I wrote my thesis on the topic of utilizing AI in B2B markets and for that purpose I conducted a preliminary literature review to first and foremost to find the research gap to define my research questions. As AI has been a hot topic for several years my interest was to understand, where and why AI is utilized, is it actually AI or machine-based learning and what are the benefits.

The theoretical concepts

The theoretical concepts, which ones describe the context of studies, can we identify the phenomenon of the study.

Artificial Intelligence (AI) has been studied in the public sector, while not a lot is known about, how technology can be deployed in creating added value to the companies in B2B €“sector. (Bhatia, 2018) For many companies in the enterprise context the application of AI can be intimidating without a clear knowledge, where to start. (Devvret, 2019) Artificial Intelligence, AI for short, has been defined by several scholars and academics throughout the years. The term was introduced by John McCarthy in 1956. The development of artificial intelligence today stimulates the solution of many information technology problems.

AI is often defined by different technologies that have more or less intelligent properties and most often it is equated with machine learning algorithms, which is already used in many applications today and there are direct features of intelligence. In general, AI system includes at least two parts, where an observation is being made after it is matched to a known knowledge based on the history it has. Artificial intelligence combines data with different algorithms and its definite advantage is that the collected data can be processed quickly (Davenport, Ronanki, 2018), while it can learn from experience to improve performance.

Artificial intelligence and machine learning can be defined in their core as fast computing using computers with almost unlimited capacity. The human brain is not capable of this kind of performance. In general, there are three types of AI are narrow, general and superintelligence. Talking about modern AI refers to narrow AI designed on specific task (e.g. Google Search), wheres general AI (artificial general intelligence AGI) is to be anticipated as the artificial intelligence, which has acquired the equivalent capabilities of the human intelligence, where it approached its environment through a holistic view and being able to make conclusions on its own on different sensors.

Machine learning, ML, is subset of artificial intelligence (Duffet, 2019) and Duffet (2019) introduces it to be known specifically as deep-learning. It is a tool helping to solve problems for example in robotics, speech and face recognition through training. It is one possible component of a system including artificial intelligence. ML has been defined by Valiant (1982) as the use of algorithms finding patterns in data.

Deep-learning involves the ability of machines to develop self-learning capabilities from amounts of data using neural networks with multiple layers of processing units. The word deep refers to, which type of architecture is used in the artificial neural network (ANN).

Algorithms are a set of instructions, which are typically used to solve or perform a task. The concept of machine learning is a system utilising data to learn, predict and draw conclusions. It builds mathematical models using the theory of statistics and leveraging samples to make inferences. (Alpaydin, 2020) Machine learning in its core is about deploying neural networks and statistics in finding insights of data.

Methods of learning

Commonly mentioned areas in machine learning are supervised learning and unsupervised learning in artificial neural networks. (Duffey, 2019) In the case of supervised learning, the results of the training examples and the problem through which the system is taught the difference are known between the results of different examples. The other types Duffey (2019) mentions are semi supervised learning, active learning and transfer learning.

Unsupervised learning is used to find the structure of data. The purpose is to group the data based on the patterns found or to find anomalies in the various data. This method uses patterns to classify people into groups based on which to sell their targeted ads. In the case of unsupervised learning, the training process of the algorithm does not require so much human intervention, as the system itself finds the parameters that should be taken into account.

Reinforcement learning is also an area of machine learning, where machine learning models are being trained to make a sequence of decisions on goal-orientation basis, which is to be achieved through trial and error -method. The difference between this model of learning to unsupervised and supervised learning is, how the input of data in being interpreted.

Business Processes consist of mechanisms, procedures and flow of activities by which the service/ product is acquired and delivered. For business processes the Michael Porter’s Value Chain model has categorised processes as core processes and support processes. the core processes of a company have identified as processes, which create value of a company. Value creating factors are marketing and sales, logistics, operations and services. (Dumas et al. 2015, 35) Marketing is one core process enabling your product to be know to customer by creating, keeping and satisfying it. Kumar & Reinartz (2016, 36) explain that in marketing one of the most important things is to develop and improve communication with customers in order to give them full satisfaction, loyalty and profitability. They also explain that customer perceived value should not be confused with other things, such as quality, perceived benefits and satisfaction. The processes, which support the core processes are support processes, which include HR, procurement, infrastructure for example. The management of business processes is vital to help to understand the possibilities, challenges and problems.

