Essay on the Impact of Artificial Intelligence on Innovation

Published: 2021/12/28
Number of words: 2718

1.0 Introduction

The artificial intelligence (AI) has changed the way industries operate. This alteration is evidenced by specific firms, such as Uber, Amazon, Airbnb, and Tesla, among others. Such companies have embraced AI utilization to apply innovative business models. However, there may be limited knowledge om how this technology impacts the business model innovation. Even though this technology is disruptive and renders other companies vulnerable to competitors implementing it, this research will then attempt to focus on the proactive impacts of technology in driving business model innovation. It is worth noting that global industries are being disrupted by the emergence of new technologies (Russell & Norvig, 2016). Well, Ai can be described as systems that are created by data utilization, analysis, and observations without requiring the programming process in order to perform these tasks. From this definition, it is explicit why every contemporary company tries to implement its use as a way of remaining relevant in the competitive industry. Furthermore, top executives of a company need to develop and maintain entrepreneurial skills, as well as innovative convictions using artificial intelligence while operating their organizations in order to remain competitive and succeed in running their businesses.

There is ongoing debate presently on how companies may preserve their position in a competitive market. Currently, the literature in this area focuses on external antecedents, which then is more likely to pressurize the firms to engage in the business model innovation. Well, this pressure can as well arise from technological disruptions, and therefore, it explains why most studies argue that the business innovation model is most likely to be influenced by the environment. Also, the companies may design their model based on emerging new technologies. Nonetheless, the researches assessing the direct effect of the emerging technologies are few, and even if they exist, their scope is commonly not exhaustive (Lu, Li, Chen, Kim, & Serikawa, 2018). So, more studies need to tackle this area so as to offer more multifaceted information elements of artificial intelligence technology to the model of business innovation. Notably, a comprehensive understanding of how firms, using AI, create value, as well as how they execute their activities, can aid in understanding the concepts of business model innovation, and its consequence. Explicitly, this research proposal considers that a business model as a set of independent activities, as well as active systems that span the boundaries of a company. Hence, this study, which will involve research on human resources, will further help to understand the concepts of business model innovation as initiated by artificial intelligence technology.

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2.0 Rationale

2.1 Research Question

What are the impacts of artificial intelligence on innovation? This question seeks to find out the influence AI has on the business model innovation. As an emerging technology, AI can be considered as a catalyst for business model innovation, and therefore, this research will offer more information based on factors shaping the model that is introduced by the merging technology. It is worth noting that enterprises that capitalize on the AI can fashion a disruptive innovation to other relevant companies through using their new models and process, thus enabling them to change the global competitive landscape theoretically.

2.2 Research Objectives

  • To find out the proactive impacts that AI has on driving the business model innovation
  • To investigate current issues that are being tackled in AI development and how they affect the business model innovation
  • To assess two case studies of two companies that are using AI in order to innovate their business model
  • To discuss how managers can create an AI-based culture in their companies
  • To find out the impacts AI has on the innovation in the companies

2.3 Hypothesis

The firms that capitalize on using artificial intelligence cannot generate a disruptive innovation via their new business models and practices, and this feature cannot result in a potential transformation of the global competitive setting. This null hypothesis creates an impression and ground for testing whether AI has an impact on innovation in the companies or not, and it examines the possibility of AI creating a competitive environment.

3.0 Literature Review

There has been improved research on artificial intelligence for the last two decades. It means that this study area has grown to be an essential area of focus. Presently, there is a need to study the theoretical framework and realistic effects of using AI comprehensively in the 21st century. Notably, this area has grown significantly in different fields, such as medicine, business, accounting, education engineering, and the stock market, among others (Li, Hou, Yu, Lu, & Yang, 2017). Apart from mentioning these fields of study, AI research has occurred, thus resulting in different individual areas of knowledge. Before tackling the impacts of Ai, it is realistic to study the challenge of this field of study. It is worth noting that artificial intelligence sprung as a result of the rise of information technology that has characterized both non-business and business firms (Aversa, Haefliger, & Reza, 2017). Therefore, the research in this field is encouraged by two main factors. Firstly, to provide the new people who are joining the Ai field an understanding of its structure and literature. So, this research then answers the question as to why one should study Ai literature. Secondly, the increased interest in artificial intelligence has enhanced an upsurge in need to invest in AI facilities.

