Robotization in Business: Issues and Solutions

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Table of Contents

Overview of AI and Robotization in Business
Current Situation
Comparison and Impact on Business
Options for Future Research, Development, and Implementation of AI
Conclusion
References

Overview of AI and Robotization in Business

The modern business environment is characterized by high competitiveness enhanced by a wide range of technological innovations that have become available in the wake of globalization and continuous scientific progress. The unique possibilities offered by developing digital technologies, IT, and artificial intelligence present new challenges associated with effectively incorporating them into business strategies. Though the benefits of embracing these innovations, including optimization of technological processes, improving the efficiency of data analytics, and significant cost reduction, are attractive, many scholars have begun to question the possible negatives of this approach (National Science and Technology Council, 2016). Extensive reliance on AI-based algorithms without proper analysis and control presents significant potential hazards for public safety, cybersecurity, and informational integrity in business applications and globally (National Science and Technology Council, 2016). Therefore, identifying the issues associated with the wide use of AI in business is essential to ensure its compatibility with companies’ interests and is also a question of responsibility and accountability. Delving into this discussion can provide insights into the current approaches regarding the use of AI and present directions for further research on the matter.

Current Situation

For businesses focusing on relevant challenges and possibilities is the only reliable way to maintain competitiveness and growth. One of the most recent global problems is the COVID-19 pandemic, which has taken its toll on the international economic system and has raised multiple concerns over global safety and stability. While governments and international organizations were searching for ways to mitigate the negative impact of the pandemic by introducing quarantine measures and focusing on developing effective vaccines, AI has presented several potential solutions for optimizing these processes (Curlee & Wyatt, 2020). A wide range of mobile applications was introduced to help track people’s movement and contacts to analyze the dynamics of the infection’s spreading and to facilitate the self-isolation of potential carriers (Curlee & Wyatt, 2020). Companies like Meta use such information in user data analytics for a more effective targeted advertisement and to manage their services and products for maximizing profit (Marr, 2020). Additionally, businesses can rely on this information to better prepare for critical situations and to optimize their operational procedures based on the current socio-economic trends.

The global impact of the pandemic has also raised an issue of the rapid development of vaccines. While the traditional bureaucratic, scientific, and technological processes had to be optimized, artificial intelligence has gained pace thanks to its efficiency and presumed reliability (Curlee & Wyatt, 2020). Particularly, Moderna and Pfizer have been using AI to facilitate their research on vaccines for some time, though the effectiveness of this approach is yet to be analyzed and determined (Curlee & Wyatt, 2020). Either way, drug manufacturing companies are currently among the primary potential adopters of the deeply integrated use of artificial intelligence in research and development.

Another issue triggered by the pandemic is the mandatory self-isolation imposed in many countries to limit the spread of the infection. This approach has significantly complicated many processes in certain areas of life and business, particularly education, healthcare, nursing, and personal assistance (Merkusheva, 2020). The difficulties of ensuring safe interpersonal contact in these settings have led to the increasing use of robots by different institutions, organizations, and businesses worldwide to limit human interactions (Merkusheva, 2020). For example, the Smart Field Hospital in Chinese Wuhan has been using robots provided by CloudMinds Technology to carry out simple nursing tasks and provide entertainment for the residents (Merkusheva, 2020). This approach can be adopted for similar applications worldwide.

Comparison and Impact on Business

Utilizing artificial intelligence in data gathering and analysis, introducing deep learning algorithms for scientific research and drug development, and replacing human workers with robots are common trends in a modern business environment. The issues discussed above are particularly relevant for use in the highly sensible healthcare sector. They are applicable worldwide, considering the common context of the COVID-19 pandemic. However, their application is not limited to this setting due to the possibilities that they present for the development of different businesses and organizations, from drug and other products manufacturing to data science and communication. While the use of robots and automation in industry has been growing since the 1950s, including advanced artificial intelligence in technological processes is a relatively recent issue, gaining pace since 2015 (Hedgepeth, 2019; Dilmegani, 2022). Multibillion-dollar corporations like Google, Meta, and Amazon are constantly racing in the research and implementation of these technologies due to their organizational and economic benefits, including operational efficiency and cutting human labor costs (Hedgepeth, 2019; Marr, 2020). Despite the growing popularity of AI-based technological solutions in businesses, some problems associated with these issues exist.

