AI and the jobs-to-skills transition

Imagine a world where hospitality workers, instead of finding themselves out of work with the closure of a hotel, seamlessly reskilling and moving into solar-panel installation roles, or where a fossil-fuel engineer realizes that many of their skills match up to a role in renewable energy. Well, you don’t need to imagine it because transitions like this are the future of work.

That’s what Mohan Reddy, SkyHive’s co-founder and Chief Technical Officer, and Associate Director of Technology at Stanford's Human Perception Lab, had to say on a new podcast. "AI will help people see all that they can do, based on skill proximities," says Reddy.

In the podcast, Reddy talks about how AI can be used to help move a company from a focus on jobs to a focus on skills, and how reskilling in the age of AI can help talent teams overcome many of the challenges that will face the workforce in the next few years. He also talks about the ethics of AI and whether AI is good, bad, or somewhere in between.

In this article, we’re going to touch upon some of the points raised in the podcast in the context of AI’s impact on the future workforce. What are the risks, challenges, and opportunities that talent leaders need to contend with?

What is the jobs-to-skills transition?

The jobs-to-skills transition is a fundamental shift in the way we think of work. Traditionally, employment has been defined in terms of specific jobs or roles, each with a set of tasks and responsibilities. With the proliferation of technology, however, there’s a growing understanding among organizational leaders that focusing solely on jobs is an inefficient approach to talent management.

Instead, organizations are increasingly adopting a skills-based approach to talent management. This is where a workforce’s capabilities are defined and described by the specific skills required to perform individual jobs. In a skills-based organization, less emphasis is placed on qualifications in favor of a preference for actual, demonstratable skills.

This skills-based approach helps to promote internal mobility and continuous learning and is largely driven by AI-powered technologies. These provide organizations with the granular levels of insight needed for developing long-term talent management strategies that can stand up to the disruption that AI and the wider digital transformation are causing.

Risks of AI’s implementation in the workforce

Task replacement and job displacement

AI has the potential to automate a wide range of tasks across various industries, leading to significant job displacement. This is something we’ve discussed before. As AI technologies become more sophisticated, they can perform tasks that were previously the sole domain of human workers, such as data analysis, decision-making, and even creative tasks.

Many argue that this task replacement could result in the elimination of certain job roles, particularly those involving routine and repetitive tasks. According to a report by the World Economic Forum, by 2025, machines and algorithms are expected to displace 85 million jobs globally, while creating 97 million new roles more adapted to the new division of labor between humans, machines, and algorithms.

The trend we’re noticing at SkyHive is that AI is more likely to replace tasks rather than entire job roles. In administrative roles, for example, AI can be used to automate repetitive tasks like data entry and scheduling. This frees up employees’ time which can then be focused on more important responsibilities. The scale of this impact will naturally vary across different sectors, with some roles more susceptible to automation than others.

Further widening of skills gaps

The widening skills gap is our most pressing global challenge, and the rapid adoption of AI is only exacerbating this problem further, creating a growing divide between the skills workers currently possess and those needed to thrive in an AI-driven economy. As AI innovations continue to hit the market and the underlying technology becomes more advanced, there’ll be an increasing demand for advanced technical skills, such as programming, data analysis, and machine learning, as well as soft skills like critical thinking and problem-solving.

According to the World Economic Forum's Future of Jobs Report 2023, 61% of global workers will require retraining by 2027 to meet the demands of the new job market. This widening skills gap underscores the urgent need for educational institutions, governments, and organizations to collaborate on providing targeted training and development programs to equip the workforce with the necessary skills for the AI era.

Unethical implementations

The integration of AI in the workplace raises several ethical concerns that need to be addressed to ensure fair and responsible use of technology. One major issue that we’ve covered before is bias in AI algorithms, which can lead to discriminatory practices if not properly managed. AI systems trained on biased data can perpetuate and even amplify existing prejudices, affecting hiring decisions, loan approvals, and law enforcement activities.

The widespread implementation of AI also poses significant privacy concerns, as these systems often require access to vast amounts of personal data to function effectively. Ensuring that this data is collected, stored, and used in a manner that respects individuals' privacy rights is crucial. Additionally, the decision-making processes of AI systems can lack transparency, making it difficult to understand how certain outcomes are reached. This opacity can undermine trust and accountability, highlighting the need for clear guidelines and regulations to govern the ethical deployment of AI in the workplace.

