The “Gartner Hype Cycle” describes the maturity, adoption and social application of technological products and services: they start as “innovation triggers”, with early proof-of-concept examples,  and sometimes end onto a “plateau of productivity”, when their mainstream adoption starts to take off.  In the middle of that path, they ascend to the “peak of inflated expectations”, fall into the “trough of disillusionment”, and walk step by painful step through the “slope of enlightenment”. Not all of them survive the journey.

The impact of Artificial Intelligence technologies on our societies, including its effects on labour markets, is very much in the spotlight of political discussions at the highest levels, in  academia, non-specialist media and is probably a topic of conversation  also for people outside of the policy bubble(s). This is fully justified by the potential effects  – positive and negative –  that the widespread use of advanced AI systems might have not only for EU citizens, but for humankind as a whole. This makes it even more important to be self-critical about the assumptions we make about these technologies.

Is AI (or at least the narrative about it?) following the Gartner Hype Cycle? Are we on the point of falling down from the Peak of Inflated Expectations? If so, how can we survive the fall and emerge from the “trough of disillusionment” with as little harm as possible, and continue our journey towards the “plateau of productivity” as quickly as possible? Most importantly, do we need to reshape the conversation about AI and its effects on society, to make sure we all – researchers, policy-makers, business people, civil society – speak a reasonably common language?

Based on his experience as a EU civil servant for the past decade, and as a consultant for tech businesses for the previous one, Mr Glorioso will reflect on how we can create spaces for real mutual understanding on what are the impacts of AI on society and the future of work, and how to maximize the benefits of AI while implementing sensible risk mitigation strategies.