All of these issues rely on the assumption of the inherent efficiency and reliability of AI-based technologies and the optimizing effect of automation on different analytical and practical processes. This assumption, however, has been repeatedly challenged by scholars and practitioners due to a lack of conclusive research on the matter (Hedgepeth, 2018). Despite the speed at which deep learning algorithms can produce results, some experts talk about the downsides of this technology, mainly the ways AI algorithms are trained and the consequences of these traditional approaches (Hedgepeth, 2019). Typical AI programs require potentially billions of articles fed to them to learn how to complete the simplest tasks (Hedgepeth, 2019). Since the contents and specifics of initial data influence the programs’ operational framework, it may lack the flexibility required for broad application.

Besides technological aspects, several other problems are present in the notion of the inevitable increase in AI use by corporations which may corrupt business applications. According to Hedgepeth (2019), since human engineers are still the major part of AI systems development, biases remain an inevitable part of any such system. Hedgepeth (2018) and Middleton (n. d.) also bring up the possibility of replacing human workers with automation and robots, potentially contributing to the job crisis as manual and, in some cases, even intellectual labor is taken over by AI systems. This fact brings the ethical dimension into the discussion on the topic, which must be addressed to preserve the integrity of the business environment concerning the employees’ access to jobs.

Options for Future Research, Development, and Implementation of AI

One of the possible options for research and development is the use of wearable technologies in different applications. The wearable technology business market development presents ways to include them in business without compromising the ethical side of the issue (Worldwide wearable technology market, n.d d.). Integrating various sensors and haptic interfaces to enhance users’ ambient awareness and performance into their standard working procedures can benefit the overall operational efficiency of businesses (Worldwide wearable technology market, n. d.). Therefore, companies should focus on introducing wearable technologies such as virtual reality and communication devices into existing processes to assist human workers instead of replacing them (Middleton, n. d.). The reactive type of AI can be used in this approach due to its accessibility and ability to provide optimized results based on different inputs (Joshi, 2019). This approach can benefit businesses in the long run, ensuring their positive moral appearance among customers and employees.

Another direction for study and potential implementation into companies’ strategies is the prospect of providing education for their employees in AI-related competencies. Artificial emotional intelligence would be the best solution for it due to its ability to understand human needs and responses on a deep level (Joshi, 2019). However, additional research is needed to develop this type of AI. According to Hedgepeth (2018), with the developments in digitalization and computerization, especially perceptible in the increasing popularity of the Internet of Things, new professions and training directions have appeared worldwide. Many are associated with information technologies, data analytics, cybersecurity, and automation, including robotization and artificial intelligence. (Hedgepeth, 2018). With the continuous expansion of technological possibilities provided by wireless communication solutions such as WiFi6, LTE, and 5G, the demand for professional operators will also increase (Worldwide wearable technology market, n. d.). Therefore, it is in the best interests of businesses to ensure that they have a reliable team of professionals trained for the specific technologies and solutions adopted by these companies.

Conclusion

The final suggested research and development option is a large-scale introduction of robots into dangerous working environments. One of the benefits of modern AI technologies and robotics is the possibility of increased safety for human employees. For example, India’s Vyommitra robot and NASA’s Robonaut 2 are designed for use in open space and harsh conditions of other planetary bodies (Merkusheva, 2020). Reactive and limited memory types of AI can already be used for these applications (Joshi, 2019). Future research and experiments will further increase the efficiency of such robots and the range of their capabilities. This option synergizes well with the previous proposals, so parallel development can positively influence them.

References

Curlee, W. & Wyatt, K. Y. (2020). Will AI find an effective vaccine against COVID-19? In Homeland Security. Web.

Dilmegani, C. (2022). Future of AI according to top AI experts of 2020: In-depth guide. AI Multiple. Web.

Hedgepeth, W. O. (2018). The growth of artificial intelligence in our society. APU Edge. Web.

Hedgepeth, W. O. (2019). AI online teachers may not be a good instructional tool. Online Learning Tips. Web.

Joshi, N. (2019). 7 types of artificial intelligence. Forbes. Web.

Marr, B. (2020). 5 predictions for wearable technology: From fitness trackers to “humans 2.0”. Forbes. Web.

Merkusheva, D. (2020). 10 humanoid robots of 2020. ASME. Web.

Middleton, C. (n. d.). We are the robots. Chris Middleton. Web.

National Science and Technology Council. (2016). Preparing for the future of artificial intelligence. UNODC. Web.

Worldwide wearable technology market (2020 to 2025) – Learn about new wearable technology business models such as pay-per-gaze. (n. d.). CISION. Web.

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