In the last 10 years a major strand of my research within the Essex Brain-Computer Interfaces and Neural Engineering (BCI-NE) laboratory has focused on the idea of combining brain signals (and other physiological and behavioral information) across multiple people to achieve a form of emergent, perceptual and, more generally cognitive, group augmentation, which is more than the sum of the parts. Over this period (in projects funded mostly by the UK Ministry of Defense and also US DOD) we have developed a technology which has delivered significant (and in fact in some cases remarkable) improvements over the group performance achieved by more traditional methods of integrating information applying it successfully to progressively more and more real-world applications. Our efforts have particularly focused on the area of decision making. Decisions (for example made by government, military or hospital management) are often made with limited amounts of information, or indeed too much information for any single person to take in, hence involving a high degree of uncertainty. Yet, such decisions can be highly critical in nature, with mistakes possibly resulting in extremely adverse outcomes, including loss of lives. So, any improvements in accuracy or speed of decisions in such conditions is vitally important. In the last 5 years we have also started to study hybrid human-AI decision-making groups by the inclusion of one or more AI-based teammates which act as peers to the humans. We found that when the conditions are right (more on this in the talk), human-AI groups produce super-human and super-AI performance. In this presentation, I will review BCIs and our approach, and will discuss some of the applications we explored including the identification of visual targets in cluttered environments, the comprehension of military radio communication, face recognition, military recognisance missions, military outposts and strategic resource allocation in a pandemic. I will finally look at potential future developments.