Uncertainty and Opportunity 

At every stage of major technological advancement in human history, from the agricultural to the industrial revolution, humans have had to re-imagine what their future will look like. Novel technologies can redefine the socio-economic fabric of our world, creating new sources of value, meaning, and opportunity alongside unprecedented risks and widespread uncertainty. According to Connor Wright, Partnerships Manager at the Montreal AI Ethics Institute, “Technology is born in a certain time, out of a certain context, by a certain person for a certain purpose.” However, most technologies are neither good nor bad—it’s how they’re used that defines their worth. 

“Technology is born in a certain time, out of a certain context, by a certain person for a certain purpose.”

Therefore, as we consider what a future with AI will look like, we must not only hold our governments and corporations accountable but also ourselves. In this transformative age, the decisions we make as individuals have never been more important, especially since we’re dealing with what may be the most transformative technology in human history—we need to embrace change, and as one executive from a Fortune 500 healthcare company put it, “Anytime a new innovation enters the market, no one’s going to adopt it. So, the early adopters are probably going to be a much smaller subset that’s going to use it, embrace it, and be comfortable with that change.”  

A transformative technology fundamentally alters the structure of businesses, industries, the environment, and society, while also empowering individuals with tools and platforms that enhance their capabilities, learning, health, and overall well-being. AI has proliferated rapidly across industries, yet it’s in the latter part of this definition where AI’s most transformative potential is highlighted. But why?

An uncertain future of work doesn’t indicate a lack of work in the future.

For many, the advent of powerful AI systems represents a considerable threat to their livelihoods, inspiring fear regarding a future where the primary source of economic value no longer lies with humans. However, an uncertain future of work doesn’t indicate a lack of work in the future. Humans create technology because it’s useful to them—it solves a problem or accomplishes a task, freeing them up to pursue things that are more important or meaningful. In essence, technologies are tools that humans use to improve their quality of life, and AI is no different in this respect. “When I think about how people are leveraging AI, I think about how humans can work in tandem, or use it as a tool, or even go as far as treating it as a teammate,” says Wright. 

Nonetheless, to take advantage of tools, individuals must know how to use them—possessing the necessary skills and knowledge required to optimize AI use will be an integral dimension of AI preparedness, and subsequently, AI literacy. Paul Marca, VP of Learning and Dean of Ikigai Labs Academy, sums up this idea with a simple analogy: “We don’t have to know how an internal combustion engine works, but it’s critical that we understand its basic functions so that we can manage it.”

Where AI does differ from other innovations is in the degree of uncertainty it inspires. Currently, no existing technologies have matched the versatility, diversity, and robustness of human capabilities. But, AI does possess the potential to do so, meaning that its array of applications could eventually span across all human task domains, generating unprecedented uncertainty as to what the future of work might entail.  

Uncertainty is like a fork in the road, where one path leads to risk and the other to opportunity. However, the path of opportunity is blocked by a gate, and only those who know how to unlock it will be able to pass through. If the average person wishes to unlock opportunity in the age of AI, they must begin by cultivating AI literacy. 

AI Literacy: What is it? 

AI literacy is something that any individual can acquire with time, interest, and internet access. The core competencies of an AI literate person can be broken down as follows: 

1. A basic functional understanding of AI: A deep technical understanding of AI is unnecessary, in the same way that people don’t need to understand how an internal combustion engine works to drive a car. A functional understanding of AI is one that considers models’ limits and capabilities, as well as the design features that make them suited to a particular task or use case. 

2. The possession of AI-specific skills: Like any other tool, AI systems can be used incorrectly, and this can lead to the destruction of value. To create value and optimize AI use, users must develop AI-specific skill sets. In doing so, users also create opportunities for novel use cases. For instance, the corkscrew was invented in the 17th century to remove bullets stuck in the barrels of muskets, yet today it’s a common appliance in virtually everyone’s kitchen. Moreover, possessing AI-specific skills will also allow individuals to identify which human-specific skills might still be necessary or emphasized as the future of work evolves. 

