Advancements in AI Technologies and Their Impact on Future Work

Posted: November 7th, 2023

Advancements in AI Technologies and Their Impact on Future Work

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November 7, 2023

Advancements in AI Technologies and Their Impact on Work

The conventional drivers of economic growth, labour, and capital investment, are no longer capable of sustaining continuous growth in GDP for Canada as an advanced economy. The government is entering a new age of Artificial Intelligence (AI) to counter the physical barriers of labour and capital. Within such an economic transition, the gap that companies have to answer is how best to maximize the advantages of AI while preparing for possible industry disruptions. The growth in AI adoption has not come without its controversies, with economists and policymakers lamenting the potential loss of jobs to automation. However, such concerns are skewed as data shows a decline in low-skill jobs and increased demand for high-skilled jobs. AI is offsetting job losses with job creation. While most of the narrative on AI has centered on job losses, the reality is that more people are excited to use AI to enhance individual and collective productivity, negating its perception as a threat to job security and transforming it into a beacon of future corporate and national prosperity. 

AI and Industry Disruptions

            One of the primary ways AI is transforming the manufacturing industry is through the automation of production processes. Manufacturers are becoming more capable of improving production efficiency, including reducing the demand for human labour, by integrating AI into various production tasks (West & Allen, 2018). An example of automation in manufacturing is the application of robotics in automotive industries to carry out assembly, welding, and painting. AI-powered automation is also used to increase the efficiency and reliability of routing and scheduling in product distribution and logistics. Machine learning algorithms analyze data to identify transport bottlenecks and areas where efficiency can be enhanced. Optimizing routing and scheduling reduces time and resource wastages because of a more streamlined flow of products and services. According to Insider (2018), automation of production processes is another way for manufacturers to avoid human errors. Managers are adopting the technology to reduce their reliance on human labour, enhancing the production process’s predictability.

            Finance is perhaps one of the economic sectors experiencing massive-wide transformations due to AI. Machine learning and AI offer great potential in the finance industry, with data becoming the most valuable asset (Moez, 2023). Businesses are learning to employ machine learning to find patterns in human transactions. As a result, they can generate insight for financial advisory services and trading. There is also the case of AI transforming contemporary marketing. Customer metadata has become integral in tailoring marketing processes to improve the quality of customer interactions and consumer segment targeting (West & Allen, 2018). Automation is also being applied to facilitate the shift from paper-based transactions to digital receipts for optimized analytics in finance and healthcare. Unfortunately, as businesses shift to online environments, they will become more dependent on metadata, which implies the continued use of AI.

            AI-influenced industry changes impact multinational corporations and Small and Medium Enterprises (SMEs). Innovations such as ChatGPT are revolutionizing AI by making the technology accessible to everyone (Chui et al., 2022). The platform provides a range of features that small businesses can employ to enhance their data processing. Because of ChatGPT, businesses can access powerful AI tools with minimal financial strain. While ChatGPT is one of the famous and more accessible AI innovations, there are countless and more powerful platforms in the consumer market (Chui et al., 2022). The diversity of industry disruptions confirms that AI gives companies a competitive advantage by enhancing their efficiency, productivity, and accuracy in understanding consumer markets. Companies that employ AI are better informed for decision-making, underpinning why the technology is an essential investment for any business seeking to be competitive.

The Decline in Demand for Low-Skill Jobs

            The potential imbalance in the job market associated with automation and AI is a looming concern for many communities. Formal reports by OECD suggest that nearly 1.2 million jobs are at risk of replacement or eradication by 2018 (Bordot, 2022). Former American Presidential candidate Andrew Yang believed that AI and automation would rob one out of three Americans of their jobs (Miller, 2021). There is sufficient room for worry because it is unclear how many jobs will be lost or replaced by the technology. Nevertheless, scientific research on AI-related job losses indicates that the relationship is not linear (Miller, 2021). AI disproportionally affects low-income, low-skilled jobs more than high-income, high-skilled jobs (Frenette & Morissette, 2021; Wilson, 2020). Such classes of jobs tend to be occupied by immigrants, people from Aboriginal communities, and ethnic minorities. Therefore, there is credible concern that unregulated AI adoption could reinforce systemic racism.

            Apart from potential job losses, AI could increase the vulnerability of marginalized households to systemic oppression. Machine learning based on old data reinforces conventional social structures that often exclude non-Whites. For example, (Cutts, 2022) conducted a report on machine learning in the healthcare setting, where the cohort involved over 90% of White participants. As a result, the subsequent medical system would prescribe less dosages for African American patients due to a skewed analysis of cardiovascular risk (Cutts, 2022). If the data applied is not accurate or representative, then the predictive capacity for decision-making reduces and becomes flawed. The question that looms in the mind of many people is whether AI will truly offset job losses with job creation. There is also the question of which type of people will access these new positions in the labour market.

            Even though there is reason to be skeptical about AI’s impact on the job market, governments can introduce steps to ensure the negatives do not outweigh the positives. Miller (2021) proposes the introduction of universal basic income (UBI) for low-income households that lose their employment to automation. The introduction of UBI is a more plausible solution to the job losses than campaigning for the complete non-application of AI (Guo, 2021; Deo, 2020). Guaranteeing that low-income households benefit from consistent federal pay will help any transitioning economy to avoid a crisis. Federal policymakers can introduce a tax heritage that helps transfer capital from wealthier classes to low-income households. The objective is to provide low-skill income earners with the necessary funds to start their SMEs or enroll in school to improve their competencies (Guo, 2021). Stimulus payments given during the pandemic were evidence that UBI could address social discord associated with unemployment.

