IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v68y2022ics0160791x22000203.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X22000203
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techsoc.2022.101879?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paredes-Vergara, Matías & Palma-Behnke, Rodrigo & Haas, Jannik, 2024. "Characterizing decision making under deep uncertainty for model-based energy transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Jackson, Canek & Pascual, Rodrigo & Mac Cawley, Alejandro & Godoy, Sergio, 2023. "Product–service system negotiation in aircraft lease contracts with option of disagreement," Journal of Air Transport Management, Elsevier, vol. 107(C).
    3. Kahagalage, Sanath Darshana & Turan, Hasan Hüseyin & Elsawah, Sondoss & Gary, Michael Shayne, 2024. "Exploratory modelling and analysis to support decision-making under deep uncertainty: A case study from defence resource planning and asset management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:68:y:2022:i:c:s0160791x22000203. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.