Generative AI: The implications
development options; AI-powered chatbots can coach individuals. In some cases, AI-powered tools support learning designers to develop new modules and build learning interventions. But the big buzz has been about Generative AI – tools like ChatGPT. The difference between earlier models
for L&D I
n L&D, we’ve seen some examples of AI utilisation – applied with varying degrees of success. AI can now track your learning activity and recommend future
of Machine Learning and AI is that Generative AI seems to talk to us. It can use unstructured data – text, images, video – spot patterns and predict associations. It will report back on what it has found in ways that resemble the output of a real person. This represents big news – according to McKinsey some $12bn was invested in the development of generative AI in the first five months of 2023 alone. So what does this mean to organisations and the people they employ? As L&D teams, how do we help prepare for and engage with this new technology landscape?
What won’t be big news – or shouldn’t be, in any case – is L&D teams using, for example, Chat GPT to create yet more content. I urge people to think it through. One inescapable point about generative AI is that it is pretty ubiquitously available. The tools keep getting better, the access to them is easier than ever and, through trial and error, many of our colleagues have started to create prompts and interact with these AI tools in ways that generate things of use. The L&D team’s intervention in the
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How do you prepare people for the use of AI?
middle of that seems unnecessary at best and a despairing search for continued relevance at worst. If your people can use Generative AI to find answers to the questions they need answering, L&D’s role is to enable critical faculties and support the acceleration of adoption. It is not to do it for them and cut and paste the outputs into PowerPoint, e-learning modules or videos on your LXP. With Generative AI we need more imagination. If we just use these powerful tools to do more of the same, more quickly we fail to harness their potential. What’s more, one of the ways of paying for the costs associated with AI – and these will increase as investors seek a return on the millions they have ploughed into its development – will be to reduce headcount. Finding more efficient ways to do what we have always done, inevitably leads to job losses. What may also be needed is to help people recognise that the very ubiquity of AI tools means that there is no differentiation or competitive advantage in using the same tools as everyone else. Yes, they will get slightly different answers by using different prompts, but where the data being used is the same everywhere, the results will be pretty similar. The plethora of companies who have sprung up with solutions – which turn out to be no more than a branded interface for ChatGPT 3.5 – take note Generative AI tools in their current
forms are not finished products. They are principally marketing tools, produced to showcase what can be achieved. They demonstrate new capabilities to get
people thinking about the future uses of Generative AI. The first thing for L&D teams to consider
therefore is: which jobs, or parts of jobs, could, should or might be automated? The second consideration is: ‘If these activities are automated, what will the people who used to do them, do instead?’ I don’t have a crystal ball. The degree to which AI – and specifically Generative AI – will impact work is as yet unknown. However, predicted capability of Generative AI may impact many millions of jobs, especially those involving working with knowledge.
Despite the uncertainty, I think there are four things we can do now: 1. I would start by helping people to understand what Generative AI is. While some will have a very good idea of what it is and what it can do, others will not have moved beyond the social media hype and the apocalyptic news reports. They need to see it in action with informed guides.
2. This awareness raising is the forerunner to a more informed discussion of what might come next. Those creative knowledge workers in our organisations – whose jobs may change the most – are probably as well placed as anyone to define the kinds of routine, repetitive
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