The fast development of robo-advice has responded to a growing demand for automation and enhanced capabilities to industrialize investment advisory (IA) solutions in the FinTech landscape. Until recently, the first generation of robo-advisors have naturally focused on the low-end segment of the IA market, mostly thanks to a rather low sophistication of the portfolio allocation systems based on simplistic versions of Modern Portfolio Theory, leaving wealth managers with no serious competition from fully digitized solutions. Nowadays, the second generation of robo-advisors is more ambitious, both from a scientific and an ergonomic point of view. Even though we are not yet witnessing the age of industrialized big data or machine learning fully automated investment advisors, the maturity level of today’s robo-advisors is sufficient to accommodate behavioral sources of complexity like mental accounting or loss aversion at the investor’s level. The pressure on margins induced by regulation and digitalization gradually increases the competitive advantage of robotized IA in the mass affluent and private banking segments, making them a serious threat to those incumbent firms that cannot adapt with proper tooling or niche offering. In the near future, the mature generation of robo-advisors, with full deep learning and data treatment capacities, will presumably coexist with those firms that have been actively preparing today, that will use performant tools besides human expertise, but in a world in which fees will presumably have largely decreased and service quality will have been improved, at the benefit of the customer.