When describing added value the impact of the value in integrative relationships created upon the market offering is more direct than any other relationship (Kothandaram&Wilson 2001). According to Kothandaram & Wilson (2001) potential partners should be evaluated based on their operation risk and the potential value added to the partner. Added value can differ when discussing the concept in different contexts as in marketing added value can mean brand loyalty.

Preliminary literature review

The literature review of this article will examine the existing literature on the subject of the utilization of AI and the most related and relevant existing research, while the related scholars are being discussed mainly from the Universities of Standford and MIT. Aim is to create a foundation and an understanding, what type of artificial intelligence exists as a standalone and what is the definition of it in the purpose to this study by defining the key concepts linked to it. Many studies and articles state that artificial intelligence has enormous potential in the B2B-marketplace, yet studies show that companies are not able to use the AI-tool either due lack of capacity or knowledge in the various business processes. (Ransbotham, Kiron et al. 2017) Several books in literature focus on describing, what AI is, how it has formed and debating over the fact, will AI take over or not.

Artificial Intelligence can be broken down into two words, such as artificial and intelligence, which can be viewed separately to have a more in-depth analysis of the concept raising the questions about the nature of the mind and the limits of scientific arrogance. (Chirsley&Begeer, 2000) Moreover, AI along with machine learning might potentially change the workforce, change the way of internet marketing, the way data in being analysed and draw conclusions on the matter. (Bartoletti, Leslie et al. 2020) Some of the changes are already visible in the present, but there are many more to come according to several reports. The advantage of AI and ML compared to a human is its ability to process data faster and smarter with little mistakes if any. (Husain, 2017) Algorithms are a set of instructions, which are typically used to solve or perform a task. The concept of machine learning, which links to AI, is a system utilising data to learn, predict and draw conclusions. It builds mathematical models using the theory of statistics and leveraging samples to make inferences. (Alpaydin, 2016)

According to Linda Gottfredson (1997), ”intelligence is the ability, among other things, be able to reason, plan, solve problems, think conceptually, understand complex ideas, learn quickly, and learn from experience.” Artificial intelligence can contain everything that Linda Gottfredson has defined, but artificial intelligence is not thoughtful, and in its implementation one has to plan what the artificial intelligence enters and what predefined problem it solves. (Russell and Norvig, 2020) Humankind has consciousness, what machines cannot possess along with emotion, creativity, humour and also being aware of one €TMs thoughts is consciousness. (Chace, 2018) American computer linguist Noam Chomsky (2010) has stated person’s uniqueness on the ability to combine two concepts into a third one; meaning to perform an operation of combining concepts. (Chomsky, Bricmont et al. 2010) According to Patrick Henry Winston neither the mentioned qualities, nor the ability to reason or to combine concepts, is a sufficient distinguishing feature of the human intellect. (Abazorius, 2011)

In addition to the external, ordinary language, which has developed due to its social nature, man also has an internal language through which he manipulates stories and organises the systems of perception in order to solve problems and derive the necessary rules. (Husain, 2017) Thus, viewing artificial intelligence a concept of NLP, natural language processing, is seen as a difficult task for artificial intelligence as it is crucial to understand the context of the text. In addition, in many cases the text itself needs background information and it includes grammar; both of which can be seen as obstacles for AI to process the text as coherent as a human does. Application such as Siri, which has been designed by Apple, can be seen the closest solution to processing the text and replying, but in reality it lacks further understanding of the text. (Haikonen 2017) The algorithms, which need to be developed for AI should be able to solve tasks, which require human thinking, intelligence and creativity. (Siukonen& Neittaanmäki, 2019) Importantly, the merge operations give the person the ability to describe an event and man has developed the ability to combine two concepts from linear descriptions to lookup descriptions that represent events related to each other in time, causation, and other ways. To have the ability to move back and forth in the sequence of memorised events, to explain what happened and to predict what will happen next. The ability to edit stories developed into the ability to combine new, unfinished stories from familiar stories. (Husain, 2017)

When talking about artificial intelligence it lacks consensus as and adds confusion when the simple forms of machine learning (ML) has been cited as artificial intelligence. Approaching AI can be based on the assumption that human brain is the only conceivable object meaning the structure of the human brain should be mimicked to obtain artificial intelligence. In order to model the structure of the human brain there has to be the usage of artificial neural networks and genetic algorithms to achieve it. Patrick Henry Winston, a researcher at the Massachusetts Institute of Technology (MIT), can be considered a researcher following this approach in belief that human brain needs to be imitated in order to unleash the power of artificial intelligence. (O’Connor, 2019)

Both Charniak (1985) and Pearl (1985) have researched AI from the perspective, where artificial intelligence is combined with Bayesian theory. The key point in Bayesian theory is that the affairs of mankind do not proceed perfectly or systematically and it relies on the probability theory.. Artificial Intelligence as a whole has been defined by McDermott and Charniak (1985) to be the study of mental faculties by leveraging the computational models while Winston (1992) defines it as an instrument making it possible to perceive, reason and act with the help of computations.