AI is composed of machines. In the scope of computer science, the AI field is described as the study of intelligent agents. So, this terminology is used to refer to the simulation functions of the computer, which is human linked, such as problem-solving, as well as learning (Cockburn, Henderson, & Stern, 2018). In the past few years, the arrival of a large software utilizing AI has improved this field. Other subfields, such as machine learning, processing of the image, and mining of data, and processing of natural language have sprung to be an essential study area for technology enthusiasts (Sousa, & Rocha, 2019). In the Google Company, machine learning is actively used in the predictive search bar. The same merit applies to other firms, such as Netflix, where it is involved in the show suggestions, and Gmail Company uses it in filer options. The natural language processing is used by Google voice, and Apple’s Siri (Kepuska & Bohouta, 2018). Facebook also uses image processing capability where they use it for facial recognition tagging. Also, Google utilizes image processing in actualizing self-driving cars. Data mining is a broadly used terminology in the software industry because of the mass data quantity that is amassed daily. Large firms, such as Google and Facebook, collect statistics from users in order to interpret them so that they can use them to make organizational decisions.

Creating a robust AI for a firm is commonly accompanied by complications. In order to succeed in the AI implementation, industries must be able to know how the input relates to the output. Also, they must understand the subfields of AI, alongside the associated psychology of its use. Many problems entail human-machine interaction because it is typically complex when it is related to human interaction (Lauterbach & Bonim, 2016). Most communication happening between people cannot be coded facts any machine could easily recite. Humans interact in so many ways with each other that influences communication. Leading from this fact, factors that affect human to human interaction entails several factors, such as body language, voices innovation, slang, response to stimuli, feelings, and cultural facts, among others (Russell & Norvig, 2016). As such, it is difficult to model in a computer that does not have a basic common sense. Another complication is linked to fuzzy logic. It is modeled based on the human’s excellent ability to approximate activities without actual values. Computations, generally, needs numbers rather than concepts or words. When trying to imitate human intuitions, a complication often arises. Another problem is that image processing needs data in order to be consistent. Also, modeling the real world from mere photos of the internet is complicated because these photos vary. Overall, these complications are subject to the future improvement of AI systems.

The applications of AI have increased. It is used for various gaming applications. The video games are the most common example, which is familiar with people because it has been used for quite along time. The improved application of AI has enhanced its complexities and efficiencies, and as such, video game characters can now learn human behaviors, and even respond to stimuli, which then makes them to be able to react in specific ways that maybe not easy to predict (Soni, Sharma, Singh, & Kapoor, 2019). Other typical applications of AI include natural language processing, image processing, and vision system, virtual personal assistants, self-driving cars, fraud detection, security surveillance, handwriting recognition, and human-machine interaction. These applications explain why an industry that applies AI may disrupt a given business niche. The AI field of study offers the ability of machines to operate analytically while using concepts. AI will continue to play a significant role in different areas, including business.

4.0 Method and Design

4.1 Method

The research method chose for this study is the combination of archival study and case studies. The former will involve a search of existing literature on the topic, while the latter entails analysis of specific companies using artificial intelligence in their business. The result from this method will then be analyzed while following the objectives lead. As such, a conclusive inference can then be deduced in order to find a concrete answer on the main topic. This way, a phenomenon about AI in real life can be investigated, and an in-depth understanding of the subject can then be achieved. So, these methods involve multiple sources, and then exploratory or meta-analysis can be made on them in order to find valuable information on the subject.