3. An up-to-date knowledge of current AI applications and use cases: Tracking current AI developments, even at a broad level, can be enormously useful. Possessing an understanding of where AI is generating the most substantial impacts, whether in the form of risks or benefits, can help individuals navigate their career trajectories by identifying novel or existing sources of value creation. 

4. An adaptable mindset via continuous learning and experimentation: AI is proliferating rapidly, which makes it difficult to keep up with the most recent advancements. By experimenting with and continuously learning from AI systems, individuals can increase the robustness of their AI-specific skill sets, making them more adaptable in the long run. If a particular AI skill becomes outdated, AI literate individuals will still have an arsenal of AI skills they can draw from. Moreover, some AI-specific skills may be transferable between different kinds of models—the more one experiments with a variety of different models throughout various task domains, the more likely one is to discover skill-based commonalities between them. 

In a nutshell, AI literacy involves the development of AI-specific skill sets and fundamentals, the cultivation of a broad yet current understanding of where AI is generating the most substantial impacts, and the ability to maintain an adaptable mindset through continuous learning and experimentation. But, as Marca points out, “Understanding the tools and how they work will afford you the ability to experiment and trial,” so the process of developing AI literacy can be viewed as a positive feedback loop. The more you know about AI, the more you will be able to experiment with it. 

“The future of work is connected to the future of learning.”

While some corporations and governments have already begun helping their workers and citizens cultivate AI literacy, this responsibility still falls chiefly on individuals. Given the exponential rate of AI innovation, individuals cannot wait to be spoon-fed by the various institutions at their disposal—those who wait will quickly fall behind those who are proactive, and as Marca predicts, “The future of work is connected to the future of learning.” All that is required for access to some of the most powerful AI systems today is an internet connection, and as Wright suggests, “I don’t need to know how to code in order to use it, I just need to know how to type.” Therefore, those of us with these capabilities have little excuse not to begin cultivating AI literacy now. 

Specific AI Skills and Recommendations

Though we recognize that we can’t predict with absolute certainty which AI-specific skills will prove valuable in the future, we can provide a series of informed recommendations as to what skills and approaches will be necessary to cultivate AI literacy. Nonetheless, AI innovation and deployment are progressing rapidly, so readers should take all recommendations—not just the ones provided here—with a grain of salt. Importantly, we subdivide these skills and recommendations with respect to the aforementioned core competencies required for AI literacy. 

A Basic Functional Understanding of AI 

The following strategies and approaches will be instrumental in cultivating a functional understanding of AI: 

• Explore the wide variety of available online resources: There are a plethora of available online resources that individuals can leverage to develop a basic functional understanding of AI. Such resources include newsletters, articles, and AI reports from accredited research and consulting institutions, educational podcasts, YouTube series, interviews with industry experts, free online courses offered by prominent academic institutions, as well as interactive online communities. To avoid information overload, individuals can also leverage conversational AI models, like ChatGPT, Perplexity, Claude, and Falcon 180B to explore further resources or hone in on specific kinds of AI concepts, though they should be careful to cross-reference all model outputs with reputable sources. 

“What is now actually becoming a skill is when to not use AI.” 

• Understand frontier AI models’ limits and capabilities: Frontier AI—highly advanced general-purpose AI models like ChatGPT and Gemini—tend to represent the tip of the AI innovation iceberg. In other words, if frontier AI models display certain limitations, it’s unlikely that other less-sophisticated models will be able to overcome these limitations, and the same goes for frontier AI capabilities. Therefore, by understanding frontier AI’s limits and capabilities, individuals can gain a stronger and more accurate sense of how AI innovation is progressing and understand where AI should and shouldn’t be used, or as Wright states, “What is now actually becoming a skill is when to not use AI.” 