The Rise in Demand for High-Skill Jobs

            There is no going around AI’s potential to create new jobs while supporting existing ones. The scarcity of data on AI adoption is one of the main reasons why little is known about how the technology is currently shaping the labour market (Loprespub, 2018). Early studies highlight an increase in demand for specific AI skills and techniques. According to Alekseeva et al. (2020), there has been an aggregate increase in demand for positions in computer science-oriented fields. The studies also show that the new jobs have a more positive wage growth than the replaced ones (Alekseeva et al., 2020). Therefore, in young start-up companies, AI can potentially reduce wage inequality. People who secure the new jobs benefit from better pay and enhanced opportunities to gradually increase wages. The employment pattern implies improved employee satisfaction and wellbeing in AI-related fields compared to traditional manual jobs.

            Even with the projection of new jobs, it is prudent to acknowledge that AI will not disrupt all industries. Some economic sectors are bound to grow with the adoption of the technology. There will be the creation of new positions with the preservation and modification of old ones (Bordot, 2022). The healthcare industry is at a lower risk of disruption, with AI stimulating the shift to electronic medical records. Improved efficiency in the national healthcare system translates into reduced federal and institutional spending, which increases the likelihood of new positions. Critics argue that technologies such as ChatGPT will bring an end to creatives and the gig economy. The tool was initially projected to replace artists, writers, and actors (Chui et al., 2020). However, AI has led to a surge of artists shifting their creations online to establish digital libraries for tokenization. It is becoming common for creatives to use AI to replace physical galleries. More and more artists are growing from transactions involving non-fungible tokens (NFT), which is mostly facilitated by the growth of AI and digital currencies.  

            AI improves average pay by enhancing demand for high-paying jobs while creating second and third employment opportunities. According to a longitudinal study by OECD in developing countries, adopting AI was associated with a decline in average work hours (Bordot, 2022). The research highlights a negative correlation between the degree of AI exposure and average hours worked per employee in occupations where computer application is high. The trend implies that people working in these new AI-based positions have the chance to secure part-time employment. Such alternative opportunities are another way that AI helps address income inequality in society. The question that research should answer concerning this developing trend is whether the reduced work hours impact employee productivity, wellbeing, and mobility across jobs.

AI and Its Implications for Future Competitiveness

            Companies that fail to adopt and embrace AI in their business strategy risk losing their market share to competitors. AI is leveling the field between large corporations and SMEs by negating physical and material barriers (Insider, 2018). With the growth in data analytics, it is rational to conclude that AI could represent the future of competitive intelligence in the global economy. Smart algorithms represent the future approach for companies in dealing with digital contacts, dependencies, and interconnections in the market. Smart algorithms are how companies will be able to maximize on social media networks (Ulnicane, 2022). Software that can answer market questions promptly will act like an early warning system for corporate decision-makers. On the other hand, the answers will expose new trends and competitive behaviours that will help the organization adjust its supply chain.

            Embracing AI as a competitive strategy introduces new managerial requirements for capacity building. Smith (2017) documents that Canada has grown into a regional hub for AI professionals due to its commitment to capacity building. Canada has a flourishing AI industry in major cities, including Toronto, Montreal, and Edmonton. The government has been investing in localized AI studies via programs such as the Pan-Canadian Artificial Intelligence Strategy. In the same way, corporations will have to invest in AI training, workshops, and programs to enhance their internal capacities to use AI (Smith, 2017). Companies that provide their computer experts access to unique skill development programs are bound to benefit the most in the changing global economy. Organizations that fail to do so will incur additional expenses in contracting outside counsel.

            The biggest mistake managers and entrepreneurs could make is to consider AI as a temporary ‘plug and play’ technology. Some experts claim that the real benefits of AI are yet to be realized for several reasons, such as the lack of human expertise and the technology’s nature of incremental improvement (Ulnicane, 2022). AI is a technology that should not be associated with immediate returns. The reality is that AI adoption is a complex and slow-paced process. The strength of a company’s competitive position in the market will depend on its ability to regulate mismanaged technology expectations (Ulnicane, 2022). Poor management will result in job losses without creating new ones and additional operational costs associated with implementation. Therefore, businesses must undertake internal needs assessment to evaluate which type of AI is required to perform which type of tasks. In addition, management must understand the strengths and weaknesses of each adopted AI technology. Such knowledge is critical in capturing and advancing AI’s incremental improvement.


            Artificial intelligence promises to become a critical force in future business productivity, growth, and competition. Enterprises and governments are identifying ways to use the technology to enhance their global competitiveness. The technology is bound to result in job losses for low-skilled labour while increasing job creation for high-skilled labour. In areas where job losses are immense, UBI can be introduced to offset the loss in household income. However, after the long-term adoption of AI, cost-related improvements, including cost reductions and enhanced productivity, will create room for new investment and employment opportunities. What is required is for corporations to conduct internal needs assessments and to trace the distribution of AI-related benefits. The information from such evaluations is to guide on the type of technologies to be used and the type of developmental training to be offered to employees. Excitement regarding AI is justified, but caution is imperative. Remaining acutely aware of the risks will prove essential in maintaining continued AI-based growth and development.


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