Artificial intelligence has been defined by Nils J. Nilsson (2013) as an activity, which makes machines intelligent and intelligence is that quality enabling an entity to function accordingly and with foresight in its surroundings. (Nilsson, 2013) For an example video recommendation, which suggests videos based on your past behaviour is one form of AI, when the system has learnt from multiple users €TM behaviour.

Artificial intelligence can be identified through heuristic programming, which has been studied by James Slagle (Norvig, 2014). Lee (2018) divides AI research as learning of the neural network, which is a learning system and a rule-based approach. Neural networks need a lot of information and in this field Hinton Geoffrey has contributed significantly by introducing millions of shapes, symbols and images to layers of networks for learning. (Siukonen& Neittaanmäki, 2019)

Lee (2018) names two camps for the field: ones with an utopistic view fro AI and the ones with a dystopistic -view. Lee (2018) points Kurzweil Ray, author of several books about AI and also currently (2020) working for Google as director of engineering, into the utopia camp as Lee (2018) points Elon Mask, the founder of Tesla, to belong to dysopistic-camp. The supporters of mentioned utopistic view would have the outlook for machines as the superior to humans. (Lee, 2018) Moreover, Kurzweil calls for a rational approach to the development of information technology is necessary, meaning that management and control, as well as initiatives, must be kept in the hands of responsible and cooperative parties, such as universities, governments, research and industry. Similarly, Kurzweil and an AI researcher Hassabis Demis believe the deployment of AI could benefit the human to live longer and even in helping to tackle major issues, like global warming. (Lee, 2018)

The computational theory of intelligence has not yet been fully developed, although applications of artificial intelligence are ubiquitous. Artificial intelligence is an advanced entity in technology, whose applications and related practices are still evolving. Artificial intelligence exhibits intelligent behaviour, analyses the surrounding environment and, to some extent, makes independent decisions in order to achieve certain goals assigned to it. (Allen & West, 2018)

Chace (2019) believes that the future holds several interesting innovations in automotive industry it can be seen through development of self-driving cars and also in healthcare most common tasks in measuring patient’s vitals might be given tasks for AI. In addition, Kurzweil believes the age of AGI to be near, referring to artificial general intelligence, (Husain, 2017) which refers to a hypothetical computer program that would be able to perform intellectual tasks as well as a human or even better. (Hodson, 2019) As the opinions of AI differ among scholars and business influencers, several institutions have published studies and reports by including different scholars about future of work, which predict AI to reshape the nature of workflows as some tasks of certain processes can be automated, while it cannot entirely substitute all the tasks included. (Evans-Greenwood, Lewis et al. 2018) Unanimously, several authors see the emerge of AI to unleash new opportunities. Moreover, researchers do agree upon some of the benefits if AI, such as speed, accuracy and productivity.

In general AI is seen as a section of information technology and as part of data-science, it is based on functions in information technology. AI is used to describe intelligent systems and in general it can be defined as the study of the computations making it possible to perceive, reason and act. There are the common features of the various definitions stating that artificial intelligence is the concept of intelligence being used in information technologies. Some researchers of artificial intelligence claim that capabilities for learning, intelligence, memory and computation doesn’t necessarily need human. In its core artificial intelligence in general is the development and theory of computational systems enabling them to perform tasks without human intelligence. (Lawal, 2019)

There are different views and approaches in the literature and among researchers on artificial intelligence, its definitions and dimensions along with future developments, which makes it challenging to create an exhaustive definition that satisfies all parties.