• Understand basic AI concepts: Understanding basic AI concepts will allow individuals to optimize their AI experience by identifying the contexts in which AI can provide the most value for them. To this point, Marca agrees that “as you start to use a tool, you become more aware of what the use cases are.” Below, we list several useful starting points for individuals to consider (there are many other important AI concepts to think about, however, by beginning with these, individuals can cultivate the necessary foundational knowledge to make further discoveries on their own): 

1. Understand the difference between narrow and general AI. 
2. Understand the difference between predictive and analytical AI. 
3. Understand the difference between Deep Learning, Machine Learning, Probabilistic, Rule-based, and Hybrid AI models. 
4. Understand the difference between Neural Networks, Reinforcement Learning, Bayesian Networks, and Expert Systems. 
5. Understand the difference between unsupervised, semi-supervised, and supervised learning. 
6. Understand the differences between various kinds of GenAI, such as Large Language Models (LLMs) and text-to-image generators. 
7. Understand the differences between the main kinds of neural network architectures such as CNNs, RNNs, LSTMs, and Transformers. 

• Understand how to integrate AI with other applications and digital tools: AI capabilities are already impressive, but they can be further enhanced through integration with other digital tools. There are four main categories to consider in this respect: search engines, browser extensions, knowledge databases, and digital communication/collaboration platforms. Understanding how to integrate AI with these technologies can dramatically improve business operations, especially in terms of increasing workflow efficiency, facilitating more targeted and useful communication between teams, quicker and more relevant information retrieval and synthesis, more robust managerial oversight and security protocols, as well as more efficient meetings. There are many other benefits, and users will be better able to identify them if they know exactly what they want to use AI for. 

The Possession of AI-Specific Skills 

The following skills will be crucial as the AI landscape continues to evolve: 

• Learn to write good prompts: Contrary to the hype, we think prompt engineering will persist as a high-value skill of the future. While frontier AI models can autonomously generate sophisticated prompts, human judgment, intent, and creativity will remain necessary in contexts where such models are leveraged to enhance creative ideation, critical thinking, and the discovery of novel opportunities. 

• Learn to leverage conversational AI as a “thought partner”: Conversational AI models, like ChatGPT, can prove useful when leveraged as “thought partners.” In this context, such models can help users think more creatively about the problems they face, challenge their preconceived notions and biases, explore perspectives on novel issues from well-known experts, synthesize and identify connections between disparate ideas, formulate alternative strategies, and more quickly access information that corresponds with user intent via built-in personalized search and data analytics features. Though there are many other ways in which users can leverage conversational AI to think bigger, these offer a useful starting point. However, it’s worth noting that the ability to optimize these functions will depend on how good users are at prompting and whether the conversational AI models they leverage have multi-modal capabilities. 

• Learn to leverage embedded data analytics and personalized search features: Such features are still nascent in frontier AI models, however, they’re already proving enormously useful. Personalized search can improve the relevance and efficiency of search results, provide context-aware responses, handle ambiguous and complex search queries, reduce information overload, and enhance learning and discovery. On the other hand, embedded data analytics features can be leveraged for predictive analytics, trend analysis and reporting, data visualization for complex data, and real-time data analysis, all of which can enhance the efficiency and precision of data-driven decision-making processes. 

• Understand the differences between different kinds of AI tools: Some AI tools, like LLMs, are general-purpose technologies, while other AI tools, such as those used for medical image classification or financial forecasting are specialized (i.e., narrow). However, even within LLMs, capabilities vary by task-domain. Therefore, understanding which AI tools and their subsequent capabilities are suited to certain tasks is essential to maximizing the utility gained from them. 

• Learn to leverage low-code and no-code AI tools: Low-code/no-code AI tools have fueled the democratization of AI, and of creation itself, by making AI accessible to users without extensive programming backgrounds. Such tools have an incredibly vast array of applications, which are listed below: 

• Data analysis and visualization → making complex data more easily interpretable. 
• Predictive and trend analysis → creation of models that predict trends based on historical data in domains like financial forecasting and consumer behavior.   
• Chatbots and virtual assistants → customization of pre-trained models to suit specific needs, like customer service.  
• Automating business processes → automation of repetitive business tasks like document processing and data entry. 
• Custom application development → creation of customized AI applications that suit specific purposes such as project or inventory management.  
• Image and video analysis → enabling image and facial recognition, as well as video analysis in industries like security, marketing, and manufacturing. 
• Anomaly detection → streamlined creation of cybersecurity defense measures for purposes such as fraud detection, network security, and data access controls. 
• Personalized search engines → creation of systems that offer personalized user recommendations in domains like e-commerce and digital content creation. 

