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Determining the human to AI workforce ratio – Exploring future organisational scenarios and the implications for anticipatory workforce planning

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  • Farrow, Elissa

Abstract

There are waves of organisational adaptation challenges facing decision makers due to current time societal, systemic and pandemic implications. It is difficult to plan strategically and then act decisively towards a future that is uncertain - the cause and effect offering many scenarios, some plausible and some outliers. In this research 110 participants from 36 different organisations were invited to explore the implications of different ratios of human and artificial intelligence (AI) in future organisational operating models. Five operating models were explored using the Futures Wheel (Glenn, 1972) [1]. The Futures Wheel is a methodology to causally link the future implications of a scenarios and change. Operating models explored varied from a fully human workforce with no AI to those which had a changed ratio of AI and human workers and leaders with the outlier being an AI lead (no human) model. Three participatory workshops generated 20 futures wheels, four for each of the five organisational scenarios. This article will present the results, personally prioritised by participants, to identify which implications they thought in an anticipatory 2040 organisational context would be best avoided (stop happening) or amplified (make happen). These findings then are analysed to produce macro themes that form part of a proposed anticipatory workforce design approach (5As) for organisations strategising on what the ideal Human to AI ratio (Human:AI) ratio is within an organisational context.

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  • Farrow, Elissa, 2022. "Determining the human to AI workforce ratio – Exploring future organisational scenarios and the implications for anticipatory workforce planning," Technology in Society, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:teinso:v:68:y:2022:i:c:s0160791x22000203
    DOI: 10.1016/j.techsoc.2022.101879
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    References listed on IDEAS

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    1. Stanton, Muriel C. Bonjean & Roelich, Katy, 2021. "Decision making under deep uncertainties: A review of the applicability of methods in practice," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
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    Cited by:

    1. Sayed Fayaz Ahmad & Heesup Han & Muhammad Mansoor Alam & Mohd. Khairul Rehmat & Muhammad Irshad & Marcelo Arraño-Muñoz & Antonio Ariza-Montes, 2023. "Impact of artificial intelligence on human loss in decision making, laziness and safety in education," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.

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