Futures and foresight learning in HRD
Authors: Gold Jeff , Jolliffe Patricia , Stewart Jim , Glaister Catherine , Halliday Sallyann 
Published: October 2022

Purpose

The purpose of this paper is to argue that human resource development (HRD) needs to embrace and include futures and foresight learning (FFL) as a new addition to its field of theorising and practice. The question to consider is: How can FFL become a new feature of HRD? A key part of the authors’ argument is that the inclusion of FFL will enable HRD to add to the success of any organisation and make a vital contribution to the management of people at work.

Design/methodology/approach

This paper firstly considers some of the debates surrounding the meaning of HRD. The authors suggest that instability of the time serves to disturb any comforts that have been created in HRD and that there is a need to consider how there might be different futures for what we still call HRD in research, practice and praxis. This paper then considers how FFL might become one possibility for expanding the existing boundaries of HRD. The authors characterise futures and foresight as a learning process, which provides new but complementary features to what is already considered as HRD. This paper will show how FFL can lead to organisation's success and the way this can be achieved.

Findings

There is a wide variety of meanings of the term HRD; however, HRD is still cast as a “weakened profession” which has to play a subservient role to others in the workplace. Over the last 15 years, the expansion of the meaning of HRD has been seen as evidence of its evolving and emerging nature and development based on a co-creation with other disciplines. This creates a space for FFL, defined as an ongoing learning process to find predictable, probable, possible and/or a variety of long-term futures. FFL embraces three key processes of scanning, futuring and reconfiguring, all of which contain a high potential for participants and others to learn as they proceed, providing outcomes at each stage. FFL has been shown to enhance organisation performance and success and HRD interventions can play a key part in implementation. This represents a significant opportunity for the HRD profession to move from weakness towards strength.

Research limitations/implications

For HRD researchers, while FFL is not yet on its radar, the authors would argue that the uncertainties of the future require that more attention be given to what might lie ahead. Indeed, HRD researchers need to ask the question: What is the future of HRD research? In addition, if the authors’ call for FFL to be included in the practice of HRD, such practice will itself provide new pathways for HRD research. Further research questions might include: To what extent is FFL practiced in organisations and what role do HRD practitioners play in delivery? How does FFL impact on organisation behaviour and outcomes? What new products and services emerge from FFL? What new skills are required to deliver FFL? Can FFL enhance the status of HRD practitioners in the work place and its role in decision-making? and How can the HRD profession develop as a hybrid profession with respect to machine learning (ML)/artificial intelligence (AI)?

Practical implications

FFL produces outcomes that have importance for strategy, HRD practitioner can learn to facilitate FFL by action learning and in leadership development programmes. FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. FFL offers those involved in HRD a significant opportunity to transfer ideas into practice that have an impact on organisation sustainability. HRD can play a significant role in the design and delivery of ML and AI projects.

Originality/value

This paper concludes with a call for embracing FFL as a challenging but important addition to how we talk about learning at work. The authors argue that FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. At its centre, FFL involves learning by people, groups, organisations and machines and this has to be of concern to HRD.