An Up-to-Date Knowledge of Current AI Applications and Use Cases 

The following strategies will help individuals develop and maintain an up-to-date knowledge of current AI applications and use cases: 

• Follow the most recent innovations in the AI landscape → Individuals can track the most recent innovations in the AI landscape by keeping up with frontier AI company press releases and reports from reputable tech media outlets, research institutes, and consulting firms like MIT Technology Review, The Future of Life Institute, and Mckinsey & Company. 

• Follow the most recent developments in AI regulation and policy-making → Most people don’t have the time or ability to read through complex regulatory and policy frameworks. However, individuals can keep up with the most recent developments by reading the regulatory briefs that major private and public institutions such as KPMG and the EU Commission produce in addition to leveraging GenAI content summarization capabilities to make regulatory and policy documents more digestible. Importantly, AI regulation is mostly reactive—AI typically progresses too quickly for regulation to be designed proactively (with some exceptions for high-risk systems and high-impact domains)—which means that regulations are designed and implemented in response to risks and benefits inspired by real-world AI use cases. In essence, understanding AI regulations enables individuals to better grasp real-world AI applications, and more broadly, an informed opinion on current and emerging AI legislation is critical to our collective ability to shape how the AI wave unfolds. 

• Understand the difference between open-source and API deployment → When an AI model is open-sourced, the whole model is released. This means that users can freely access and build upon the entire source code of the model. An API, on the other hand, is when a specific version of a model is released. Some of the source code may be freely accessible to users, but most of it will remain private. Understanding the difference between open-source and API deployment informs the array of risks and benefits specific to certain kinds of AI models. 

• Ask your friends and coworkers how they’re using AI in their lives → AI will affect everyone sooner or later. Understanding how friends and coworkers use this technology will push people to widen their perspectives and challenge any preconceived notions or biases they may have regarding AI use. Ultimately, this will help individuals enhance their ability to identify novel and existing AI use cases and applications, to ensure they continue providing value as the future of work remains uncertain.  

• Experiment with use-cases beyond frontier AI → Frontier AI is where all the hype is, but it doesn’t mean that impressive or useful AI systems and capabilities won’t exist elsewhere. In other words, AI isn’t just ChatGPT—specialized or narrow systems can also be remarkably useful and even revolutionary in some cases. For instance, DeepMind’s AlphaFold can predict protein structure with unprecedented speed and accuracy, and it’s already fueling the discovery of novel pharmaceuticals and medical treatments around the world.⁶ 

An Adaptable Mindset Via Continuous Learning and Experimentation

The following mental strategies will enhance individuals’ ability to maintain an adaptable mindset, especially as AI innovation increases uncertainty in the future of work: 

“What if and how might we questions are really critical to sustaining a person’s job—as it relates to artificial intelligence, how does this impact the work that I’m doing?”

• Learn to embrace uncertainty → The AI genie is out of the bottle—AI is here to stay whether we like it or not. Those who resist AI-inspired changes will quickly fall behind those who embrace them, and even more importantly, be far less able to influence the course of AI innovation. If we want to ensure a safe and beneficial future where humans and AI coexist peacefully and prosperously, we must embrace AI-driven uncertainty and learn to navigate the opportunities it provides us with. In Marca’s words, “What if and how might we questions are really critical to sustaining a person’s job—as it relates to artificial intelligence, how does this impact the work that I’m doing?” 

• Recognize the importance of critical thinking → The digital information ecosystem will only become more saturated, especially as AI-generated content proliferates. There’s also a salient possibility that future AI systems will be trained on AI-generated content, which may increase the risk that such systems output misleading or false information. Regardless, humans must continue to think critically about the information they interact with, more so than they do today and even in cases where it comes from reputable sources, or as Tony Doran, Co-Founder of SimplyPut AI wisely states, “Always be conscious of your current context.”

