In simple terms, AI is a set of tools, techniques and software that enable computer programmes to perform tasks formerly entrusted to humans. The technologies\u2013machine learning, supervised learning and neural networks, for example\u2013may differ, but these methods share a common foundation that is rooted in the use of historical data to feed their probabilistic or statistical prediction models. <\/p>\n\n\n\n
According to Louis-David Benyayer, what has shifted over time is that :<\/p>\n\n\n\n
\n\u201cArtificial intelligence is no longer based on explicit rules, but on data from which rules are extracted.\u201d <\/p>\n<\/blockquote>\n\n\n\n
Hence, the idea of inanimate machines learning.<\/p>\n\n\n\n
Like AI, generative AI also employs statistical models to \u201cgenerate\u201d or create new content, be it text, images, sounds or videos. \u201cSo today\u2019s AI\u201d, says Louis-David Benyayer, \u201cis both new – in terms of the form taken by the content it produces – and familiar if we look at the tools, methods and techniques used. This explains why some experts, when confronted with the ChatGPT phenomenon, have said that it in no way constitutes scientific progress<\/em>.\u201d That being said, the democratisation and sheer computing power available today have propelled the transformation of an already existing technology in an impressive way.<\/p>\n\n\n\nWhen it comes to generative AI, the future is speculative, although changes are already happening<\/h2>\n\n\n\n
Recent technologies such as NFTs, blockchain and the metaverse were expected to have major impacts on the economy and so far, these predictions have not panned out. Similarly, Generative AI, although record-breaking in its early user numbers, could take off or it could fizzle out like previous so-called technological revolutions. <\/p>\n\n\n\n
But, while it\u2019s too early to predict the staying power of the generative AI, Louis-David Benyayer points out that there are already several existing challenges and changes, which include geopolitical issues in the form of infrastructure independence, ecological issues in the form of the environmental impact of the computing centres, and competition issues in the form of an introduction of new models.<\/p>\n\n\n\n
Are we seeing the programmed obsolescence of humans or a new era of worker productivity?<\/h2>\n\n\n\n
Current examples of predictive AI in the workplace already exist for things like automation and the streamlining of processes, such as work shift scheduling at McDonald\u2019s, writing marketing emails, and notifications in the industrial and retail sectors. According to Louis-David Benyayer, this new iteration of AI has companies expecting gains in productivity. <\/p>\n\n\n\n
\u201cBy automating certain tasks and reducing the risk of error, costs are also reduced. Automation and standardisation can also sometimes lead to an improvement in quality, which has an impact not only on costs but also on revenue,<\/em>\u201d he says.<\/p>\n\n\n\nRather than replacing jobs, AI automation will target certain tasks while other tasks, sometimes new ones, born out of the further integration of these tools, will require human skills. \u201cHowever\u201d, points out Louis-David Benyayer, \u201cexperience also shows that this shifts the workload: when we use artificial intelligence to automate everything that is easy to process, human operators are left with all the complicated cases.<\/em>\u201d The result will be a need for higher-qualified staff. In terms of the kinds of companies that will be affected by the widespread deployment of AI tools in their workflows, it is more a question of when rather than which ones. SMEs with limited resources might be impacted on a later timeline.<\/p>\n\n\n\nRe-discovering the value of human beings in the face of generative AI<\/h2>\n\n\n\n
Generative artificial intelligence is currently experiencing significant interest and excitement, accompanied by ambitious promises of future revolutions. However, history has taught us that the adoption and impact of new technologies often take longer than expected. Nonetheless, several indicators suggest that this technology will have substantial long-term repercussions on the world of work, as it has already been tried and tested with significant investment from major players in the digital and traditional sectors. <\/p>\n\n\n\n
One of the key changes to anticipate is the transformation of professions and processes, rather than a complete replacement of jobs. This raises questions about the uniqueness of human beings and their value in the face of these advancements.<\/p>\n\n\n\n
Louis-David Benyayer<\/h4>
Louis-David Benyayer<\/strong> is an Affiliate Professor at ESCP Business School\u2019s Paris Campus, where he is joint Scientific Director of the MSc (Master of Science) in Big Data and Business Analytics. He is also involved in the Jean-Baptiste Say Institute for Entrepreneurship and manages the Executive MSc in Big Data and Business Analytics in Dubai.<\/p>Over the last 20 years, he has developed a strategy consultancy business working with large companies and SMEs. He has been involved in two start-ups and led a recovery plan for a bankrupt company. <\/p>
Louis-David Benyayer<\/strong> co-founded Without Model, a think tank dedicated to promoting open, collaborative and responsible business models, and co-ordinated the book Open Models, on business models of the open economy. With Simon Chignard, he co-authored the book Datanomics, les nouveaux business models des donn\u00e9es<\/em> (Datanomics, New Data Business Models). <\/p>His areas of expertise include business models, entrepreneurship and strategic innovation. He is particularly interested in the changes in business models and competitive dynamics brought about by the widespread use of digital technology. A graduate of ESCP (Master in Management), he holds a PhD in Management Science from Paris Dauphine University.<\/p><\/div>\n\n\n\n
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