Another aspect to consider is how to make money on the product in practice and how it should
be priced. Since the data commonly is scalable between customers, the more customers on-
board with the product, the better the result will be for all users. For this reason, finding a price
for the AI models that is suitable for the customers, but still are bringing profit for the producing
company is both important and challenging.
6.2.3.5 Front-End Support
In the understanding phase, support from the management team is a requirement. In the
development phase, the front-end people should also be engaged and supporting the AI projects.
Front-end employees are referring to all employees with customer contacts, such as service
technicians and salesmen. During the development phase, more customers are getting involved
which will require more customer communication and guidance. Depending on how digitalized
the operating industry is, customers might be more or less conservative towards digital changes.
Needless to say, the front-end people need to be confident and informative, making the
customers feel both safe and satisfied with what is being developed.
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A drawback with AI models is that it is not always clear how the solutions are generated, to
mitigate the unsureness there is a need for skilled staff that can convince customers and prove
the value gain with the AI models. Several of the interviewees states the importance of having
the employees with customer contact educated in AI and to support the AI projects.
6.2.4 AI Framework Launch & Scale Phase The launch & scale phase is the last phase of the development project. During this phase, the
models will be launched and scaled up. Besides the launch, new internal working methods and
product teams should be established. The implementing company will change from
implementing AI to managing AI with continuous work.
6.2.4.1 New AI Model Standardization
For most organizations, the implementation of AI is done with some AI models in mind.
However, having the whole technical infrastructure set up, now is the time for innovation and
the discovery of new AI models. To get there, organizations need to establish working
principles for how this should be done. Since customers are the ones with the most valued input,
keeping a continuous communication with these are important. To be able to consider all the
practicalities with the establishment of new models, teams containing AI developers, process
experts, and customer experts should be established. Some interviewees mentioned that
establishing a standardization in model development is important for succeeding.
6.2.4.2 Customer Feedback
Having customers involved in the understanding phase and development phase is important to
identify useful products and to develop these in the desired way. Furthermore, when the
implementation is about to finish and the work is more about managing the AI models, customer
involvement and feedback is still important. Improvements in the current models and the
creation of new models will take place, keeping a continuous collaboration with the customers
assures input and feedback on the work. Besides, when customers get a deeper understanding
of what data can do, they will be more demanding about the models. Establishing a partnership-
relation with the customers will help in the creation of new models as well as mitigating the
risks of losing customers to competitors. Continuous collaborations with the customers are
advocated by some interviewees.
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6.2.4.3 AI Model Follow-Up
The developed AI models will be functional at the implementation, but since the models will
learn and update from data, there are risks that the models will drift away from their optimal
state. Also, data entering the models might contain errors due to broken sensors or other faults
which will affect the learning part of the models. To avoid this, continuous follow-up work on
the models is needed to make sure they are working as they should. Besides technical follow-
up on the AI models, follow-up on the progress made with the AI models is also important.
Establishing key performing indicators to monitor the AI models and what value they are
bringing are important. If external partners are handling some part of the AI models, KPIs to
monitor these partners might be needed as well. Some interviewees mentioned that follow-ups
on the AI models are important to keep them functional. Establishing KPIs to measure progress
is mentioned by several interviewees as important.
6.2.4.4 New Product Teams
With the implementation of AI in the product offering, new product teams and product owners
taking care of concerns regarding the AI models should be established. The platform which the
AI is delivered through also needs to be considered and supported, providing AI models is more
than just the models themselves. These teams should offer customers service and education in
AI models, but also working with enhancements for the AI models and continuous
development. Continuous development of the models will require a broad knowledge base,
experts with domain knowledge are needed as well as customer input and AI developers. Other
functions affected by the integration of AI into the business should also be a part of these teams.
Going from implementing AI to managing AI will still require data expertise and coding skills,
bug fixes and model improvements will highly depend on these capabilities. Clear
responsibilities in the product teams need to be established to make sure that each individual
knows their duties. Establishing new teams and working methods after a product launch is
advocated by some interviewees and studies, see for example Furr and Dyer (2014).