• Always assume that you will need to re-skill → The AI landscape is constantly changing, meaning that new risks and opportunities will continue to emerge regularly. In other words, there is no AI literacy finish line—individuals will either be more or less AI literate than others, but only those who persistently cultivate AI skills through constant experimentation with AI applications will be able to ensure they continue providing value in the future of work. People need to learn to relish, “the exploration of generating your AI literacy,” as Doran recommends. 

• Understand that no one knows what the future will look like → Despite how confident some experts and institutions are in their predictions of AI-driven future scenarios, no one knows with certainty what will happen. This can be scary to think about, but also very exciting. In essence, the array of possible opportunities that emerge in the future of work could be endless, and this empowers individuals to both identify and create AI-driven opportunities for themselves. 

AI Literacy: Why is it Important? 

Though some of us may envision a future of work where all human labor functions have been automated by AI, this view doesn’t accurately reflect how humans have interacted with automation in the past. According to Marca, “People should think about this [AI] as another tool rather than a job replacement.” Historically, automation has taken over dangerous, mundane, or repetitive tasks, effectively improving worker productivity and safety while also giving rise to new and profitable opportunities. In this respect, automation has also implicitly fueled a greater focus and demand for high-level and specialized skill sets, like critical thinking and creativity. We explore some examples below: 

• The Assembly Line: When Henry Ford perfected the assembly line, automation reduced the need for worker multi-tasking, decreasing worker fatigue and the frequency of workplace accidents. While the assembly line did automate much of the labor performed by unskilled and semiskilled workers, it also dramatically increased the complexity of workplace operations. This led businesses to develop new managerial positions that required specialized skill sets.⁵ 

• Automated Material Handling: More recently, the manufacturing sector has invested in “smart factories,” which leverage technologies like automated storage and retrieval systems as well as automated guidance vehicles to handle materials. These systems reduce the need for human-machine interaction, and subsequently, the probability of human error, which often contributes to workplace accidents.¹¹ Smart factories have also given rise to a number of new professions, such as digital twin engineers, robot teaming coordinators, and smart safety supervisors.⁹

• Collaborative Robots (Cobots): In high-risk task domains, collaborative robots have helped improve workplace safety while reducing the physical strain that many workers experience.¹ Imbued with a variety of sophisticated sensors, these robots can detect human presence and avoid workplace accidents that may arise due to human-machine interaction. The advent of this technology has fueled the creation of a new sector within the robotics industry in addition to professions like cobot programmers and operators, maintenance technicians, and human-robot collaboration coordinators. 

AI tools are transforming and expanding human labor rather than replacing it.

This positive view of automation doesn’t deny that historically, automation has caused significant job displacement among low and semi-skilled workers—it’s very likely that this trend will unfortunately continue into the future. However, the US Chamber of Commerce offers a more optimistic interpretation of AI and the future of work, indicating that AI tools are transforming and expanding human labor rather than replacing it.¹⁰ In fact, according to a 2023 OECD survey, which spanned over two thousand firms in various countries and sectors, approximately 80% of AI users note that AI has improved their work performance, with a mere 8% saying that it has negatively impacted their work.⁷ Importantly, this survey also highlighted that one of the main barriers organizations face with AI adoption is a lack of relevant skills, with half of employers reporting that AI facilitates the creation of novel tasks not previously performed by humans. 

Findings by the World Economic Forum and edX support a similar narrative, highlighting three positive trends for the future of work: 

1. AI adoption will facilitate the creation of new jobs: 49% of all organizations surveyed indicate a belief that AI adoption will drive job creation. This corresponds with current trends in the job market, which highlight that the fastest-growing professions are those that require AI and machine learning skills.¹² 

2. Businesses are increasingly prioritizing AI-specific skills: More than 85% of organizations have increased their adoption of frontier technologies, with the primary concern being increased investment in AI skills training to enhance how AI is leveraged to boost business performance.¹² 72% of executives believe their organizations should substantially increase investment in AI learning and development initiatives.³ Moreover, independent programs, like TeachAI, Education 4.0 Alliance, and UNESCO Thought Leadership, further demonstrate this concern, stressing the necessity of AI training and reskilling at a societal scale. 

