The findings of this year’s Voices of the Leaders of Tomorrow Report paint a rather sobering picture of the younger generation’s hopes for technology’s impact on future freedoms: their most desired tech futures are not necessarily the ones they consider the most probable to materialise.

This year’s Voices of the Leaders of Tomorrow Report – a collaboration of the Nuremberg Institute for Market Decisions and the St. Gallen Symposium – surveyed young top talent on their views of how technological change will impact future freedom (n=898). A first look at the assessment of the future impact of technologies on global freedom in general reveals the following key findings: Most of the Leaders of Tomorrow have neither a naïvely optimistic nor a fatalistic, negative view. In a nutshell: The overall attitude is cautiously optimistic. But this also means that nearly all of them worry about some threats as well. More than a third even fear more threats than opportunities.

This is surprising at first because the Leaders of Tomorrow are a generation that can be considered Digital Natives. The time of unreserved techno-optimism seems to be over: The younger generation apparently is keeping an eye open for threats looming behind new technological developments.

When presented with future scenarios about algorithms, AI or new business models, it is interesting to compare what the Leaders of Tomorrow predict will occur with what they hope or wish for. The highest probability of occurrence is attributed to the scenario that artificial intelligence will play a key role in many companies as a tool for far-reaching strategic decisions. More than 50% of the respondents consider it to be very likely or certain. A similar share regards this idea to be “highly desirable” or at least “desirable”. The lowest probability is assigned to the scenario of absolute management control by the AI. Less than 20% consider this likely, and the desirability is even lower. Both figures are lowest in the respective rankings.

These two scenarios match in terms of probability and (un-)desirability: The Leaders of Tomorrow wish for a strong AI support and expect it to come true, but they do not want to lose complete control to AI and do not consider this scenario probable either.

In contrast, their predictions and their dreams diverge for the other scenarios. That algorithms will know people’s preferences better than the individuals themselves is considered relatively probable but is desired only by very few. Vice versa, three scenarios that address ethical aspects are much more desired than expected. Among these, the scenario that algorithms help people stay true to their ethical principles ranks relatively low in terms of desirability. Probably this is again a matter of distrust against too much control by technology: Some may find the idea uncomfortable that AI takes over moral decisions from humans and see it as a transgression of boundaries that should better remain in place.

The mismatch between predictions and dreams can be seen even more clearly when visualized in a matrix. The upper left quadrant is filled with unfulfilled dreams of the Leaders of Tomorrow: Concepts and ideals that they consider very desirable but cannot imagine being realized in near future. These seem to be ideal connecting points, so to speak, for promising future opportunities for courageous and visionary entrepreneurs.

All these desired scenarios have a common basis: A strong vote in favor of greater morality in future business procedures and models. Apparently, transparency, respect for data privacy and moral principles are important for the Leaders of Tomorrow.

To put it in a nutshell: Morality in terms of honest consideration for others has the potential to be a guiding principle for future generations of business leaders when it comes to developing and designing technologies with inherent trade-offs between convenience and efficiency on the one hand and human freedom of choice on the other.

Read the full Voices of the Leaders of Tomorrow Report here for all findings and detailed analysis.