Shaping the future of AI-enabled teamwork

Learning about and experimenting with AI are distinct processes that require ongoing attention. Within the Reach Network, we continuously explore collaborating with AI in teams and share valuable lessons on this vital topic. We focus on collective experiences with AI, highlighting the importance of open communication about its impact and the need for collaborative frameworks in its development.

Experiments are the backbone of how we learn. In the April session of Reach School we discussed a self-built AI tool by Quicksand, called Babel, that helps us to delve into past project knowledge in our studios and find the gems that are useful in current projects. The benefits of hands-on experimentation became very clear because it helped demystify AI, making it feel less like a "black box." We could see and discuss what happened behind the scenes of a prompt you put into Babel.

We noted a lack of foundational frameworks for using AI in teams, which sparked discussions about building knowledge and leveraging community efforts to fill this gap. Personal experiences of feeling overwhelmed by AI advancements opened up important dialogues about concerns of being left behind, while also showcasing AI's potential to enhance teamwork and practices.

It’s important to create a common understanding within the Reach Network that prevents individuals from working in isolation. Informal, collaborative sessions like the ones of Reach School but also public events of the Teaming with AI community are crucial for fostering shared knowledge and strengthening teamwork within the AI community.

Image: Tower of Babel By Pieter Bruegel the Elder, 16th century. Taken from wiki commons : http://commons.wikimedia.org/wiki/Category:Tower_of_Babel