January 20, 2025: By Peter McAliney and Hoda Niaz. In the 21st century, artificial intelligence (AI) is no longer just a buzzword; it’s a transformative force impacting almost every industry. As AI technologies advance, they’re shaping the future of work in profound ways, and their effects are not limited to technical fields. Non-technical professionals need to understand and leverage these changes to stay relevant in their careers.

In this article, we’ll explore how non-technical professionals can adapt to the rise of AI, focusing on how AI will impact their work, the skills they need to develop, and the steps they can take to harness its potential.  We will provide some concrete examples that not only exhibit how it is currently impacting specific job roles but can also spur thinking of how you can think about it in job roles relevant to your career beyond the examples provided.

The takeaway is the same for all professionals: While GenAI is highly unlikely to take one’s job, a person who knows how to use GenAI productively almost certainly will. Now is the time to get “AI Ready.”

AI Is Now – Not “the Future of Work”

AI has already redefined the future of work. While it’s impossible to precisely predict the scope and timing of the changes in the labor market, professionals need to build their awareness to rapidly detect and respond to AI developments. AI implies a significant change across all industries, impacting not only technical jobs but also non-technical roles. In fact, as AI technologies become more integrated into our workflows, the ability to work alongside AI will become a crucial skill for all professionals, not just coders and engineers.

Why Understanding AI Matters

AI tools and technologies are increasingly integrated into workplaces, from chatbots to autonomous systems in healthcare, manufacturing, service industries, and logistics. By understanding both the branches and categories of AI, professionals can identify which tools are most relevant to their industries and enhance their work without delving into complex algorithms. As AI continues to evolve, understanding its historical perspective and equipping yourself with foundational knowledge and practical skills is vital for staying competitive in the modern workforce.

Understanding the General AI Landscape

A Historical Perspective

The broad and evolving AI landscape may seem complex, but understanding its core components helps non-technical professionals navigate it effectively. Familiarity with AI tools and their applications can improve productivity and efficiency without requiring deep technical expertise.

According to McKinsey, generative AI has the potential to contribute between $2.6 trillion and

$4.4 trillion annually to the global economy.1 It has emerged as a breakthrough category, with notable advancements like OpenAI’s ChatGPT and DALL·E, and Google’s Gemini (and the emerging DeepSeek). However, the rapid evolution of AI has led to the rise of agentic AI, a more autonomous and intelligent category. A historical chart of AI’s evolution—from early machine learning models to today’s generative and agentic systems—highlights the significant strides made in expanding AI’s functionality.

In order to get a better understanding for the evolution of and dimensions of AI, it helps to have an understanding of the key branches of AI (Table 1). Additionally, AI can be considered Narrow AI (Weak AI) or General AI (Strong AI). Narrow AI is currently the most common form of AI and is designed to perform specific tasks like image recognition, language translation, or customer service. Examples include Siri, Google Translate, and AI-powered chatbots. General AI a future vision for AI and aims to replicate human-level intelligence, performing any intellectual task a human can. While we have yet to achieve true general AI, advancements in agentic AI are steadily bridging the gap.

Combining AI with engineering, robotics creates autonomous machines capable of performing tasks in industries such as manufacturing, healthcare, and exploration.
  Machine Learning (ML)Natural Language Processing (NLP)        Robotics        Generative AI      Computer Vision
The foundation of AI and ML focuses on algorithms that enable systems to learn from data and improve their performance over time. It powers predictive analytics, enhances decision-making, and is widely used in finance, marketing, and more.NLP enables machines to understand and generate human language. Applications include chatbots, language translation, and sentiment analysis, facilitating seamless human-machine interactions.The foundation of AI, ML focuses on algorithms that enable systems to learn from data and improve their performance over time. It powers predictive analytics, enhances decision-making, and is widely used in finance, marketing, and more.Generative AI specializes in producing new, original content like text, images, and videos. Using models such as GANs and GPT, it is revolutionizing content creation, design, and personalized experiences across industries.This branch focuses on enabling machines to interpret visual data, such as images and videos. It is integral to advancements in medical imaging, facial recognition, and autonomous vehicles.

Table 1: Historical Branches of AI

Generative AI: Revolutionizing the Present and Shaping the Future

Generative AI, including models like ChatGPT, focuses on creating new, original content such as text, images, and code, using advanced models like generative adversarial Networks (GANs) and generative pre-trained transformers (GPTs). These tools are transforming industries such as marketing, journalism, design, and personalized experiences by automating tasks, generating ideas, and enhancing productivity. Their ability to produce innovative and contextually relevant outputs has made them some of the most popular AI technologies today.

