  The use of Artificial Intelligence (AI) is becoming increasingly pervasive
and relevant in many different application areas. Researchers are putting a
considerable effort to take full advantage of the power of AI, while trying to
overcome the technical challenges that are intrinsically linked to almost any
domain area of application, such as the Internet of Things (IoT). One of the
biggest problems related to the use of AI in IoT is related to the difficulty
of coping with the wide variety of protocols and software technologies used, as
well as with the heterogeneity of the hardware resources consuming the AI. The
scattered IoT landscape accentuates the limitations on interoperability,
especially visible in the deployment of AI, affecting the seamless AI
life-cycle management as well. In this paper, it is discussed how to enable AI
distribution in IoT by introducing a layered intelligence architecture that
aims to face the undertaken challenges taking into account the special
requirements of nowadays IoT networks. It describes the main characteristics of
the new paradigm architecture, highlighting what are the implications of its
adoption from use cases perspective and their requirements. Finally, a set of
open technical and research challenges are enumerated to reach the full
potential of the intelligence distribution's vision.
