A Technology Landscape of Artificial Intelligence: Technological Structure and Firms’ Competitive Advantages

2019 ◽  
Vol 22 (3) ◽  
pp. 340-361
Author(s):  
Wangjae Lee ◽  
Hakyeon Lee
2020 ◽  
Vol 0 (0) ◽  
pp. 1-20 ◽  
Author(s):  
José Luis Ruiz-Real ◽  
Juan Uribe-Toril ◽  
José Antonio Torres ◽  
Jaime De Pablo

Artificial Intelligence is a disruptive technology developed during the 20th century, which has undergone an accelerated evolution, underpinning solutions to complex problems in the business world. Neural Networks, Machine Learning, or Deep Learning are concepts currently associated with terms such as digital marketing, decision making, industry 4.0 and business digital transformation. Interest in this technology will increase as the competitive advantages of the use of Artificial Intelligence by economic entities is realised. The aim of this research is to analyse the state-of-the-art research of Artificial Intelligence in business. To this end, a bibliometric analysis has been implement using the Web of Science and Scopus online databases. By using a fractional counting method, this paper identifies 11 clusters and the most frequent terms used in Artificial Intelligence research. The present study identifies the main trends in research on Artificial Intelligence in business and proposes future lines of inquiry.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hishan S. Sanil ◽  
Deepmala Singh ◽  
K. Bhavana Raj ◽  
Somya Choubey ◽  
Narinder Kumar Kumar Bhasin ◽  
...  

Purpose “Machine learning (ML)” in business aids in increasing company scalability and boosting company operations for businesses all over the world. “Artificial intelligence (AI)” technologies and several “ML” algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of “ML” is continuously increasing. As a result, companies may now profit from knowing how companies may use “ML” and incorporating it into their own operations. “ML” derives useful results from the data to address very dynamic and difficult social and business problems. ML helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ML is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic. Design/methodology/approach For firms all around the world, “ML” in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly popular. The importance of “ML” is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using “ML” and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ML) extracts valuable results from data. Machine learning (ML) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ML is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemic. Findings According to a new study published by the Accenture Institute for High Performance, “AI” might double yearly economic growth rates in several wealthy nations by 2035. With broad AI deployment, the yearly growth rate in the USA increased from 2.6% to 4.6%, resulting in an extra $8.3tn. In the UK, AI may contribute $814bn to the economy, raising the yearly growth rate from 2.5% to 3.9%. The authors are already in a business period when huge technological development is assisting us in addressing a variety of difficulties to achieve maximum development. AI technology has enormous developmental consequences. In addition, big data analytics is helping to make AI more enterprise ready. Future developments in “ML” cannot be understated. Machines will very certainly eventually be smarter than humans in practically every way. Originality/value The introduction of AI into the market has enabled small businesses to use tried-and-true strategies for achieving greater business objectives. AI is continually offering a competitive advantage to start-ups, whilst large corporations provide a platform for building novel solutions. AI has become an integral component of reality, from functioning as a robot in a production unit to self-driving automobiles and voice activated resources in complex medical procedures. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).


2021 ◽  
Vol 244 ◽  
pp. 10011
Author(s):  
Khuta Gumba ◽  
Svetlana Uvarova ◽  
Svetlana Belyaeva ◽  
Vyacheslav Vlasenko

Sustainable development is possible only on the basis of the growth of innovation and the introduction of digitalization in modern conditions. Therefore, it is necessary to speak about the concept of systemic competitiveness, the basis of which is sustainable competitive advantages. This paper is devoted to the problems of forming the competitiveness of construction in the modern economic conditions of the transition to a new technological structure and a digital economy, based on the implementation of sustainable competitive advantages predicted on the basis of foresight and activated by creating “points of innovative growth”.


Author(s):  
Lyudmila Aleksandrovna Gudyaeva

One of the key priorities of the modern Russian domestic policy lies in gaining competitive advantages in solution of the tasks of global scientific and technological frontiers as the basis for sustainable economic development. Russian economy is oriented towards seeking new growth drivers through technological changes in both traditional and new technology, products and services markets. A breakthrough in the sphere of innovations is possible in the conditions if the applied science would create the system of advanced scientific-technological platform, which is human centered and more resilient for the future. Thus, in the context of drastic technological and structural shifts in the global economic system, Russia’s task of transition towards the new stage of scientific revolution acquires particular relevance. The goal of this article consists in the analysis of scientific and innovation development of the Russian Federation and its regions under the new conditions. Analysis is conducted on the fundamental trends within the framework of transition to a new technological structure, scientometric indicators worldwide, Russia and the Republic of Tatarstan in the context of the prospects for the country’s accomplishment of the new objectives of global scientific and technological agenda. The author determines the central problems in building the integral domestic innovation system in the conditions of external factors; develops the original approach towards assessment of scientific capacity of the universities and academic institutions in the technological paradigms of the Industries 4.0 and 5.0.


Author(s):  
David L. Poole ◽  
Alan K. Mackworth

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