Back to the future(s)
Talking about the future and innovation, one can never relax, at least without the risk of missing out on the latest trend, the coolest update, or the ultimate revolution. This has been especially evident in the last few years: the technology industry never rests and keeps proposing newer and newer novelties, generating unprecedented needs and unexpected solutions. It must be kept in mind that “innovation does not emerge when a technology is designed, but when those who adopt it recognize its value”, as journalist Luca De Biase explains in its article “At the Frontiers of the Future” for our Bollettino.?
The author details his thesis in his book Apology of the Future, reminding us that there are many possible futures and, above all, “there is a choice. There are many lights at the end of the tunnel and many tunnels come to one light. If only one future is seen, it is propaganda. But alternative futures must be imagined. And discussed. And evaluated: possible, plausible, probable, and preferable.”?
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How to change the course of events?
Such matters are at the center of a new field of study known as futurology, whose goal is a systematic exploration of possible, probable, and preferable futures as well as the worldviews underlying them. In those studies, uncertainty remains crucial, as “there is no way to study the future empirically, that is, on the basis of future facts, because those facts do not yet exist”. This means that while we investigate the consequences of current events and technologies, we also need to learn to navigate the suspended dimension of unreality that is intrinsic in future itself.?
In this scenario, the ability to predict which technologies have the potential to generate innovation becomes crucial. Such ability is needed by those who produce new technologies and hope they will be adopted; it would help those who introduce new technologies and create the conditions for their adoption; and it is indispensable for sceptics of these technologies who conceive others solutions designed to be even more innovative. Furthermore, once it becomes clear that a new technology may have an impact, we need to know whether it will be desirable or bring harmful side effects.?
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On predicting the future?
To achieve this goal, models like the Innovation Adoption Life Cycle, also known as the Roger’s curve, theorize the process through which novelties are adopted by individuals or groups within a society. It identifies a predictable pattern in innovation adoption, characterized by distinct stages, the involvement of different types of adopters, and the influence of various factors like perceived benefits, ease of use, social influence, economic considerations, facilitating conditions, and the active engagement of open-source communities. In this way, it provides a clear and solid base to understand whether and how technologies will spread over long terms, generating the opportunity to set up the best conditions for their adoption.?
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Source: Reuter, 2023.?
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Anyway, we must be aware of the risks underlying our times. Entire ideologies can be built to convince people to adopt something, and facts can be manipulated. The most powerful emotional impulses can be activated to attract users and then make an impact by retaining them. Luckily, “technology ideologues” can easily be spotted: they care little about side effects and assume that unexpected consequences can be dealt with after results are achieved. We have the best example of this phenomenon in social media: when they got launched everything was for the good with no thought given to side effects such us misinformation, hate speech, teenage depression, and generational clash.?
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On preparing the future?
The Technology-Organization-Environment (TOE) framework is another model developed to explain how new technologies are adopted and used. It focuses on the interplay between the characteristics of the technology itself, the internal context in which the technology is used (the organization), and external context in which the organization operates (the environment), including factors such as market conditions, regulatory requirements, as well as social and cultural norms.??
An example of the model’s application is the research carried out by the Stikom Bali Institute of Technology and Business, which analyzes the contribution of technologies to increasing the competitiveness of Indonesian expo-oriented MSMEs (micro, small, and medium sized-enterprises). The results confirm the close relationship between the environmental, organizational, and technology dimensions and show the importance of creating positive preconditions to facilitate technologies adoption, especially in the case of structural economic change.?
This is also the goal we pursue in the framework of our multi-year partnership with UNDP, aiming to support the financial resilience of vulnerable communities, SMEs and global value chains to climate and other risks by incentivising innovation in insurance, financing, and devleopment.?
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What’s next for AI??
Artificial intelligence has been revolutionising our lives and the work of companies, thanks to its potential to solve complex problems and think intuitively, while going beyond simple automation. At Generali, we have been integrating Artificial Intelligence into our services to deliver tangible business value for our organization, to reduce costs, to shorten time-to-market, and to radically improve customer experience. Furthermore, in 2020 the Generali Innovation Fund was launched as part of the Innovation & Digital Transformation pillar of our strategic plan to support innovative initiatives across the Group, and empowering us to create new products and solutions. Since its launch and 16 funding rounds, this initiative has financed more than 200 ideas and created over 60 new products and services, including some AI / GenAI based solutions.?
In the case of AI, we may say that innovation is happening: as we said, we have radically changed the way we think about work and we have become aware of the potential of AI to completely transform our society. Nevertheless, users and companies have begun to be more cautious about Artificial and Generative Intelligence, focusing on practical applications rather than utopian promises. People seem to be realising that innovation does not simply come from designing a technology, as its value must also be recognised by those who use it.?