Top Generative AI Products

Several standout tools dominate the generative AI landscape today:

Other, more specific purpose tools that have emerged are:

Each of these tools leverages large language models (LLMs) and excels in unique applications across industries. Generative AI remains a transformative force, but understanding the shift to agentic AI and its potential impact is crucial for staying ahead in an ever-evolving technological landscape.

The Transition to Agentic AI

The role of AI is growing beyond mere automation. As we move forward, the focus is shifting from generative AI to agentic AI — where AI is able to make decisions and perform tasks with minimal human intervention.2 This shift is moving us from the reactive use of AI to more proactive decision-making tools. AI systems are becoming increasingly sophisticated, capable of self- learning and adapting based on new information and experiences.

Agentic AI represents systems that can perform tasks autonomously, learn from experience, and make decisions. Unlike generative AI, which relies on user prompts to create content, agentic AI takes a proactive role, adapting to complex situations without constant human guidance. These systems are poised to revolutionize a broad array of industries by offering sophisticated decision-making capabilities.

Skills for the AI Age

Non-technical professionals must develop an understanding of AI concepts and tools that directly relate to their industries. Those who fail to embrace these changes may risk being left behind as the rapid adoption of AI technologies across sectors continues. According to a survey from the Society for Human Resource Management (SHRM), organizations are already adopting AI, and employees need to be prepared to work alongside it or risk becoming obsolete. The AI revolution is rapidly reshaping the job market, with projections suggesting that 25% of people may lose their jobs while another 25% could become millionaires. A striking example of AI’s swift progression is ChatGPT, which skyrocketed from 1 million users in just 5 days to 150 million users within two months.3

In the era of AI, there are both technical and non-technical skills that professionals must develop to stay competitive.

Technical Skills for Non-Techies

While non-technical professionals don’t need to be experts in coding or AI algorithms, understanding the basics of AI technologies will be important. These technical skills include:

Durable Skills (aka “Soft Skills”)

The rise of AI demands a focus on skills that have already been identified as important in the workplace but now take on an even more important role. While these have traditionally been labeled “soft skills,” they are more aptly named “durable skills” or “human skills” since they transcend industries, job roles, and, as the emergence of AI has clearly shown us, technologies. A recent study by America Succeeds, a 501(c)(3) nonprofit committed to engaging business leaders in modernizing education systems to drive equity and opportunity, looked at 82 million job postings across 22 sectors and found that durable skills represented seven out of the ten most requested skills called for in job postings (see Figure 1) 4.

Figure 1. America Succeeds, The High Demand for Durable Skills

Other skills that have surfaced as important in an emerging AI workplace are critical thinking, teamwork effectiveness, collaboration, self-awareness, self-management, adaptability and flexibility, entrepreneurship, and an aptitude toward lifelong learning.

It is important to note that when we speak of collaboration, it takes on a multi-dimensional perspective: AI tools are often collaborative by nature. Non-technical professionals should be able to work effectively with both technical and non-technical teams to leverage AI systems and achieve common goals.

These skills will be critical for adapting to the evolving work environment. Professionals who can blend traditional expertise with AI knowledge will have a competitive edge.

While generative AI tools, such as those integrated into workplace platforms following the launch of ChatGPT, are transformative, they rely heavily on the quality of prompts and guidance provided by users. This dependency means professionals cannot fully rely on AI to do all the work independently. Even OpenAI CEO Sam Altman has cautioned that generative AI systems are “still flawed, still limited,” often appearing more impressive at first use than upon deeper engagement.

AI in the Workplace … It’s Already Here

AI is already making a mark in numerous sectors, including healthcare, finance, retail, manufacturing, and education. It is impacting a variety of job roles, including marketing, program design, supply chain, risk management, human resources, and customer service. For example, in the healthcare industry, AI is being used to assist in diagnostics, patient monitoring, and even drug development, and in Finance, it is being used in risk management, fraud detection, and algorithmic trading. In human resources, AI is used in recruitment to analyze resumes, predict candidate success, and even handle employee onboarding. In the job role of marketing, AI tools help automate content creation, analyze consumer behavior, segment customer data, and personalize advertising campaigns.

According to a seminal report entitled “Artificial Intelligence in the Future of Work” by the National Academies (2024), one way AI will affect jobs is through its impacts on individual tasks5. The most relevant concern for workers is not whether AI will eliminate jobs, according to the committee, but how it will shape the labor market value of expertise. In some cases, the report predicts that AI will broaden the reach of workers rather than making their expertise superfluous.