4.2 Design

The meta-analysis is the chosen research design for this study. It involves using different secondary sources so as to draw a conclusive inference based on the study objectives. This design consists of combining pertinent qualitative and quantitative data from various chosen studies in order to develop a conclusion that has a robust statistical strength (Cooper, 2015). This increased power emanates from an increased number of secondary sources with powerful research outcomes. Research design commonly fuses different components of a study coherently, thereby ensuring that the research problem is adequately addressed. Therefore, it combines the main elements of the collection, measurements, and data analysis. Noting that this study will involve case studies and secondary data as the primary method for research, the data collected from different sources will then be analyzed based on the objectives. This way, a comprehensive inference can then be made based on the impacts an AI may have on innovation, particularly considering the business model innovation, which is believed to pose a disruptive situation in the competitive environment.

4.3 Procedure

The main procedure for doing this study is first to formulate a feasible research problem concisely and then justify the selection. Afterward, the study will consider realistic literature on AI any other information, which may provide relevant data based on the objectives. The research will then involve identifying the hypothesis explicitly and then work with it in order to conclude. The data retrieved from different secondary sources will then be analyzed alongside the case study, and this process will be followed by undertaking adequate tests on the hypothesis in order to explain the link between them and the objectives of the study. The last and significant step is to meta-analyze the retrieved data from the archived sources in order to draw a conclusive interpretation.

4.4 Expected Results

The expected analysis is expected to prove the null hypothesis false. Therefore, AI is supposed to appear as a disruptive technology in a competitive environment. This effect is considered as one of its significant impact in an entrepreneurial environment. Also, as part of the objectives of the study, the meta-analysis of archived sources and case studies is supposed to reveal the obstacles that AI faces in the world in detail. Also, the research will elucidate how AI changes business models. This aim will then form a benchmark on how AI prompts the organizations to alter their approaches in order to cope with the ever-changing competitive business environment.

Another crucial area that is expected of this research is the definitions and trends. This area will reveal types of development approaches, such as symbolic AI and neural AI. Also, defining the current trends in AI technology is crucial for this study. It is because AI is considered, by experts, to be in its early days of development (Li et al., 2017). Therefore, much work remains for it to reach an efficiency level that will enable scaling learning and even the ability to reason across more extensive applications.

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For confidentiality issues, the case studies will involve the use of anonymous names, such as A and B. Therefore, the inferences will be made based on how AI has impacted their business model innovation. Then from this conclusion, a new AI-based business model can be created in order to help understand the influences that AI has instigated in the world. It may include AI technology, the culture of the organization, preconditions that a business needs to succeed, and even the industry sector. Overall, the results are expected to state how AI affects innovation in the industries by, particularly, considering proactive influences.

5.0 Significance and Conclusion

Technological advancements in AI continue to create more opportunities and even challenges the delivery system. Therefore, this study intends to reveal contingent aspects that help in shaping the business model innovation that is fashioned by emerging technology. Therefore, this study will be useful because it will offer insights on such conditional factors while discussing the proposed AI-based models of running industries. The result of this research can then be helpful to the managers because they can use them in order to advance the growth of their organizations. Generally, artificial intelligence will continue to offer significant merits in different fields, such as business, medicine, science, and computer science, among others. It gives the machines capability to think analytically while using different concepts. So, research in this field is crucial in order to provide scientists an in-depth knowledge on how to advance their creativity in this area. Undoubtedly, this technology will result in a far-reaching impact on the life of humans in the future.


Aversa, P., Haefliger, S., & Reza, D. G. (2017). Building a winning business model portfolio. MIT Sloan Management Review58(4), 49-54.

Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (No. w24449). National Bureau of Economic Research.

Cooper, H. (2015). Research synthesis and meta-analysis: A step-by-step approach (Vol. 2). Sage publications.

Kepuska, V., & Bohouta, G. (2018). Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa, and Google home). In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 99-103). IEEE.

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Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology & Electronic Engineering18(1), 86-96.

Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications23(2), 368-375.

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Sousa, M. J., & Rocha, Á. (2019). Skills for disruptive digital business. Journal of Business Research94, 257-263.

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