Everywhere we turn these days, we see another news story, TV show, or podcast mention about the advances in AI. The narrative runs from it’s going to improve our lives to huge job displacements and, finally, the rise of Skynet, an homage to the Terminator movie machine-takeover of the future. These are valid concerns, but I think they are all worst-case scenarios. The scenario I am most concerned about is the lack of GenAi skills to mitigate all of these scenarios adequately.
A Lack of AI Skills
As industries rush to adopt AI technologies, a significant AI skills gap has emerged, marking a discord between the demand for AI expertise and the available supply of skilled professionals. This gap has steadily widened with the rapid advancements in AI and machine learning technologies. In research from Salesforce this past July, they saw that a large percentage of desktop productivity workers don’t know how to use GenAI.
Source: Salesforce: Generative AI Snapshot Research Series
The burgeoning demand for skilled personnel capable of developing, managing, and optimizing these complex systems underscores a pressing industry challenge.
The Evolution of Automation: Generative AI’s Impact on Knowledge Work
Historically, automation technologies predominantly influenced tasks that were physical in nature. However, with the advent of generative AI, we’re on the brink of a shift. Knowledge-intensive sectors—including education, law, technology, and the arts—are now in the limelight. The reason? Generative AI’s unparalleled prowess in deciphering patterns in natural language and dynamically applying them.
This doesn’t just mean faster processing—it signifies a reimagining of roles and responsibilities, particularly in tasks that require decision-making and collaboration. Professionals in these sectors may witness parts of their roles being streamlined or automated sooner than anticipated. With AI’s growing capability, what was once thought to be exclusive to the human intellect is now within the grasp of algorithms.
The Current Magnitude of the AI Skills Gap
The AI skills gap is not a novel issue, but its dimensions have gradually broadened. A recent study by Deloitte highlights that 68% of companies report a moderate to extreme AI skills gap, indicating a significant demand for “AI builders” who can create AI solutions. Another study underscores that while 93% of US and UK organizations prioritize AI, over half lack the in-house talent necessary to execute their AI strategy. This talent shortfall will drive 55% of IT organizations to invest in AI skills by 2023. The demand for AI-skilled workers in China is predicted to grow sixfold by 2030, with a glaring shortage of about four million skilled individuals. These statistics epitomize the global scale and the growing magnitude of the AI skills gap.
Implications of the AI Skills Gap
The repercussions of the AI skills gap are far-reaching, affecting economic growth, organizational competitiveness, and workforce dynamics. The skills gap could potentially result in a loss of $8.5 trillion to the US economy alone by 2030. Furthermore, 56% of hiring managers foresee that technological interventions like AI will necessitate a major shift in the skill sets required from employees.
Addressing the Gap Through Education and Training
Education and training constitute a cornerstone in bridging the AI skills gap. Online courses, boot camps, and university programs are pivotal in imparting the requisite skills to individuals. Though I am more of a fan of learning directly from cutting edge resources than actually pursuing a degree around AI. Furthermore, businesses channel investments into training programs to upskill their existing employees.
A notable initiative is the “InSkilling” service launched by InStride, which replaces traditional degree requirements with necessary skills for specific job roles, broadening the pool of qualified talent for in-demand jobs.
Leveraging AI to Narrow the Gap
Ironically, AI itself forms part of the solution to the skills gap. AI-powered tools can assist in identifying skills adjacencies, thus closing the skills gap. Moreover, predictive analytics and generative AI are employed to anticipate future skills needs and formulate training programs accordingly.
The Rising Demand for Generative AI Expertise in Organizations
While organizations are charting their generative AI trajectories, there’s an emerging realization: the acute necessity for employees well-versed in generative AI. As early adopters start reaping the benefits of generative and other applied AI tools, the chasm between the available talent and the demand for such expertise persists.
Organizations must hone their talent management strategies to remain competitive in this rapidly evolving landscape. This entails hiring the best in the field of generative AI and curating enriching work environments that encourage growth, ensuring they attract and, more importantly, retain these valuable assets.
Incorporating a Skills-Based Approach
Transitioning from a degree-centric to a skills-based hiring model is another pragmatic approach to addressing the skills gap. This paradigm shift values the skills and experiences of individuals over formal education credentials, thereby expanding the talent pool and fostering more inclusive hiring practices. According to a study by TestGorilla, companies using skills-based hiring are experiencing dramatic improvements in performance across five key metrics.
Source: TestGorilla
Conclusion
The AI skills gap is a complex issue requiring a concerted effort from educational institutions, businesses, and individuals. Investment in education and training, leveraging AI to identify and fill skills gaps, and adopting a skills-based hiring model are critical strides toward bridging the AI skills gap. By embracing these actionable strategies, we can inch closer to fostering a more skilled workforce capable of harnessing the immense potential of AI in propelling organizational and societal advancement.
