” THE CHALLENGE ISN’T ONE OF MASS UNEMPLOYMENT, BUT ONE OF MASS REDEPLOYMENT. IT’S A FUNDAMENTAL CHANGE IN THE KIND OF SKILLS AND CAPABILITIES PEOPLE WILL HAVE TO BRING TO BEAR IN THEIR WORK.”


DR DANIEL SUSSKIND


vectors of compliance, retention and inclusion. In this space, KPMG’s ‘2023


Global Assignment Policies and Practices Survey’ urges organisations to be aware of the impact of rapid digitisation and automation. This is particularly on operating and service delivery models, workforce composition, risks to talent across mobility life cycles and workplace practices. Yet when it comes to next-generation technology, 48% of organisations and 60% of global talent mobility functions do not have a strategic vision for automation and robotics. Where they do, 71% of respondents report automation is not yet being used to streamline the global mobility process. Across sectors, a lack of budget,


bandwidth and skills are the biggest roadblocks to implementing greater technological automation. These findings suggest huge scope for the people profession to step up, lead and align with internal and external stakeholders to cope with the rapidly changing world of work and geopolitical environment. “We are all thinking more and


more strategically about the shape of work in the future,” said Peter Cheese. “How do we build the skills that we need for the future and


20


make sure that our learning and development capabilities sit at the very heart of organisational agility? If we can’t be agile, we can’t upskill and reskill people rapidly in a world where job demands are changing as rapidly as they are. We’re seeing ourselves increasingly at the heart of the business agenda and we must be at the heart of the business agenda.” Oxford University professor,


economist and author Dr Daniel Susskind picked up the question of reskilling in his keynote address at the CIPD ACE 23. Recalling how technology had initially been regarded as impacting routine processes and blue-collar jobs most, he said the next generation of technology marked a new paradigm that will significantly impact white-collar jobs too. This is already happening, with tools like ChatGPT able to code, diagnose medical conditions and draft tight legal arguments. However, generative AI is moving away from extrapolating how humans think towards “making sense of that data in ways that we acting alone simply could not perceive”. It is increasingly performing analysis beyond the scope of what humans can. “These increasingly capable non-thinking machines are what I think the second wave of artificial


intelligence is all about,” said Dr Susskind. “Systems and machines are using remarkable advances in processing power, in data storage capability and in algorithm design to perform tasks through these technological changes in fundamentally different ways.”


RESPONDING WITH EDUCATION & TRAINING In this analysis, it is easy to see why earlier this year those working at the frontiers of AI called for a temporary halt on further development. The analysis also highlights the huge impact on jobs and the human skills needed to safely, transparently and effectively incorporate


these tools into our


daily life and work. For Dr Susskind, the best


response to these technological disruptions is education


and


training. “What we’re going to see over the next ten to 20 years is an increase in demand for human beings to do work that is very hard to automate. The challenge isn’t one of mass unemployment, but one of mass redeployment. It’s a fundamental change in the kind of skills and capabilities people will have to bring to bear in their work.” Mindful of the present chasmic skills gaps to deliver technological


Dr Daniel Susskind, Oxford University professor, economist & author, speaking at the CIPD Annual Conference & Exhibition


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94