3. AI is more likely to augment, rather than automate human labor: Despite what many may think, businesses are becoming less confident that AI will be able to automate all relevant work tasks—only 20% of workers believe that AI will be able to automate most of their job-related tasks.³ Moreover, companies continue to rank analytical and creative thinking as the most important skills for workers to possess. 

Despite these positive outlooks, the effects of AI on the future of work may nonetheless be inconsistent and challenging to predict. That being said, one thing is clear: work requirements are changing, and an increasing emphasis is being placed on the procurement of AI-specific skills and the identification of human skills that are unlikely to be automated. In fact, 87% of executives indicate difficulties in sourcing talent with AI skills, with 82% believing AI-skilled workers should have higher salaries and 74% believing that AI-skilled workers should be promoted more frequently.³ 

Though it may be unclear which human and AI skills will be critical in the future, those who begin cultivating AI literacy now will be more equipped to withstand and even benefit from the disruptive forces of AI as they impact every corner of our world. 

Generative AI and the Future of Work 

Before GenAI captured global interest and investment with the public release of ChatGPT in 2022, most AI-specific concerns regarding the future of work stemmed from discriminative AI. Discriminative AI is designed to recognize, understand, or classify data by reference to what it’s learned from a dataset. GenAI is designed to create new content, whether in the form of text, image, audio, or video, that’s similar but not identical to the data it was trained on. Though many GenAI systems also possess discriminative AI capabilities, the effects of GenAI on the future of work will be distinct. 

Current multi-modal GenAI models—models that can process and generate data across different formats, such as audio, text, and visual—have demonstrated capabilities that range beyond data classification and analysis. Such capabilities include code and application development, content augmentation and original content creation, definition and execution of business objectives (e.g, a go-to-market strategy), drug discovery, disease prediction, and medical treatment identification, as well as more general abilities like complex problem solving, reasoning, and the synthesis of novel ideas and information. The capabilities listed by no means encompass everything that GenAI is capable of, nor do they consider GenAI’s weaknesses, of which there are many. However, they do demonstrate that GenAI can accomplish or at least streamline tasks that were previously thought to be unique to human skill sets. 

Therefore, it’s likely that the effects of GenAI won’t only be felt among low and semi-skilled workers, but also among knowledge-based workers, who typically occupy positions that are more financially lucrative and require higher education. A 2023 study conducted by the University of Pennsylvania further emphasized this point, suggesting that 80% of jobs will have at least 10% of tasks altered or replaced by AI, while 20% of jobs will have at least 50% of tasks altered or replaced by AI.⁴ Even the C-suite is feeling these effects, with 49% of CEOs indicating that the majority of their roles could be automated by AI.³ 

If half of all work activities are automated, does this mean we’ll lose half of the labor force to automation? 

Knowledge workers spend around a quarter of their time on tasks that require natural language understanding, like the ability to acquire some kind of critical information through a conversation with a coworker.⁸ Currently, it’s estimated that GenAI can automate between 60% and 70% of these kinds of tasks, with long-term predictions suggesting that it could automate approximately half of all work activities between 2030 and 2060. A recent survey highlighted similar trends in the c-suite—CEOs expect that by 2025, 49% of workplace skills will no longer be relevant due to AI.³ If half of all work activities are automated, does this mean we’ll lose half of the labor force to automation? 

No, this 50% figure represents the impact of AI-driven automation across industries and domains. While some jobs will almost undoubtedly be fully automated in the future, the majority of workers will still have the time to develop new AI-specific skills and adjust their career trajectories accordingly. Even though AI is progressing rapidly, history has shown that most major technological advancements are followed by significant periods of gestation.² We consider some examples below: 

• Commercial Flight: The Wright Brothers took their maiden flight in 1903, and while planes were used in WWI, commercial flight didn’t become a possibility until the 1930s. Even then, average people couldn’t fly regularly or safely until several decades later. 

• The Internet: The foundations of the Internet were created throughout the 60s and 70s, materializing into what we now recognize as the Internet in the mid-90s. Even so, it’s only within the last decade that most businesses have adopted networked computing—the field of cloud computing is only about 15 years old. 

