Salford Business School

Mid-Term Assignment | Student# @00582731



Blog and Review

Review: Artificial Intelligence in Business: From Research and Innovation to Market Deployment


Neha Sonia, Enakshi Khular Sharmaa, Narotam Singhb, Amita Kapoorc Information Communication and Instrumentation Training Centre, India Meteorological Depart


Summary of Paper

Many intelligent products and services have emerged in the past decade while their commercial availability and the socioeconomic impact is undeniable.

The paper focuses on the full range of implications of AI and discusses both positive and negative sides and impacts on government, communities, companies, and individuals.

Besides of overall impact of AI from research to deployment, this paper addresses the influential achievements and innovations in this field. According to (Soni, et al., 2020), Two lists of top 100 AI startups are considered and discussed in addition to how AI can impact business operations and the global economy.

The abstract section clearly explains the paper or the details of the work. However, in the introduction, the author had more room and space to go into detail.

Quality of the Research and The Research Method Item

The research question is clearly stated in the introduction section, and each objective/question is tackled in the paper. The search question in which it asks, “if the present emergence of AI is just hype, or does it have the capability of transforming the world?” Could have been asked differently while still conveying the same idea to the reader. For the objectives of the experiment, they were cited in the introduction section. The author did mention a lot of sources for reference. However, the majority of them were articles or published work regarding Deep Learning; further studying and reading is needed to determine if those papers back up the claim made in this one.

Each table/graph has the proper narrative to back it up. However, It would be more appropriate to directly have the source in the footnote to make it easier for the reader to check the source and the work. The beginning part of the Discussion and Conclusion is supported by the data and the earlier presentation in the paper; however, the final paragraph not so much. The part about the vulnerability of Deep Learning Networks (DLN) and the unreliable results they can present. Besides, the shortage of AI talent in the market.

Regarding the methodology and approaches toward the response to the objective, the author started with an introduction, following by research objective and DC, State of the art of AI, Global Market analysis, and conclusion.

Starting with innovation and describing it and its relationship with AI and similar technologies was a good example and comparison methodology. According to this article, CC, big data, IoT, and AI are the trend technologies. The importance and integration of these technologies are studied in the introduction section. Increases in transparency [open source and code sharing] and computer tech improvement are introduced as the main factors for the advancement. Different impacted social and commercial fields are studied, and AI is defined as the heart of progress in industry 4.0. Evidence and data in this regard support the article.

The above figure illustrates that in AI 2017, Core AI gained the maximum attention, and in AI 2018, cybersecurity was maximally benefitted from the AI technology. Unfortunately, the paper is not correctly cited or referenced. It took quite a noticeable time to find the sources of information. [especially figures and tables]

The conclusion section is addressed and organized. The topics have been responded to one by one and highlighted. Four broad areas of deep learning have been concluded, computer vision, text analysis, speech recognition, and game playing. Despite these conclusions, other fields of integration of AI somehow are missing [for example, prediction in HR, work efficiency, etc.]. It has been concluded that the AI trend is continuing and request for AI growth is exponential. The main areas of influence of AI in business and automation are concluded. The impact of AI on inequality in social, economic, and cultural sectors in a different part of the world is concluded, which is a fascinating finding.

Quality of Presentation Item
  • Utilization of available Data and including the previous findings in the paper [fully or partially]
  • The data and comparisons, in general, are narrowed down to two years [17-18]. Widening the perspective is recommended
  • Definition of businesses and the impact of AI in each specific sector of companies separately
  • The author relays on tables and graphs to present her work. However, the figures are not shown in the right way. Some of them are crowded and can be confusing.
  • Improvement can be made in the formatting of the structure and body, to make more eye-catching and an exciting read for the reader.
  • Proper Citation and increasing the referencing throughout the body of the paper.
  • Some of the references are published by the author(s).

Agrawal, A., Gans, J. & Goldfarb, A., 2019. Economic policy for artificial intelligence. Innovation Policy and the Economy, 19(1), pp. 139-159.

Anon., n.d. The Impact of Artificial Intelligence on Innovation. [Online]
Available at:

Borkar, V., Carey, M. & Li, C., 2012. Big data platforms: What’s next?. XRDS: Crossroads, The ACM Magazine for Students, 19(1), pp. 44-49.

Furman, J. & Seamans, R., 2018. AI and the Economy. IDEAS Working Paper Series from RePEc.

Lazo, M. A. A., 2018. Schwab, Klaus. The Fourth Industrial Revolution. Ginebra: World Economic Forum, 2016, 172 pp. Economía (Lima), 41(81).

Lecun, Y., Bengio, Y. & Hinton, G., 2015. Deep learning. s.l.:Nature Publishing Group.

Marston, S. et al., 2011. Cloud computing – The business perspective. Decision Support Systems, 1 4, 51(1), pp. 176-189.

Park, S. C., 2018. The Fourth Industrial Revolution and implications for innovative cluster policies. AI and Society, 1 8, 33(3), pp. 433-445.

Soni, N., Sharma, E. K., Singh, N. & Kapoor, A., 2020. Artificial Intelligence in Business: From Research and Innovation to Market Deployment. s.l., Elsevier B.V., pp. 2200-2210.

Taigman, Y., Yang, M., Ranzato, M. & Wolf, L., 2014. DeepFace: Closing the gap to human-level performance in face verification. s.l., IEEE Computer Society, pp. 1701-1708.