In the field of artificial intelligence, technology giants Google and Facebook have been leading the way in applications of artificial intelligence (Siukonen&Neittaanmäki, 2019), using algorithms and developing their own libraries, such as TensorFlow and Keras. (Keras, 2020) Artificial intelligence is being used in e-mail software classifying certain emails as spam, categorising images in a person’s mobile devices and cloud services, automation in credit decisions and self-driving vehicles. Artificial Intelligence has enabled applications such as speech recognition and synthesis, machine translation from one language to another, image recognition, home robots, uncontrolled vehicles and even expert systems that advise specialists in certain fields. (Nilsson, 2013) Speech processing allows a computer to understand a given text and human speech. Speech-based software can use commands to perform tasks assigned to them, to communicate with people, and to turn a person-delivered speech into text that can be used, for example, to compile reports (doctors). Voice processing is used in voice-assisted virtual assistants, identity fraud prevention, and chat robots. (Husain, 2017) Artificial vision provides the ability to understand and process visual data in many forms (image, video, external surroundings). Artificial vision applications are used, for example, in sensors in self-propelled cars, in detecting people from images and videos, and in medical image diagnostics. (Duffey, 2019)

Artificial intelligence is strongly present in daily life by assisting in decision making, creation of applications and modelling of practical processes. One of the practical applications of artificial intelligence is image recognition as an example (Siukonen&Neittaanmäki, 2019) by separating numbers and letters from pictures, as happens in parking garages. When driving to the parking garage, the registration number of the car is associated with driver’s parking ticket enabling a person to exit the garage if the payment has been paid by an automatic opening of the boom. This is artificial intelligence in simpler, more common executions visible in daily activities. Another example of this that an individual consumer constantly comes across is chatbots. (Duffey, 2019)

Artificial intelligence can be utilised also in optimisation of data-driven processes in B2B -sector. (Bean & Davenport, 2018) In terms of this study the consumer-based solutions are being excluded unless there is a direct link or position in the B2B-industry. In addition to B2C -sector, chatbots can be seen also at service in B2B-industries. In many cases amount of data is being collected and stored, but not in all the companies it is not clear, whether all the collected data is being used and how. (Bean & Davenport, 2018) AI has already been used in companies in several business areas for optimising business processes, automating customer communication, product quality control, risk management and elsewhere. (Duffey, 2019) One of the most common applications is chat robots, which can interpret the text entered by the user and thus solve customer problems in dialogue with the user. The popularity of chat robots stems from the ability to make labor-intensive work processes, especially those related to customer service, more efficient by resolving some customer inquiries automatically. However, their prevalence is not yet widespread, due in part to the fact that this technology is still in its infancy and there are no easy-to-deploy solutions. On the other hand, there seems to be a lack of awareness of deployment opportunities. (Bartoletti, Leslie et al. 2020) Study conducted by McKinsey (2019) it was evidential in increasing deployment of AI among respondents. However, the firms are mostly in the early stages of adopting the technology. (McKinsey, 2019) There are innumerable business processes where artificial intelligence is or could be utilized. In all places with softwares, machine learning and artificial intelligence are part of everyday life and little by little artificial intelligence has some applications to some extent. AI has the potential to transform the future of humankind more than some other technology. (Duffey, 2019)

In B2B-market utilising data through AI can have enormous potential in workflows, reshaping processes, creating new ecosystems, managing of content and customers as for some examples. (Duffey, 2019) One of the benefits in deployment of AI is the possibility to handle large amounts of data at a vast speed as commonly cited benefits of AI is the increased performance through labor productivity when utilizing AI in the workflows.

The benefits for AI can be adapted through automation of basic activities and opportunities associated with AI are in automation what means that the jobs of people need to be transformed in order for the AI and humans to work in collaboration. For example in teaching grading is a repetitive task, which could be automated enabling teachers to focus more on interaction with students. By utilization of AI the work of 2 humans can be replaced as it can work fast, efficiently and around the clock. (McKinsey 2019) Most firms might be always looking for ways to have cost reductions and this is one of the reasons for firms implementing AI. However this does not mean that humans would be eliminated from the job, instead AI would be augmenting the humans and giving organizations more capacity to be innovative as repetitive tasks are being performed by AI (Salesforce 2018) along with possible economical benefits brought through labor cost (McKinsey 2019), which in some cases still may be unclear. Another benefit of AI can be found in the improvement of quality in service (Adobe 2018) as it provides a deeper understanding of the customer based on the captured data helping to meet the demands of the customer better than before. (Salesforce 2018)

From looking into the existing literature and the set of components studied, benefits of AI (value proposition, customer value proposition, value constellation, value capture, etc.) are emphasized in multiple studies. Brightedge survey (2018) points towards the obstacles for marketers in AI, where one third admitted to be confused over AI and what it really is. Microsoft’s report (2018) finds that half of the companies in UK have no strategy regarding AI while based on the McKinsey’s report (2017) companies should spur themselves to leverage AI on its early stage since later it can be a struggle to catch up with other companies.

