The continuous advancements in technology, including generative artificial intelligence (AI), have raised concerns about their impact on various job sectors and the potential effects on knowledge workers. As we have witnessed from the Industrial Revolution to the current age of AI, changes in the workforce are inevitable. Large language models, like GPT, can revolutionize the knowledge worker industry and reshape how employees collaborate with machines to enhance productivity and efficiency.
In this context, communicators and advisors must comprehend the paradoxes and theories underlining the connection between technology and people and the dynamics between early adopters and those who are slower to embrace AI technologies.
Paradoxes, Theories and Models, oh, my
Several theories and paradoxes help us understand the relationship between technology and people from a communicator’s and advisor’s perspective.
- The Jevons Paradox indicates that when technological advancements improve efficiency, consumption increases instead of reducing the demand for resources or labor.
- underscores the complementary strengths and weaknesses of humans and machines, where tasks that humans find easy are challenging for machines and vice versa.
- The O-ring Principle highlights the significance of human expertise, creativity, and judgment in tasks that remain even as some aspects of a job are automated.
- The never-get-enough, drives job creation, suggesting that participation in the labor market has risen despite labor-saving automation.
- The S-curve Theory explains the growth and adoption of new technologies over time, showing that technology adoption starts slowly, accelerates, and then plateaus as it reaches market saturation. This can help communicators and advisors anticipate and plan for the stages of technology adoption and its impact on businesses and industries.
- The Diffusion of Innovations Theory, on the other hand, describes the process through which new ideas and technologies spread within a social system. This theory categorizes individuals into groups based on their willingness to adopt new technologies: innovators, early adopters, early majority, late majority, and laggards. Understanding these categories allows communicators and advisors to tailor their strategies and messages for each group, ensuring more effective communication and smoother technology adoption.
In addition to the principles and theories mentioned earlier, there are several other concepts that can help us better understand the relationship between technology and people:
- Technological Determinism: This theory suggests that technological advancements drive social, cultural, and economic changes. It implies that technology is an independent force that shapes human behavior, institutions, and values.
- Social Constructivism: In contrast to technological determinism, social constructivism emphasizes that human actors and social conditions shape the development and adoption of technologies. This perspective highlights the role of social, political, and economic factors in influencing technology use and its impact on society.
- Digital Divide: This concept refers to the gap between individuals, households, businesses, and geographic areas concerning access to information and communication technologies. The digital divide can lead to inequalities in opportunities and resources, as well as social and economic exclusion.
- Technological Unemployment: This term refers to the loss of jobs due to technological advancements and automation. While some experts argue that technology will create more jobs than it displaces, others believe that the pace of change will lead to a significant increase in unemployment, emphasizing the need to reskill and upskill the workforce.
- Human-Computer Interaction (HCI): This interdisciplinary field studies the design and use of computer technology, focusing on the interfaces between humans and computers. HCI research aims to improve the usability, accessibility, and user experience of technology, considering cognitive, social, and emotional factors that influence user interaction with digital systems.
- Responsible Innovation: This concept emphasizes the need for ethical considerations and social responsibility in the development and deployment of new technologies. It encourages stakeholders to engage in dialogues and collaborations to assess potential risks, benefits, and societal impacts of technological advancements.
- Technological Adaptation: This principle refers to the ability of individuals and organizations to adopt and adapt to new technologies. It involves learning new skills, adjusting to changes in work processes, and embracing new ways of thinking to maximize the benefits of technological innovations.
- Technology Acceptance Model (TAM): This model suggests that the adoption of a new technology depends on two primary factors: perceived usefulness (the extent to which a user believes that using the technology will enhance their performance) and perceived ease of use (the degree to which a user believes that using the technology will be free from effort).
Each of these principles can provide valuable insights for professionals working in various fields, such as reputation management, public relations, data analytics, and geopolitical analysis, as well as organizations aiming to understand better the complex relationship between human behavior, job markets, and technology advancements. By leveraging these insights, effective communication strategies can be developed, and informed decision-making can take place, enabling businesses, individuals, and professionals to harness the power of technology while mitigating potential risks and adapting to the evolving technological landscape.
Front-Runners and Nonadopters
The Trainers, Explainers, and Sustainers
A timeless MIT Sloan Management Review study by H. James Wilson, Paul R. Daugherty, and Nicola Morini-Bianzino proposes three new job categories that AI will create: trainers, explainers, and sustainers. These roles involve humans working alongside machines to ensure they operate effectively and ethically.
- Trainers: these professionals will teach AI systems how to perform tasks and make decisions. They will help machines understand human behavior, preferences, and cultural nuances, ensuring that AI systems can interact effectively with people.
- Explainers: they will bridge the gap between AI systems and human users by interpreting and translating the decision-making processes of machines. They will help businesses and individuals understand the rationale behind AI recommendations, fostering trust and confidence in AI-driven solutions.
- Sustainers: they will monitor and maintain AI systems, ensuring they operate within ethical and legal boundaries. They will address any biases in AI decision-making and ensure that AI technologies align with the core values and principles of the organizations they serve.
Generative AI has the potential to transform the knowledge worker industry and redefine the relationship between humans and machines. By understanding the paradoxes and theories that underpin this relationship and acknowledging the dynamic between front-runners and nonadopters, we can better prepare for the AI-driven future.
The emergence of new job categories, such as trainers, explainers, and sustainers, highlights the importance of human expertise in working alongside AI technologies. By fostering a culture of lifelong learning and interdisciplinary knowledge, we can ensure that the benefits of AI are shared across all societies.