Convergence Of AI And IoT For Business

Boosting Productivity, Security, and Efficiency

Today’s business is changing, and in order to be competitive in the market, the businesses are focussing on capturing a tremendous amount of data from multiple sources. Businesses are finding ways in order to get ahead and stay ahead of the competition. As AI and IoT are the technologies in today’s era that poised the impact of every industry as well as society radically, therefore, it is necessary for the business to invest in AI and IoT technologies (Zhou et al. 2019). The convergence of AI and IoT can help in redefining the way in which business generally functions and further helps in increasing operational productivity, improving protective measures, triggering new services and eliminating costly unplanned downtime.

The main aim of the study is to analyze how the convergence of AI and IoT is helpful in keeping the business competitive. The study also focuses on the ways that strengthen security measures for avoiding data breaches and theft of confidential information.

The objectives of the study are as follows:

• To analyze how the convergence of IoT and AI mainly helps businesses in staying ahead of the competition;
• To identify the way in which AI and IoT strengthen security measures;
• To determine the limitations of AI and IoT use in businesses;

According to Liu et al. (2018), AI in IoT crunches the constant streams of data and further helps in detecting patterns. Additionally, machine learning coupled with AI further helps in predicting the operation of the conditions for detecting proper parameters for ensuring proper and ideal outcomes. Hence, intelligent IoT helps in offering proper insights on the processes that are redundant as well as time-consuming for boosting operational efficiency. It is stated by Ke et al. (2020) that data collection is quite important for the growth and development of the business. The presence of IoT strategy within a business generally help the business in transforming data compulsion by offering greater access to the consumer. Additionally, AI within the business generally helps in handling information quite well, which further help the business in using the acquired data for devising better means for enhancing the consumer experiences. It is opined by Yarlagadda (2018) that pairing AI and IoT generally help the businesses in understanding as well as predicting a very much broad range of risks which further help the organizations in properly handling financial loss, cyber threats as well as safety of the employees.

According to Hansen and Bogh (2021), the current rise in data breaches as well as theft of confidential information, security, as well as safety, becomes one of the most concerning factors for a business. IoT enabled AI helps in providing militant support to the private information of the organization, and it further the intrusion of the third party. Additionally, machine to machine communication is also facilitated by organizations for detecting incoming threats as well as for giving out automated responses to the hackers. It is stated by Misra et al. (2020) that there must be a fine balance between the supply as well as the demand, and therefore it is necessary to use AI and IoT as the convergence of both the technology helps in improving the inventory management and helps in analyzing the condition of the stock early which further help the retailers in finding products that cannot be sold. It is opined by Soni et al. (2020) that AI and IoT platforms help the business in maintaining proper cybersecurity. The use of technology helps in the successful and proper management of the project risks as well as financial hurdles that occur within the organization and further provides prompt responses for handling the risks quite well.

Intelligent IoT – Insights, Data Collection, and Consumer Experience

According to Zhou et al. (2019), the convergence of AI and IoT technologies generally take away mundane jobs, which can be a challenge for less-educated individuals. Additionally, increasing employment further becomes a big trouble for developing countries where entrepreneurship, as well as unemployment, is already a challenge. Additionally, a major disadvantage that any worker of the business experience is mainly due to increasing dependency on the technologies. It is identified that when inevitable glitches, bugs as well as failures occurs then, it becomes quite difficult to handle the situation well. It is stated by Ke et al. (2020) though IoT and AI have wider scope in almost every business still it is necessary to keep proper contact with the IoT development company in order make an amalgamation of the business successfully by avoiding project-related challenges. Furthermore, the efficiency of AI is mainly dependent on the data that is gathered, and thus, if the data that is gathered is not sufficient enough, then it becomes quite difficult to make accurate decisions as biases can impact the outcomes. It is opined by Yarlagadda (2018) that developing an AI system can be too difficult for achieving in practice, and sometimes the use of AI raises ethical, legal as well as moral issues that are not properly addressed.

Research philosophy

In this study, positivism research philosophy is applied by the researcher in order to properly understand the significance and reality of the research work (Boaz et al. 2018). The positivism research philosophy generally helps in researching the entire study based on scientific analysis so that it becomes easy to understand the significance of IoT and AI for the growth and success of the business.

Research approach

The researcher utilizes a deductive research approach as the deductive method mainly focuses on different types of established theories as well as philosophies that are associated with AI and IoT for filling up the gap so that the entire research can be conducted quite successfully. Additionally, the inductive research method is mainly avoided as the approach mainly focuses on the real scientific-based research framework (Novikov and Novikov 2019).

Data collection method

The qualitative data collection method is used for collecting data and information related to the convergence of AI and IoT. A number of journal articles as well as research papers are reviewed in order to analyze the significance of IoT and AI for the success and growth of the business.

Research design

The researcher selects an explanatory research design for helping as well as successfully understanding the effect of enhancing knowledge. With the help of this particular research design, it becomes quite easy to understand the importance of AI and IoT. By utilizing this particular research design method as the researcher helps in determining the research gaps as well as limitations that mainly occurs due to a particular resource (Van den Berg and Struwig 2017). After determining the limitations, it becomes quite easier for the researcher to improve the research method for meeting the research objectives.

Ethical considerations

During the research, it is quite necessary to use steps that are ethical and as per the research guidelines for avoiding unethical behaviour.

• The findings of the research are mainly dependent on proper secondary data analysis so that the outcomes of the research are accurate and dependent on data and information of research papers and journal articles for avoiding inaccurate information.
• The data and information that will be collected from the research papers and journal articles will be used for educational purposes only, and none of the information will be misused.
• The researchers will not be pressurized to work extra in order to meet the deadline as it can create ethical challenges.

Timeline

The timeline that is prepared for conducting the research that focuses on IoT and AI reflects that a total time of around 6 weeks is needed. The detailed timeline is provided below:

References

Boaz, A., Hanney, S., Borst, R., O’Shea, A. and Kok, M., 2018. How to engage stakeholders in research: design principles to support improvement. Health research policy and systems, 16(1), pp.1-9.

Hansen, E.B. and Bøgh, S., 2021. Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, pp.362-372.

Ke, R., Zhuang, Y., Pu, Z. and Wang, Y., 2020. A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. IEEE Transactions on Intelligent Transportation Systems.

Liu, L., Zhou, B., Zou, Z., Yeh, S.C. and Zheng, L., 2018, September. A smart unstaffed retail shop based on artificial intelligence and IoT. In 2018 IEEE 23rd International workshop on computer aided modeling and design of communication links and networks (CAMAD) (pp. 1-4). IEEE.

Misra, N.N., Dixit, Y., Al-Mallahi, A., Bhullar, M.S., Upadhyay, R. and Martynenko, A., 2020. IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal.

Novikov, A.M. and Novikov, D.A., 2019. Research methodology: From philosophy of science to research design. CRC Press.

Soni, N., Sharma, E.K., Singh, N. and Kapoor, A., 2020. Artificial intelligence in business: From research and innovation to market deployment. Procedia Computer Science, 167, pp.2200-2210.

Van den Berg, A. and Struwig, M., 2017. Guidelines for researchers using an adapted consensual qualitative research approach in management research. Electronic Journal of Business Research Methods, 15(2), pp.pp109-119.

Yarlagadda, R.T., 2018. Internet of Things & Artificial Intelligence in Modern Society. International Journal of Creative Research Thoughts (IJCRT), ISSN, pp.2320-2882.

Zhou, J., Wang, Y., Ota, K. and Dong, M., 2019. AAIoT: Accelerating artificial intelligence in IoT systems. IEEE Wireless Communications Letters, 8(3), pp.825-828

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