Is winter coming for Artificial Intelligence?

For some years now, artificial intelligence (AI), in particular generative AI, has been revolutionising our lives, and more importantly the work of companies, thanks to its potential to solve complex problems and think intuitively, while going beyond simple automation. 

This has led to a radical change in the way we think about work and has the potential to completely transform our society. Recently, however, companies have begun to take a more cautious approach to AI, focusing on practical applications rather than utopian promises.

Two new studies have shown that while interest in AI is still high, the honeymoon period seems to be over. Companies are rethinking their strategies, with a closer eye on costs and security, favouring a more realistic understanding of the potential and restrictions of this technology. 

In addition, gen-AI has not taken over as many jobs as previously feared, and it appears that the technology may be reduced to the role of “sophisticated assistant” to human employees. In February, the Wall Street Journal reported how some "early adopters" - companies that were the first to invest in generative artificial intelligence - are now struggling to find applications useful enough to justify its expense

This is even fuelling widespread suspicions among some analysts that AI is a remarkable and potentially useful tool, but that companies in the industry today are exaggerating its capabilities, largely to attract investments. 

This view is also supported by Gary Gensler, chairman of the Securities and Exchange Commission (SEC), the US federal agency responsible for overseeing stock exchanges, who has coined the term “AI washing” to describe the corporate strategy of mentioning artificial intelligence in company reports, often without any concrete basis. 

There are, of course, exceptions to this trend. Some sectors, such as technology and retail, have had a certain amount of success with AI, using it to streamline operations and to drive growth, rather than to replace human workers. 

Far from making humans obsolete, AI appears to be a potentially very powerful tool, which can help us work smarter and more efficiently. The future of AI therefore seems to be one of collaboration, where humans and machines work together to achieve more. 

People seem to be realising, however, that innovation does not simply come from designing a technology, as its value must also be recognised by those who use it. The journalist and essayist Luca De Biase, in his article “At the Frontiers of the Future”, published in the December 2023 edition of the Bollettino Generali, effectively summed up this point: “The research process starts with an attempt to understand whether the new technology will be adopted. And then to foresee the possible consequences. The theory is clear: technologies in a networked world are valuable only when widely adopted”, writes De Biase.

The article stresses the pressing need for improved ability to predict which technologies have the potential to produce innovation. “Satisfying the need for knowledge about the potential of new technologies requires a change of culture. It is not an easy task; once it is realised that a new technology may have an impact, we need to know whether it will be desirable or bring harmful side effects”.

Since predictions rarely come true in fluid circumstances, De Biase says we need to find a more realistic way of predicting which innovations will have multiple consequences, especially when it comes to understanding the desirability of their effects on people and communities.

One research tool has proven particularly effective in fully understanding the adoption of innovations and how people accept and adopt new ideas and technologies: the ‘Rogers Curve’.

This basic conceptual tool is named after American sociologist Everett Rogers and helps outline the process by which innovations spread through society, describing how people receive and adopt new products. Put simply, it is a graph that shows the extent to which different segments and classes of the public are willing to try or use a particular product over time.

Rogers theorised the existence of five groups of adopters, each with specific characteristics, who buy and/or use products in different ways and at different times:

• Innovators, who are not afraid of the high degree of complexity and uncertainty usually associated with new products, and who tend to be technology experts;
• Visionaries, who are not afraid of technology, but need more information, in order to understand how it works;
• Pragmatists, who adopt a product only after it has gained a good reputation;
• Conservatives, who do not like change unless it fits in with their world view;
• Sceptics, who are reluctant to change, and may be difficult to reach with marketing campaigns, as they often have minimal media exposure.

An innovation must ultimately be compatible with the values, experiences, and needs of the individual or organisation: the more an innovation fits into the existing environment, the greater the likelihood that it will be adopted. Understanding these factors is essential for designing effective innovation diffusion strategies, and tailoring marketing messages to the needs and perceptions of audiences.