Challenges in adapting to AI in the workforce

Organizational resistance

The risks that we’ve outlined, in addition to other factors, can unfortunately lead to organizational resistance to AI adoption, and this can be an extremely difficult challenge to overcome. Resistance to change is often rooted in fear of the unknown, concerns about job security, and a lack of understanding of AI technologies. Employees may worry that AI will replace their roles, leading to reluctance to embrace new systems and processes.

There’s also the problem of organizational inertia—a situation whereby existing workflows and practices, which are deeply entrenched into organizational DNA, can impede the adoption of more advanced technologies. To overcome these challenges, organizations must prioritize clear communication and education about the benefits and implications of AI. Change management strategies should include engaging employees early in the adoption process, providing comprehensive training programs, and fostering a culture of continuous education and learning.

Workforce readiness and skills

Getting your workforce “ready” for AI is another big challenge. AI technologies are complex, and many require specialized skills to manage effectively. At both leadership and employee levels, there’s usually a shortage of individuals with the necessary skills in AI and complementary areas like data science.

At a leadership level, for example, decision-makers might lack a deep understanding of the tech that’s being implemented and the implications it has on strategy. This could lead to a misalignment between AI efforts and long-term goals. Similarly, at the employee level, individuals may lack the technical skills and knowledge needed to work with AI tools effectively.

It will therefore be necessary for certain individuals who occupy roles that are likely to be automated by the AI tech that you’re implementing so that a) they’re not displaced by the technology and b) they know how to use it in their roles. To prevent being caught off guard, talent leaders should therefore conduct skills assessments before any AI implementation to identify skills gaps and tailor training programs for those who are going to be affected. You may also consider collaborating with educational institutions and leveraging online learning platforms to facilitate continuous skill development. By investing in workforce readiness, companies can ensure their employees are prepared to adapt to new roles and responsibilities through internal mobility.

A shortage of AI talent

This largely relates to the skills gaps that we’ve throughout this article but it’s a big enough challenge to merit a dedicated mention. Organizations worldwide in all sectors are experiencing a general talent shortage that, by 2030, could be a shortage of more than 85 million people.

This shortage hampers the ability of businesses to effectively implement AI technologies and capitalize on their full potential. The demand for AI expertise spans various roles, including data scientists, machine learning engineers, and AI ethicists, yet the supply of qualified professionals is insufficient.

This talent deficit not only slows down AI adoption but also exacerbates the competitive divide between organizations that can attract top AI talent and those that cannot. Addressing this shortage requires a concerted effort to develop robust educational programs, invest in continuous learning and development, and create attractive career pathways to nurture and retain AI specialists.

Opportunities presented by AI

Better employee development, engagement, and retention

AI can be a boon for talent teams who utilize it effectively. AI tooling can, for example, be used to improve employee development by assessing individuals’ skills and career aspirations for personalized career planning. They can also be used to analyze current workforce skills and identify gaps that need to be addressed through training and development programs.

Sentiment analysis is another interesting AI application in the talent context. AI can be used to monitor and analyze employee feedback and the general mood from various sources, such as surveys, emails, and social media. This can help talent teams to identify and address potential issues before they escalate. All this together makes for better retention prospects which, in the current market, is not something to be sniffed at!

Fine-tuned recruitment processes for the modern era

AI is leading the charge in the shift from job roles to skills. AI-driven talent acquisition systems can, for example, analyze candidate data at scale and identify individuals with the exact skills needed for vacant roles. Rather than matching candidates to job descriptions, these systems assess the actual competencies and potential of applicants, ensuring a better fit for the organization's needs.

Paired with a skills intelligence platform like SkyHive, AI-powered talent acquisition tools can provide talent teams with a clear view of the skills needed for their vacant roles. This information can then be used to visualize matches and gaps in your current workforce, giving you the right foundation to build a workforce plan.

Listen to the podcast now

If you would like to learn more about how AI and the shift in focus from job roles to skills is redefining the future of work, we recommend tuning in to Reddy’s podcast. Produced in collaboration with Kryterion, which is in the testing and certification business, Reddy talks about:

• How AI can be used to help move a company from jobs-based to skills-based.
• What's next for Generative AI?
• Reskilling in the age of AI.
• The four, siloed parts of the workforce ecosystem
• The ethics of AI.
• His opinion about whether AI is good, bad, or somewhere in between.

Listen to Mohan Reddy on the podcast here.