Study (2019) by McKinsey&Company illustrates, how respondents of organisations currently leverage AI in their business functions and it shows the best adoption of AI in Telecom’s service operations. The same survey states that respondents consider the most significant value derived in manufacturing industry (over 50% of respondents). The organisations’ utilization of AI also enables to point out, where further improvements of the processes could be made.

Microsoft’s (2018) key findings of AI are based in improved performance with companies utilising AI perform 11.5% better on average in comparison to companies, which are not utilising the tool. In a survey (2018) conducted by Brightedge Research companies are seeing that AI would help to understand the customer better and thus have an input, when aiming to personalise the consumer experience. Based on the same study 27.39% of the respondents consider it to increase productivity and save time while only 8.07% saw increased ROI as a success story with AI.

McKinsey’s report ’Leveraging AI in business functions’ (2018) names a challenge for broader AI -deployment in technological limitations, such as acquiring large pools of data, having labeled data for training procedures, generalizing models and outcomes as well as explaining the results. Besides the challenges caused by technological limitations or adoption of techniques due to skillset or capabilities or a company law and ethics have a major role as respecting privacy matters can be a barrier of improving business processes.

Several studies also consider that there are gaps within the utilization of AI, when discussing it in the context of smaller companies as opposed to leading technology companies such as Google, Facebook and IBM. A potential threat in utilisation of AI is also widening the gaps between companies as there are still companies, who have not even partly digitalised their processes and are still handling a lot of forms, paper while in many cases also lacking an online presence. AI can be beneficial, but the positive outcomes might only have an impact for few.

Through research and evaluation of previous studies: existing literature on utilisation of artificial intelligence is not handling the topic in B2B markets to a great extent, but instead highlights the possibilities and mostly has been targeted into niche markets or B2C -markets. A strong consensus about artificial intelligence is not present among the top experts in the field and the most common applications of artificial intelligence seem to solve only a predefined, single problem given by a human.

Artificial intelligence is first and foremost a tool and by using the right tool a specific problem can be solved. The value for businesses can be harnessed from the abilities to adapt the techniques of AI. By incorporating AI, it provides the possibilities to stay ahead of the competition. However the data gathered by AI needs to be stored and processed, which has cost implications and professionals of the area see this as the most time-consuming part of creating the value through data. (Duffey, 2019)

The incapability to understand the tool can be derived from the skillset of the company and for some companies the business value within is difficult to grasp meaning the deployment of the tool by a company is not being strived for. On the other side, a report by Salesforce: ’State of the Connected Customer’ (2020) mentions AI to be perceived by the customers as an emerging technology and revolutionary to businesses of a different kind. As several companies are not utilising AI in the B2B-sector to its full potential and the utilisation is even smaller on this industry compared to the B2C -sector making B2B-industry the main subject of the study. (McKinsey&Company 2017)

Hence, due to mentioned facts the main research questions of this particular study are:

How are firms currently utilizing AI in their different business processes? What kind of value has the utilization of AI created for the firms? What factors facilitate/hinder the utilization of AI in different business processes?

By gaining insight on the deployment of AI in current business processes the research questions can be answered. Moreover, by studying current processes within the companies, both internal and external, the key enablers and barriers that drive or hinder the utilisation of artificial intelligence can be analysed and defined.


Abazorius, A., 2011. Unlocking The Key To Human Intelligence. Mit News, .

Adobe, 2018. Is There A Gap Between Ai Research And Ai Applications? .

Allen, R.J. And West, M.D., 2018. How Artificial Intelligence Is Transforming The World. Brookings.

Alpaydin, E., 2016. Machine Learning: The New AI. The Mit Press. Artificial, Intelligence, The Circular Economy, Artificial Intelligence And The Circular Economy.

Bhatia, R. (2018). Is There A Gap Between Ai Research And Ai Applications? [Online] Available At: Https://Www.Analyticsindiamag.Com/Is-There-A-Gap-Between-Ai-Research-And-Ai-Applications/ [Accessed 20 Sep. 2019].

