Artificial Intelligence in Business

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Introduction

The successful application of Artificial Intelligence (AI) in businesses would arguably be one the most pre-eminent innovations of all time; or the worst. AI is gradually being adopted into everyday business use, ranging from management workflow to different trend predictions. It opens up new opportunities for businesses, with research estimating its potential to increase productivity is at 40% or more (Ricard, 2020, para. 1). Further, “professional services giant PwC claims AI could add nearly $16 trillion to the world economy by 2030. The consultancy group McKinsey predicts $13 trillion in the same time frame,” (Ricard, 2020, par. 1). However, while AI may be intelligent, it remains a machine. Its emergence is paving way for a whole new different set of business models, but it is not without its inherent problems that may affect business operations.

New Problems Related to the Impact on Business

One of the main concerns with the adoption of AI is bias. AI algorithms are human-made, meaning, they could have in-built bias created either intentionally or otherwise by their makers. As a result, the AI algorithms produce biased results that may lead to unintended consequences (Chalmers et al., 2020). A biased AI system will damage a company’s reputation and credibility instantly, especially in a generation where people are aware of rights and inclusivity and are ready to ostracize companies based on related missteps (Park, 2017). A recent example was Amazon’s AI-infused hiring process in 2018 that received negative press for being biased against women. The program was trained predominantly on resumes that men submitted, it ended up being biased against female applicants.

Secondly, safety and social manipulation are a concern for businesses and their consumers. AI technology is bound to malfunction, and that would be detrimental for the businesses deploying it (Chalmers et al., 2020). Instances, where such malfunctions have occurred, include when the AI chatbots in Facebook started interacting with one another in a new language that only they could understand, also when Microsoft’s Twitter chatbots were hijacked. It would be difficult to retrieve and secure personal data if a faulty AI used an unknown language. In addition, these malfunctions raise ethical issues and legal concerns for the companies using them. Several law enforcement and private entities are known for using facial recognition technology. Google, in 2015, launched a photos app that was meant to make searches easier for its users. Instead, it implicated the company and raised ethical concerns when it tagged a black couple as gorillas.

Newest Developments in AI and their Future Projections, Problems Related, and the Solutions

AI Advancements in Wildlife Conversation

Oxford University recently developed a new AI software that has the ability to recognize and track chimpanzees in their habitat. With facial recognition, scientists project they will cut down the time and resources required to track animals in the wild by about ten times or more. For instance, the team used 50 hours of archival footage extending over 14 years to train the AI. The video of the 23 chimpanzees in the wild was taken in Bossou in Guinea, West Africa. The algorithm completed the task in 30 seconds and attained an accuracy of 81 percent (Schofield et al., 2019, p. 2). On the other hand, the already experienced researchers available used 55 minutes for the same task and achieved an average accuracy of 42 percent (Schofield et al., 2019, p. 2). These are promising figures and hold a potential to transform the industry.

Similarly, ChimpFace is another AI development that aims at reducing the number of chimpanzees being trafficked. With the emergence of social media, most incidences happen on these platforms, where a seller posts an image of a chimpanzee and finds a buyer on the same platform. Sometimes the buyer ends up posting it on their pages. The ChimpFace is able to scan and find matches of the trafficked animals. Currently, the software is only able to search publicly displayed photos; therefore, traffickers using private Facebook or Instagram profiles can seamlessly conduct their businesses. The manufacturing company has partnered with others, such as Liberia Chimpanzee Rescue and Protection (LCRP) to strengthen the use and further innovation of the software.

AI in Cyber Security

With the COVID -19 outbreak came an increase in cyber threats that wrecked cybersecurity measures and stole sensitive information. CSOs and CISOs came up with AI and machine learning-based tools that would identify anomalies in the existing systems before any breach happens, such as threatening practices and suspicious IP addresses (William, 2020). This, thus, reduces the amount of losses companies incur due to cyber-attacks. The existing problem now is that hackers are also using machine learning to launch their threats. To handle the situation, organizations are training AI to outsmart hackers.

Conclusion

AI will either be the best or worst innovation in the history of technology in equal measure. It is projected to have a massive impact on businesses by improving productivity. On the other hand, it could create massive destruction to businesses mainly as a result of being biased. As intelligent as AI may be, it is made by humans and is bound to malfunction. The malfunctions jeopardize the reputation and credibility of a business. In addition, it puts at risk the safety and privacy of customers’ both the companies and customer data. New AI developments are made often, with some of the most recent being ChimpFace, AI facial recognition for chimpanzees, and AI and machine-based tools to boost cybersecurity. These developments are projected to each improve their respective elements by a significant amount. Companies are collaborating and funding research to be able to advance AI and make them more secure for use.

References

Chalmers, D., MacKenzie, N., & Carter, S. (2020). Artificial Intelligence and entrepreneurship: Implications for venture creation in the Fourth Industrial Revolution. Entrepreneurship Theory and Practice, 45(5), 104225872093458. Web.

Park, SC, (2018). The fourth industrial revolution and implications for innovative cluster policies. AI & Society 33, 433–445. Web.

Ricard, S. (2020). Council Post: AI’s effect on productivity now and in the future. Forbes. Web.

Schofield, D., Nagrani, A., Zisserman, A., Hayashi, M., Matsuzawa, T., Biro, D., & Carvalho, S. (2019). Chimpanzee face recognition from videos in the wild using deep learning. Science Advances, 5(9) 1-9. Web.

William, J. (2020). Council Post: major advances AI that businesses should keep an eye on. Forbes. Web.

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