Every major technological innovation brings with it an evolution in the global workforce. The main difference with today’s developments in AI is how fast those changes are taking place — and identifying the skills and adjacent skills people will need to keep pace.
While the Industrial Revolution took 150 years to fully materialize, the digital revolution took 25. Today’s industry experts are anticipating that generative AI will change the way we work in under five years.
We’re only at the beginning of understanding how generative AI, like ChatGPT and Bard, will evolve with our jobs, but we do know it holds tremendous potential for transforming the ways people work.
RELATED CONTENT: Learn about our approach and what goes into Responsible AI at Eightfold.
AI’s impact on the workforce
While much of the public discourse has been about how AI might replace human workers, it’s important to remember that technological progress doesn’t just make jobs redundant — it also creates new ones, and those opportunities are likely to grow for a long time.
This progress also affects all job functions. For example, think about some of the current top skills in marketing, including proficiency in using Google Analytics, content management systems like WordPress, digital advertising platforms, and focusing on the user experience.
Or in HR, where recruiting and workforce management increasingly require proficiency with automation, human capital management (HCM) systems, and digital social media campaigns. Tech innovation is impacting all functions and technical skills are emerging in non-traditional places.
According to Goldman Sachs research, approximately 300 million jobs could be affected by the latest wave of AI. In the United States and Europe alone, approximately two-thirds of current jobs “are exposed to some degree of AI automation.”
In the World Economic Forum’s 2023 “Future of Jobs Report,” they state that 23% of jobs will change, with 44% of workers’ core skills changing within the next five years.
Technology adoption will be a key driver of business transformation over that time period. More than 75% of companies will look to adopt new technologies with big data, cloud computing, and AI high on their lists.
To meet these growing demands, six in 10 workers will require training before 2027, but only half of workers are seen to have access to adequate training opportunities today. The fastest-growing jobs will be AI and machine learning specialists, with the top skills being analytical and creative thinking.
For instance, the financial services industry considers AI and big data to be increasing in importance by up to 86% for its workforce, from now through 2027.
Take software engineering, where AI has had the earliest foothold. Generative AI tools will play an increasingly larger role, and with it, demand new approaches to these roles — and new technical skills. Although some jobs will likely become less prevalent, like entry-level coders, the net effect is expected to be a positive creation of jobs.
Jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new professions following technological innovations accounts for the vast majority of long-run employment growth.Beyond building AI-driven tools, engineers will need to integrate AI into their daily work and career planning to remain competitive:
New roles and skills in the data domain will likely grow rapidly, as vast data sets are needed to power effective generative AI.
Engineers will be expected to use new AI-based coding tools and think about how to develop new products around these tools to stay competitive.
Product managers will need to determine how to best use generative AI during the development process.
New companies will be created within the AI ecosystem, much like what came out of the creation of iOS.
Developing a workforce for the hottest emerging AI jobs
The enormous growth in AI adoption will require a large increase in related roles and in people with the skills to build and operate the new technologies. Hiring for AI jobs has already grown 32% in the past few years. But there are not enough qualified applicants for vacant positions, and that talent gap will only grow.
So what are the fastest-growing AI roles, what skills do they require, and how will organizations fill their AI needs?
Important and growing AI roles, measured by job listings
The most important skills for those roles, based on job descriptions
Adjacent skills that could be most easily be built on to increase the qualified pool of talent
The AI explosion could understandably cause anxiety that our workforce isn’t ready, and innovations that could transform the global economy will be underutilized while their potential to create jobs stagnates. But with these insights, we can help reframe the generative AI revolution to better understand the opportunities it presents.
We may not have enough workers with specific primary skills for these new jobs, but we have many people who can easily learn new AI-related skills based on adjacent, transferable skills they already have.
To truly plan for the future, every organization needs to take a critical look at which skills it needs and how to acquire them. Taking a critical look at your workforce’s skills will be imperative for any business plan going forward. This approach ensures that your organization has the right talent in place to achieve its business objectives.
If it’s difficult to find people with the primary skills, hiring managers can expand the talent pool and look for people with adjacent skills. Using the concept of adjacent skills, business and talent leaders can identify the best people to learn new skills, based on what they already know and their potential to learn. By locating individuals who already meet a high percentage of the requirements of the role and would only require a small amount of additional training, organizations could find a great fit for the role while minimizing costs, time, and effort.
For instance, your talent pool (internally or externally) might not have experience working with the programming language Scala since it usually is reserved for massive projects that need many resources and parallel processing. However, according to our data, if a candidate knows similar programming languages like Apache Spark and Hadoop, they’ll be able to upskill themselves and learn Scala with relative ease.
Let’s take a look at three of the fastest-growing new types of jobs in the age of AI — and the adjacent skills that can help identify talent for upskilling.
Job No. 1: Prompt engineer/Large language model engineer
Prompt engineering is “a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results. As in most processes, the quality of the inputs determines the quality of the outputs. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual,” according to this definition from the Four Week MBA.
For instance, if the primary skills needed include Python, Agile methodologies, and large language models, people with the below adjacent skills could be easily upskilled or reskilled to learn those main functions.
Job No. 2: Staff data engineer
Staff data engineers have a wide range of responsibilities related to owning, mapping, and building infrastructure for large data warehouses, designing data models, and often leading and setting strategy for data teams. Generative AI is powered by large and ever-growing data sets, so this role is central to effective AI innovations.
If data science and Scala are the primary skills, adjacent skills like machine learning and Apache Spark could help potential candidates upskill.
Job No. 3: Artificial intelligence product owner
Artificial intelligence product owners are the tactical engine driving the development of AI-powered products. They translate the product team’s strategy and vision into detailed action items, prioritize development backlogs, and oversee the workflow and collaboration of cross-functional teams, among other responsibilities. They’re a highly specialized version of a general software product owner role, and are most common in organizations that use Agile methodology.
For this role, if machine learning, Hadoop, and AWS are primary skills, people with any of the adjacent skills, like experience in algorithms or data science, could potentially upskill to take on this role.
RELATED CONTENT:Read about our latest solution, Skills-based Talent Planning, and how it can help you create a future-ready workforce.
How should organizations implement AI?
Along with operational transformation, skills-based hiring and identifying key skill adjacencies will be essential to organizations eager to embrace the AI revolution. But building new processes and evolving workforces are far from the only challenges that business and talent leaders will face in successfully adopting AI.
As new potential uses of AI are discovered daily, the discussion around AI is as likely to be characterized by fear as by excitement. The good news and bad news of AI is that it can replace human workload, meaning that many people are rightfully worried that AI will make their jobs obsolete before they can learn how to perform any of the new jobs it creates.
Organizations implementing AI need to be sensitive to this, and deliberate in how it is talked about and rolled out. For example:
Start small. Focus on one function or solution and show the relevant teams how the AI can make their jobs easier, freeing them up to do more strategic or substantive work. When that smaller effort creates proof points and buy in, build on it with other AI implementations. (Read more great advice on this from Amazon’s AI Product Lead Greg Coquillo)
Make sure everyone involved, including leadership, is very clear on why AI is necessary for the business. These technologies are still new. Following hype isn’t a justification for adopting anything.
Use it as an opportunity to nurture and upskill valuable employees. Give talented people the support to learn new skills and solutions rather than assume you need external folks who are already experienced.
As roles and functions evolve, consider a move toward skills-based talent planningthat will help create more equitable access to the fastest-growing jobs of the future.
Sania Khan is the chief economist at Eightfold AI, the AI-powered platform for all talent, and the author of the upcoming book Think Like an Economist. She previously worked for the U.S. Bureau of Labor Statistics.