While AI will not discriminate what jobs it can effect, it will impact them in different ways. Higher-paying jobs in fields like computing, business and finance, engineering, and law are highly exposed to the impacts of AI, but the technology is most likely to augment or take over discrete tasks rather than entire jobs. On the other hand, lower-paying office jobs like insurance claims processors, legal secretaries, bookkeepers, and administrative assistants are now much more likely to see their roles fully automated – certainly a clarion call to invest in professional development activities.

According to the McKinsey report, they expect an additional 12 million occupational shifts by 2030. For instance, customer-facing roles that took a hit during the pandemic, such as those in customer service, sales, and general office support, will most likely continue to decline as the reliance on technical solutions like gen AI increases. Reduced need for human workers in customer-facing roles is expected to be responsible for around 84%, or almost 10 million, of McKinsey’s forecasted 12 million occupational shifts. On the other hand, roles in business, legal, healthcare, transportation, and, notably, STEM professions are showing signs of continued growth. According to McKinsey, ”these categories are expected to see fewer than one million occupational shifts by 2030:

Along with shifting occupational categories, Generative AI will have a similar impact on the nature of work that these roles will be responsible for. It’s expected that Generative AI may automate work activities that currently take up to 70% of workers’ time.6 Exactly which activities those will be and who – employee, employer, or both – will enjoy the benefits is yet to be seen. While AI has the potential to speed up tasks, increase productivity, and improve the quality of outputs for human employees, some of the time it frees up will allow employees to tackle more creative and strategic tasks.

Given the way AI has reshaped industries by automating repetitive tasks, enhancing decision-making, and creating new opportunities for innovation, changes have already begun in a variety of industries. Table 2 outlines some key impacts of AI across industries, necessary skills, and the distinction between technical and non-technical roles7.

Table 2: Examples of AI Impacting Job Roles in the Industry

As AI continues to evolve, it will also create new roles in industries such as AI ethics, AI operations, and AI training. These roles will require a combination of technical and non-technical skills.

Preparing Your Career in an AI World

The key to staying ahead of the curve is continuous learning. Professionals must proactively seek opportunities to develop new skills and stay updated on AI developments. Tiffany Hsieh, director of innovation programs with Jobs For the Future’s (JFF) Center for Artificial

Intelligence and the Future of Work endorses the findings of Brooking’s report that the successful 21st-century professional will remain adaptive to real-time changes in the job market and will focus on lifelong learning.

Step 1 – Determine the Impact of AI on Your Current (or Intended) Career

A first step will be to become aware of the potential for AI to disrupt your job – depending on your career, job role, and skills that are employed in this job role, AI will impact it differently. Two perspectives that you will find helpful are studies conducted by Indeed and the World Economic Forum.

Indeed (2024) identified and assessed more than 2,800 unique work skills to determine how AI will impact them (Figure 2). If your occupation is not in the Indeed analysis, look to an identified career that most resembles yours. Generally, the lower the share of skills in a given job that require physical execution, the higher the share of skills that could possibly or likely be replaced by GenAI. Additionally, a strong ability to solve problems also correlates with a higher likelihood of eventually replacing a human.

It is important to note, however, that GenAI isn’t particularly strong at solving problems using skills found in many common jobs. Almost 78% of skills commonly found in a typical

accounting occupation, for example, were rated as “possible” or “likely” to be replaced by GenAI, and GenAI said its problem-solving skills were “good” for 30.5% of skills common to accounting job postings. But accounting occupations are an outlier — it is the only occupation analyzed in which GenAI said it was at least “good” at problem solving for at least 30% of

common skills. For most other occupations analyzed, the Indeed model’s problem-solving abilities were more limited, and the share of skills likely to be replaced by GenAI was also lower.

Figure 2: Impact of AI on Job Roles – Indeed

A second perspective to ground your thinking is the World Economic Forum’s “Future of Jobs Report 2025.” They collected insights from 1,000 global employers, 14 million workers, 22 industry sectors, and 55 economies to provide a look at the impact of AI across a number of job categories and jobs (Figure 3). Their methodology defined three categories: jobs most vulnerable to AI replacement, jobs where AI can assist but not replace, and jobs where AI cannot replace. As with the Indeed categorization, it will be helpful to see where your career lies in this model to see what actions you can take – and how soon you must act.