• Autonomous Vehicles (AVs): AVs have been in production for 15 years, but it’s only recently that deployment and adoption have occurred at scale. 

The adoption of general-purpose technologies follows a J-curve trend: initially, adoption occurs slowly and in a step-by-step fashion, then progresses rapidly toward general acceptance. In the context of GenAI, we are in the early stages of general acceptance, as illustrated by the number of new LLM releases since 2022: 

Graph generated using ChatGPT 4’s Data Analytics feature

Despite the fact that we appear to be in the general acceptance stage, most experts still classify our current GenAI models as specialized systems—systems that are narrowly task-oriented. Though they excel at replicating a variety of human behaviors throughout well-known task domains, they still exhibit biases and inconsistent performance, especially in novel contexts or within changing environments. This lack of robustness strengthens the case for AI augmentation over automation, especially since holistic and resilient solutions to these problems have yet to be developed. 

Moreover, most GenAI systems succumb to another prevalent problem: a lack of explainability. While such systems can display high accuracy rates across a variety of tasks, how they arrive at a particular output or decision is often difficult if not impossible to interpret. This presents a profound ethical hurdle to widespread adoption in business settings, especially in cases where GenAI is leveraged for high-stakes decision-making or novel problem-solving. Importantly, there’s no clear solution to the problem of explainability, and some experts continue to doubt whether one will ever emerge. 

“It’s incumbent that employees remain educated so that they can be relevant in the workforce, and use tools like AI to become more efficient or effective in the eyes of current or prospective future employers.” 

Even though GenAI is proliferating rapidly, demonstrating potential as both a disruptive and transformative force, it’s important to keep in mind that the majority of statistics discussed thus far indicate beliefs or attitudes concerning the future of work—a belief that something might happen doesn’t indicate that it will happen. Nonetheless, beliefs, although subjective, often reflect one’s experience of reality, in which there may be important grains of truth. One especially salient grain of truth is the evolving need for AI skills across multiple industries and domains. Workers who begin cultivating AI literacy now won’t only augment and develop new skills but also take tangible steps toward future-proofing their career trajectories. To summarize in the words of Marca, “It’s incumbent that employees remain educated so that they can be relevant in the workforce, and use tools like AI to become more efficient or effective in the eyes of current or prospective future employers.” 

Conclusion

As AI continues to evolve, novel challenges and opportunities will emerge, necessitating a future workforce that is highly adaptable, possesses AI-specific skill sets, and a proactive yet flexible learning mindset. 

AI literate individuals will be better equipped to navigate the uncertain conditions that AI innovation inspires, unlocking doors to new opportunities and ensuring the ability to continue providing economic value in the future job market. 

The call to action is clear: cultivate AI literacy now to harness the capabilities of AI, adapt to its progression, and contribute to a future where human and AI collaboration enhances innovation and productivity.

References

*note: references are ordered alphabetically by author name, with links provided where appropriate. 

1. The Arrival of Cobots to Industry Results in Significant Job Creation (AAA, 2020)

2. Why ‘The Future of AI is the Future of Work’ (Autor, Mindell & Reynolds, 2022) 

3. Navigating the Workplace in the Age of AI (edX, 2023)

4. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (Eloundou et al., 2023) 

5. The Assembly Line (Encyclopedia Britannica) 

6. Highly Accurate Protein Structure Prediction with AlphaFold (Jumper et al., 2021)

7. The Impact of AI on the Workplace: Main findings from the OECD AI surveys of employers and workers (Lane, Williams & Broecke, 2023) 

8. The Economic Potential of Generative AI: The Next Productivity Frontier (Mckinsey, 2023) 

9. Top 5 Future Smart Automation Careers in Manufacturing (SACA, 2020)

10. AI and the Future of Work: Preparing the Workforce for an AI-Driven Economy (US Chamber of Commerce, 2023) 

11. 4 Ways To Achieve Workplace Safety Through Automation (Whitley, 2022)

12. The Future of Jobs Report 2023 (World Economic Forum, 2023)