According to World Economic Forum reports, nearly 92 million jobs will be displaced by AI by 2030, but 170 million new jobs will also be created; meanwhile, AI skills can raise pay by up to 118 percent.
According to the World Economic Forum's Future of Jobs report, about 92 million existing jobs will be eliminated by AI and automation by 2030, but 170 million new jobs will be created in the same period — a net gain of nearly 78 million jobs. The main challenge, then, is not simply "job loss" but "workforce displacement and retraining".
The reports predict that about 39 percent of the workforce's core skills will change by 2030. At the top of the fastest-growing skills are AI and big data, followed by networks and cybersecurity, and technological literacy — a combination that is no coincidence and matches organizations' need for secure, data-driven infrastructure.
Financially, having AI skills on a resume can raise pay by up to 62 percent, and in some specific industries as much as 118 percent has been reported; more interestingly, older candidates or those without advanced degrees see a noticeable boost in hiring chances when AI skills appear on their resume.
But the reports also show an important gap: although AI and information processing are expected to affect 86 percent of businesses by 2030, according to Deloitte's 2026 Human Capital Trends report only 6 percent of organizational leaders say they are systematically designing for human-AI collaboration.
For IT managers and organizational leaders the practical message is clear: a retraining program for working alongside AI should be designed proactively starting now — not as an accidental side effect after new tools are deployed.
This note is a summary and rewrite based on the report below; see the original source for the full report.
World Economic ForumThe 2026 report by the Cambridge Centre for Alternative Finance (CCAF), with BIS, IMF, WEF and others, shows 81% of financial firms use AI and software engineering is the most mature use — yet concern over lost human oversight and weak explainability is serious.
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Research by Medrano, Verma and Chhabra shows that in a production-like RAG system, RAG Fusion raises recall, but after re-ranking and context truncation much of the gain disappears and precision even dips slightly.