6.2.4.5 Organizational Support and AI Culture
Implementing the technical requirements for AI is the first part of integrating AI into the
business. However, to create business value with AI, organizations need to be able to act on AI
which requires the development of an organizational AI culture. Organizations should see AI
as a tool to collaborate with to gain deeper insights. If an AI model predicts that a customer will
need a spare part in 7 days, the delivering company needs to make sure that the customer has
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the spare part at that time. To get there, business functions need to work closely together with
the AI and system developers. There is a need for a culture where continuous development
between programmers and other business functions are established, instead of seeing it as
business functions ordering a feature from the developers. This requires new working methods
where digital development is closely connected to the regular business. Digital development is
affecting all business functions and organizations need to start to work with and plan for digital
developments. Establishing an AI culture is mentioned by several interviewees as important to
consider when implementing AI. Some studies also mention this, see for example Mohanty and
Vyas (2018).
54
7 Discussion and Conclusions This section provides a discussion of the main result of the thesis. Critics about the study will
be presented followed by suggestions for further studies in the area.
7.1 Findings of the Study The purpose of this study was to investigate how organizations can implement AI in their
product offerings in an optimal way. This was broken down to study which factors are important
to consider during the implementation of AI in a company’s product offerings and when these
factors are important to consider during the implementation process. This was solved by
creating an AI framework to guide companies through the AI implementation process. The
process of creating the framework consisted of a literature review covering AI implementation
and innovation methods as well as interviews with stakeholders working with the
implementation of AI at Company X.
Both sub-questions are answered with the created AI framework. The framework shows which
factors are important for organizations to consider during the implementation of AI in their
product offering. The phases in the framework show when each factor should be considered.
7.2 Critique The purpose of the framework is to act as a guide for companies with AI implementation in
their product offerings. This does not mean it will provide a strict answer regarding their success
chances, but to highlight important parameters the company should consider during the
implementation process. A flaw with the framework could be that it highlights factors to
consider, but do not provide as much practical guidance as desired for the implementing
organization how to handle these. However, rarely there is a general and optimal way how
companies should act, instead, it is often company-specific how to handle these parameters,
depending on their strategy and objectives.
Since the framework only is updated with input from the informants at Company X, the final
framework could be biased and provide an optimal way of working with AI from a Company
X perspective and not from a general perspective. To generate a generic framework for AI
implementations, input and tests on more than one organization are needed. Due to time
constraints, only one organization has been investigated in this study. Therefore, this case study
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should not be seen as confirmation of the validity of the final framework for AI
implementations. This is an important aspect and further testing of the AI framework on other
organizations is needed for confirming the validity of the framework.
7.3 Further Studies This study investigates critical factors for companies to consider during the implementation of
AI in their product offerings and presents a guide on how to do it. The presented framework is
developed through collaborations with Company X and employees as well as through a
literature review. Due to the fact that only interviews with employees from Company X where
conducted, there might be aspects that have been overlooked because of irrelevance to
Company X. Therefore, further studies should be performed with other organizations to test the
validity of the created framework.
The scope was to create a generic framework for AI in product offerings. This is a broad scope
and other studies could be performed which investigate some parts of the framework, or one of
the phases in the framework. Focusing on one aspect area or one of the phases during the
implementation process will likely produce a deeper analysis and better guidance in the specific
field. As mentioned before, a flaw with the framework might be the lack of practical guidance,
focusing on one specific field could potentially lead to more practical input for the
implementing organization in that specific field.
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Appendix
Interview Questions
1. What is your current profession here at Company X?
2. Describe/define your view of AI.
3. In which way do you have contact with AI in your profession?
4. From 1 to 10, how involved are you in AI and the digitalization here at Company X? The following questions should be considered on both the current AI projects and future AI projects.
1. What expectations do you have regarding AI and Company X?
2. What do you think AI should help Company X with? a. How should Company X succeed with that?
3. What challenges and risks do you identify regarding AI and Company X?
a. How to make sure these issues are getting solved?
4. Which strengths and weaknesses do you identify regarding AI and Company X?
5. Which factors do you think are the most important to succeed with AI implementation here at Company X?
a. When should these factors be considered during the implementation?
6. Other comments? .
7. Who else in your organization do you think I should discuss AI and Company X with?