Devvret, R. (2019). Bridging The Gap Between Research And Big Tech: Applied Ai/Ml Best Practices For The Modern Enterprise. [Online] Available At: Https://Medium.Com/Thelaunchpad/Bridging-The- Gap-Between-Research-And-Big-Tech-Applied-Ai-Ml-Best-Practices-For-The-Modern- D962428Beb14Â [Accessed 20 Sep. 2019].

Chace, C., 2016. The Economic Singularity. San Mateo: Three Cs Publishing.

Chace And Calum, 2018. Artificial Intelligence And The Two Singularities. 1 Edn. Milton: Chapman & Hall.

Charniak, E., 1985. Introduction To Artificial Intelligence.

Chomsky, N., Bricmont, J. And Franck, J., 2010. Chomsky Notebook. New York: Columbia University Press.

Chrisley, R. And Begeer, S., 2000. Artificial Intelligence. New York: Routledge.

Davenport, H.T. And Ronanki, R., 2018-Last Update, Artificial Intelligence For The Real World. Available: Https://Hbr.Org/2018/01/Artificial-Intelligence-For-The-Real-World.

Duffey, C., 2019. Superhuman Innovation. London: Koganpage.

Dumas, M., La Rosa, M., Mendling, J. And Reijers, H.A., 2018. Fundamentals Of Business Process Management. 2Nd Ed. 2018 Edn. Berlin, Heidelberg: Springer Berlin / Heidelberg.

Evans-Greenwood, P., Lewis, H. And Guszcza, J., 2018. Reconstructing Work: Automation, Artificial Intelligence, And The Essential Role Of Humans. The Atlantic, 321(1), Pp. 18.

Gottfredson, L.S., 1997. Mainstream Science On Intelligence: An Editorial With 52 Signatories, History, And Bibliography. Intelligence, 24(1), Pp. 13-23.

Hodson, H., 2019. Deepmind And Google: The Battle To Control Artificial Intelligence. The Economist.

Husain, A., 2017. The Sentient Machine. New York: Scribner.

Kothandaraman, P. And Wilson, D.T. (2001) The Future Of Competition Value-Creating Networks. Industrial Marketing Management, 30, 379-389.

Kumar, V. Reinartz W (2016) Creating Enduring Customer Value. Journal Of Marketing: Ama/Msi Special Issue 80, 36

Kurzweil, R., 2010. The Singularity Is Near: When Humans Transcend Biology. Duckworth Overlook.

Lawal, A.B., 2019. Artificial Intelligence Fundamentals: The Foundations & History Of Intelligent Machines. Bolakale Aremu.

Lee, K., 2018. Ai Superpowers. Boston: Houghton Mifflin Harcourt Publishing Company.

Li, K., 2018. Ai Superpowers. Boston ; New York: Houghton Mifflin Harcourt.

Mckinsey & Company, 2017. Smartening Up With Artificial Intelligence. Mckinsey Insights.

Michael Chui, Nicolaus Henke And Mehdi Miremadi, 2019. Most Of Ai’s Business Uses Will Be In Two Areas. Mckinsey Insights.

Nilsson, N.J., 2013. The Quest For Artificial Intelligence: A History Of Ideas And Achievements. Cambridge University Press.

Norvig, P., 2014-Last Update, Paradigms Of Artificial Intelligence Programming [Homepage Of Elsevier Science], [Online].

O’Connor, M.R., 2019. The Storytelling Computer.

Ransbotham S., Kiron D., Gerbert P., Reeves M. (2017) Reshaping Business With Artificial Intelligence: Closing The Gap Between Ambition And Action. Mit Sloan Management Review, 59(1),.

Russell, S.J. And Norvig, P., 2020. Artificial Intelligence. 4Th Edition Edn. Boston: Pearson.

Salesforce, 2018. Last Update, State Of The Connected Customer. Available: Https:// Www.Salesforce.Com/Content/Dam/Web/En_Us/Www/Documents/E-Books/State-Of-The-Connected- Customer-Report-Second-Edition2018.Pdf.

Siukonen, T. And Neittaanmäki, P., 2019. Mitä Tulisi Tietää Tekoälystä. Docendo Oy.
Tegmark, M., 2018. Life 3.0. Uk: Penguin Books.
Valiant, L., 1984. A Theory Of The Learnable. Communications Of The Acm, 27(11), Pp. 1134-1142.

Winston, P.H., 2011. The Strong Story Hypothesis And The Directed Perception Hypothesis. Association For The Advancement Of Artificial Intelligence.

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