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@article{207859,
        author = {Emmanuel Abegunde and Dr. Beejaye Panray Ramchurn and Ms.  Diviyha and Dr. Precious Mhaka and Dr. Zairil Hakim, ST. MT},
        title = {ARTIFICIAL INTELLIGENCE AND FUTURE WORKFORCE PLANNING: TRENDS, CHALLENGES, AND OPPORTUNITIES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {3102-3111},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207859},
        abstract = {Artificial intelligence (AI) is shifting the unit of workforce plan from the occupation to the task, in particular generative AI technologies and agentic systems are driving this transformation. This paper will explore if and how organisations can foresee shifts in demand for labor, skills, job design, productivity, worker experience and governance, and if and how they can mitigate the common pitfall of assuming technical exposure equals job loss. It builds on the workplace argument put forth by Wilson (2026) using a formal, structured mixed-evidence review of peer-reviewed studies and key institutional data sources published between 2019 and August 2026. quantitative data from the World Economic Forum (WEF), International Labour Organization (ILO), International Monetary Fund (IMF), OECD, Stanford AI Index, Microsoft/LinkedIn and PwC are contrasted with qualitative data on employee experience, algorithmic management, technostress, bias, employee privacy and human-AI collaboration. The synthesis concludes that there is a significant level of AI driven disruption but it is not even. By 2030, WEF estimates that 170 million jobs will be added, 92 million displaced and a net gain of 78 million jobs, and 39% of workers' core skills will transform. The ILO projects that about one quarter of jobs today are exposed to generative-AI, but the level of transformation is more likely than complete or rapid elimination, with only 3.3% of all jobs categorized as high exposure globally. Meanwhile, there is an acceleration in adoption, which has been moving rapidly, while skill requirements shift quickly and wage premiums reward AI skills. The key takeaway is that outcomes are not a given, they are a choice that is organizational, rather than technical. AI models are more likely to translate into productivity and job-quality improvements when combined with task-level scenario planning, inclusive reskilling, internal mobility, employee voice and risk-based governance. We propose a six-stage Responsible Human–AI Workforce Planning Cycle to be implemented/evaluated.},
        keywords = {Artificial Intelligence; Generative AI, Workforce Planning, Future of Work, Job Redesign, Skills Forecasting, Reskilling; Human–AI Collaboration; Responsible AI, Algorithmic Management},
        month = {August},
        }