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Why aren’t there enough AI professionals to fill open roles? What parts do employers and education facilities have to play in the AI talent crisis?
As demand for AI talent soars, companies and universities face a double crisis: a severe shortage of AI-skilled workers and demographic disparities in AI training. These issues require an overhaul of AI workforce development.
Continue reading for a closer look at the AI talent shortage.
As AI Jobs Grow, Talent Can’t Keep Up
A November Randstad report reveals a critical mismatch between AI adoption and workforce preparation: While demand for AI-skilled workers has increased fivefold in the past year and 75% of companies are implementing the technology, just 35% of workers have received AI training. Disparities exist across gender and age groups in AI training, and this gap between AI implementation and worker preparation threatens to create widespread and uneven labor shortages across industries.
The AI-skills crisis is severe. A 2022 Deloitte study identified just 22,000 AI specialists worldwide, even as the World Economic Forum projected a need for 97 million AI-related jobs globally by 2025. The stakes are particularly high given AI’s projected impact on the workforce. Already, 63% of US companies—from insurance to retail—report that AI and machine learning represent their largest skills deficit. Both industry and higher education appear ill-equipped to address this crisis.
AI Training Disparities
Randstad’s report reveals companies are failing to provide effective, equitable AI training across their workforce. Men make up 71% of AI-skilled workers—with the gap widening to 76% in advanced areas like Deep Learning. This disparity reflects systemic training inequities: Just 35% of women have been offered opportunities by their employers to use AI in their roles, compared to 41% of men—and they’re 5% less likely to receive AI skills training.
Age-based disparities are equally problematic: While 45% of Generation Z workers receive AI training opportunities, only 20% of Baby Boomers do.
AI Issues in Higher Education
As companies struggle with equitable AI training, universities face an even more complex challenge: They must dramatically increase AI graduate numbers to meet industry demand and develop programs that help address disparities revealed in Randstad’s workplace data. Several significant barriers stand in their way:
- Enrollment constraints. Even prestigious universities can train only small numbers of AI specialists—Carnegie Mellon’s entire engineering department hosts just 1,600 master’s students.
- Faculty shortage. Universities haven’t created enough new computer science faculty positions to meet surging student demand for AI education.
- Training lag. Many education programs are just starting to incorporate AI, with fewer than 25% of education school leaders confident their faculty can prepare future teachers to use the technology.
- Economic hurdles. Universities face millions in upfront costs to develop online AI programs.
- Access barriers. Traditional graduate-level AI programs require full-time study, relocation, and over $100,000 in tuition, creating systemic obstacles that can limit enrollment and demographic diversity.
| Is AI Having a Positive Impact? While the rapid advancement of artificial intelligence raises eyebrows, its proponents argue we’re already reaping transformative benefits across various sectors. For example, in health care, AI can serve health care providers as a tireless, hyper-observant assistant. In the radiology lab, AI tools analyze medical images and data with superhuman accuracy, enabling doctors to catch diseases in their earliest stages and put together more effective treatment plans. In the pharmacy, AI tools can tailor treatments to a patient’s unique genetic makeup and medical history, which increases the chances of successful outcomes. In transportation, AI is paving the way for safer and more efficient systems—or has at least started to move in that direction. Self-driving cars powered by AI algorithms are inching closer to reality. These AI-powered vehicles promise to reduce traffic accidents caused by human error. Smart traffic systems could make rush hour gridlock a thing of the past by optimizing traffic flow and preventing congestion. Behind the scenes in the logistics industry, AI tools optimize delivery routes and predict when vehicles need maintenance. This streamlines supply chain operations in ways that save both time and money. Proponents of artificial intelligence say the technology could augment human decision-making across domains. AI can sift through mountains of data to spot patterns invisible to the human eye. From financial markets to climate science, this would enable AI systems to provide insights that could solve some of our most complex challenges. |
Addressing the Problems
Addressing these systemic problems requires coordinated action from both industry and higher education.
Companies should:
- Develop adaptable training approaches that keep pace with rapidly evolving AI technologies, ensuring all demographic groups can access and master these skills.
- Identify and address barriers workers face in accessing AI training, such as varying technical backgrounds.
Universities should:
- Create affordable, scalable AI programs that eliminate traditional cost and location barriers, as demonstrated by the University of Texas’s $10,000 online AI master’s program, which has enrolled nearly 1,500 students in its first year and plans to reach 4,000.
- Establish more computer science faculty positions to meet the demand for AI training.
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The blog raises an important point about demographic disparities in AI training. How do you think companies can overcome these inequities while still adapting to the rapid pace of AI development?