Figure 3: Impact of AI on Job Roles – World Economic Forum

Step 2 – Don’t Get Overwhelmed – Contextualize Your Game Plan

Recognizing that AI will impact jobs at a task and subtask level, determine where you want to focus your attention – don’t get overwhelmed! According to Marc Zao-Sanders in a recent HBR article, the potential of this fusion of human and AI capabilities becomes clearer when one takes a more nuanced view of skills and how skills relate to jobs. The author, in speaking to hundreds of experts, consumers, and skeptics of AI over the past few years, found four strongholds for humans that keep coming up (see Figure 4):

Begin small. identify those areas and skills – what I call “Strongholds of Durable Skills” – in the framework presented in Zao Sanders model to develop.

Figure 4: Stronghold of Human Capabilities

Step 3 – Start Working Your Plan

Now that you have developed your strategy, start working your plan in a systematic fashion. Start testing AI in your daily tasks – one at a time. Follow trusted voices in your industry. Build your personal playbook of what works. Focus on learning through doing, not just reading.

Consider the following steps as you developing your AI skills:

  1. Start with the Basics: Understand what AI is, how it works, and the different types of AI. There are plenty of online resources that explain these concepts in simple terms.
  2. Learn AI Tools: Familiarize yourself with popular AI tools that can help automate tasks and improve efficiency in your work.
  3. Develop Data Skills: Learn to work with data, interpret it, and make data-driven decisions.
  4. Join AI Communities: Participate in online communities and forums to stay updated on the latest AI trends and tools.
  5. Practice Problem-Solving: Use AI tools to solve real-world problems and make your workflow more efficient.
  6. Storytelling: Learn how to make your data tell a story that will resonate with your partners, prospective customers, and colleagues in other areas of your organization.

Once you’ve built a foundation, continue to deepen your AI knowledge. Start refining your prompts and begin creating chained prompts. For example, an answer generated in response to a loose prompt may change, by a little or a lot, when asked the same way a second time. More advanced queries – or asks for help with more advanced tasks – will require more advanced prompt-writing skills. It is unrealistic to expect that you will learn the kinds of advanced prompt- engineering skills required to extract the most value from today’s GenAI without some kind of guidance. Enroll in the expanding range of AI courses that are now available. Depending on your comfort level with learning new technologies, you can consider two options (or some combination therein):

Do note, that you will have to learn some of the basic technology that is driving AI to fully employ it even in ‘non-technical’ roles. However, if you find that you are enchanted by the technology driving AI, you can take more advanced AI and Data Science courses.

Closing Thoughts: Embrace the AI Revolution

AI is no longer a far-off concept—it’s here, and it’s transforming industries, jobs, and skill sets. While the technical fields will experience the most direct impact, non-technical professionals must also adapt to thrive in the AI age. By developing a blend of technical and soft skills, staying informed about AI trends, and embracing the tools that AI provides, non-techies can ensure they remain valuable contributors in the workforce.

AI is not just for engineers and data scientists—it’s for everyone. Embrace the change, learn the necessary skills, and use AI to unlock new opportunities in your career.

Take the Next Step in Your AI Journey with Rutgers

As AI continues to transform industries, it’s essential for professionals and organizations to stay ahead. Rutgers University offers a range of specialized programs designed to help you leverage AI in your field, whether you’re an individual looking to build your skills or a company aiming to integrate AI into your operations.

Explore the following opportunities:

Whether you’re looking to advance your career or integrate AI into your business strategy, Professional and Executive Education at Rutgers (PEER) programs are here to guide you on your journey. For more information or to see how PEER can support your own personal or organization’s AI learning journey, contact Dr. Peter McAliney at peter.mcaliney@rutgers.edu, visit our website at https://professional.rutgers.edu, or call 856-225-6685.

References

Chui, M., et al. (2023, August 30). The economic potential of generative AI: The next productivity frontier. McKinsey and Company.

Accenture. (2024). Work, workforce, workers: Reinvented in the age of generative AI. Technology trends 2024.

Rutgers University Chapter of the Society for Human Resource Management (SHRM). (n.d.).

The AI revolution [Presentation].

America Succeeds. (2024). About durable skills.

National Academies of Sciences, Engineering, and Medicine. (2024). Artificial intelligence and the future of work. Washington, DC: The National Academies Press.

Chui, M., et al. (2023, August 30). The economic potential of generative AI: The next productivity frontier. McKinsey and Company.

OpenAI. (2024). Table 2: Key impacts of AI across industries, necessary skills, and the distinction between technical and non-technical roles. Retrieved from ChatGPT.