The Tech in Asia article explains that the challenges around hiring AI talent go far beyond simply having powerful algorithms or tools — they stem from fundamental gaps in skills, organisational readiness, and talent strategy. As research and development in AI race ahead, many companies struggle to find and retain qualified professionals who can actually implement, govern, and maintain these complex systems, slowing down meaningful adoption of the technology.
One major issue is that demand for AI expertise far outstrips supply, leading employers to compete for the same limited pool of engineers, researchers, and product specialists. This talent imbalance doesn’t just make hiring difficult — it drives up salaries, increases turnover, and creates pressure for companies to recruit internationally or outsource roles in order to stay competitive. Relatively few professionals have deep experience with cutting-edge AI models, deployment pipelines, or responsible use practices, making these positions both hard to fill and hard to structure within existing teams.
Another core challenge is the skills mismatch and training gap: even when organisations can attract candidates, they often find that academic knowledge doesn’t translate directly to business needs. Candidates may know theory but lack experience with real-world data, integration with legacy systems, or governance and ethics frameworks — and employers aren’t consistently investing in training or reskilling to bridge this gap. This means many firms are stuck wanting AI progress without having the pipeline to support it, turning hiring into a strategic workforce problem rather than just a technical one.
Finally, HR and recruiting teams are themselves grappling with new complexity introduced by AI tools in the hiring process — from managing AI-generated applications to redefining what makes a strong candidate in AI-enabled roles. In an environment where many tasks are being automated and job descriptions are rapidly evolving, organisations face uncertainty about what skills really matter, how to assess them fairly, and how to design careers that combine human oversight with machine assistance. Addressing these systemic workforce and organisational design issues is essential if companies want to harness AI successfully and sustainably.
